From 6d1c53e9122f1ea1e695fc4bee32f4be3942d36a Mon Sep 17 00:00:00 2001 From: gaow Date: Wed, 16 Feb 2022 11:17:31 -0500 Subject: [PATCH 01/63] Create README.md --- TWAS/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 TWAS/README.md diff --git a/TWAS/README.md b/TWAS/README.md new file mode 100644 index 0000000..d9975ba --- /dev/null +++ b/TWAS/README.md @@ -0,0 +1 @@ +# [obsolete] this workflow will soon be replaced by TWAS workflows in molecular phenotype analysis repo From ce719190c4c39bbb6684b258fec848dd2ea9b718 Mon Sep 17 00:00:00 2001 From: gaow Date: Wed, 16 Feb 2022 11:17:53 -0500 Subject: [PATCH 02/63] Create README.md --- multivariate-prediction/README.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 multivariate-prediction/README.md diff --git a/multivariate-prediction/README.md b/multivariate-prediction/README.md new file mode 100644 index 0000000..d9975ba --- /dev/null +++ b/multivariate-prediction/README.md @@ -0,0 +1 @@ +# [obsolete] this workflow will soon be replaced by TWAS workflows in molecular phenotype analysis repo From bb2c1f79f28280a003d8c91704d7249bfa559dd8 Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Tue, 22 Feb 2022 16:38:47 -0500 Subject: [PATCH 03/63] add rename parameter --- GWAS/liftover.ipynb | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 9cd83cd..438e43f 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -62,7 +62,8 @@ "- `--ouput_file`, the name of ouput file which will be saved under `cwd` path\n", "- `--fr`, From reference genome, defaut is `hg19`\n", "- `--to`,To reference genome, defaut is `hg38`\n", - "- `--remove-missing`, boolen, Remove SNPs failed to liftover (defaults to False)" + "- `--remove-missing`, boolen, Remove SNPs failed to liftover (default to False)\n", + "- `--no-rename`, boolen, Rename variants' ID (default to False). **Only implemented to sumstat liftover**" ] }, { @@ -149,6 +150,8 @@ "parameter: to = 'hg38'\n", "# Remove SNPs failed to liftover (defaults to False)\n", "parameter: remove_missing = False\n", + "# Rename Variant ID\n", + "parameter: no_rename = False\n", "# Container\n", "#parameter: container = str" ] @@ -172,7 +175,7 @@ " from LDtools.genodata import *\n", " from LDtools.sumstat import Sumstat\n", " from LDtools.liftover import Liftover\n", - " def liftover(input_path,output_path,fr='hg19',to='hg38',remove_missing=True):\n", + " def liftover(input_path,output_path,fr='hg19',to='hg38',remove_missing=True,rename=True):\n", " lf = Liftover(fr,to)\n", " print(\"liftover from \" + fr +\" to \" +to)\n", " print(\"Removing SNPs failed to liftover is\", remove_missing)\n", @@ -203,8 +206,8 @@ " lf.vcf_liftover(input_path,output_path,remove_missing)\n", " else:\n", " print(\"This file is considered as sumstat format file\")\n", - " sums = Sumstat(input_path)\n", - " new_sums = lf.sumstat_liftover(sums.ss)\n", + " sums = Sumstat(input_path,rename=rename)\n", + " new_sums = lf.sumstat_liftover(sums.ss,rename)\n", " idx = new_sums.CHR == 0\n", " if remove_missing:\n", " new_sums[~idx].to_csv(output_path, compression='gzip', sep = \"\\t\", header = True, index = False)\n", @@ -239,8 +242,9 @@ " fr = f'${fr}'\n", " to = f'${to}'\n", " remove_missing=${remove_missing}\n", + " rename = ${no_rename}==False\n", " print(fr,to,remove_missing)\n", - " liftover(input_path,output_path,fr,to,remove_missing)" + " liftover(input_path,output_path,fr,to,remove_missing,rename)" ] } ], From bd491ebef67e11a70da93cfd784c1b0d5654fcc7 Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Wed, 23 Feb 2022 09:42:43 -0500 Subject: [PATCH 04/63] update --- GWAS/liftover.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 438e43f..7209a1e 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -63,7 +63,7 @@ "- `--fr`, From reference genome, defaut is `hg19`\n", "- `--to`,To reference genome, defaut is `hg38`\n", "- `--remove-missing`, boolen, Remove SNPs failed to liftover (default to False)\n", - "- `--no-rename`, boolen, Rename variants' ID (default to False). **Only implemented to sumstat liftover**" + "- `--rename`, boolen, Rename variants' ID (default to True). **Only implemented to sumstat liftover**" ] }, { @@ -151,9 +151,9 @@ "# Remove SNPs failed to liftover (defaults to False)\n", "parameter: remove_missing = False\n", "# Rename Variant ID\n", - "parameter: no_rename = False\n", + "parameter: rename = True\n", "# Container\n", - "#parameter: container = str" + "parameter: container = str" ] }, { @@ -242,7 +242,7 @@ " fr = f'${fr}'\n", " to = f'${to}'\n", " remove_missing=${remove_missing}\n", - " rename = ${no_rename}==False\n", + " rename = ${rename}\n", " print(fr,to,remove_missing)\n", " liftover(input_path,output_path,fr,to,remove_missing,rename)" ] From b78f09ae33c659b5d6bbb8d209964ad45b1c1b37 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Thu, 24 Feb 2022 10:01:15 -0500 Subject: [PATCH 05/63] mtag formating --- GWAS/data/mtag_template.yml | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 GWAS/data/mtag_template.yml diff --git a/GWAS/data/mtag_template.yml b/GWAS/data/mtag_template.yml new file mode 100644 index 0000000..83837ce --- /dev/null +++ b/GWAS/data/mtag_template.yml @@ -0,0 +1,11 @@ +# mtag summary statistics template +snpid: ID +chr: CHROM +bpos: GENPOS +a1: ALLELE1 #A1 needs to be the effect allele +a2: ALLELE0 # The other allele +freq: A1FREQ +beta: BETA +se: SE +pval: LOG10P +n: N From c9e97a1578170c5fe4fabca2d41384152b3efd9e Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Thu, 24 Feb 2022 15:46:52 -0500 Subject: [PATCH 06/63] Create ReadME.md --- LDSC/Deep_Learning/ReadME.md | 1 + 1 file changed, 1 insertion(+) create mode 100644 LDSC/Deep_Learning/ReadME.md diff --git a/LDSC/Deep_Learning/ReadME.md b/LDSC/Deep_Learning/ReadME.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/LDSC/Deep_Learning/ReadME.md @@ -0,0 +1 @@ + From 5d426164be528fba50cbd91eeba9683fd64d3efa Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Thu, 24 Feb 2022 15:48:01 -0500 Subject: [PATCH 07/63] Started Work on Notebook with Deep Learning Included --- LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb | 403 +++++++++++++++++++++ 1 file changed, 403 insertions(+) create mode 100644 LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb b/LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb new file mode 100644 index 0000000..d7e0c42 --- /dev/null +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## SoS Workflow:\n", + "\n", + "This is the options and the SoS code to run the LDSC pipeline using your own data. \n", + "\n", + "## Command Interface:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "kernel": "SoS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: sos run LDSC_DeepSea_Code.ipynb\n", + " [workflow_name | -t targets] [options] [workflow_options]\n", + " workflow_name: Single or combined workflows defined in this script\n", + " targets: One or more targets to generate\n", + " options: Single-hyphen sos parameters (see \"sos run -h\" for details)\n", + " workflow_options: Double-hyphen workflow-specific parameters\n", + "\n", + "Workflows:\n", + " train_model\n", + " make_annot\n", + " format_annot\n", + " munge_sumstats_no_sign\n", + " munge_sumstats_sign\n", + " calc_ld_score\n", + " calc_enrichment\n", + "\n", + "Sections\n", + " train_model:\n", + " make_annot:\n", + " format_annot:\n", + " Workflow Options:\n", + " --full-annot VAL (as str, required)\n", + " path to full annotation file\n", + " --output VAL (as str, required)\n", + " path to output file directory\n", + " munge_sumstats_no_sign: This option is for when the summary statistic file\n", + " does not contain a signed summary statistic (Z or Beta).\n", + " In this case,the program will calculate Z for you based\n", + " on A1 being the risk allele\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output VAL (as str, required)\n", + " path to output file\n", + " munge_sumstats_sign: This option is for when the summary statistic file does\n", + " contain a signed summary statistic (Z or Beta)\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output VAL (as str, required)\n", + " path to output file\n", + " calc_ld_score: Calculate LD Scores **Make sure to delete SNP,CHR, and\n", + " BP columns from annotation files if they are present\n", + " otherwise this code will not work. Before deleting, if\n", + " these columns are present, make sure that the annotation\n", + " file is sorted.**\n", + " Workflow Options:\n", + " --bim VAL (as str, required)\n", + " Path to bim file\n", + " --annot-file VAL (as str, required)\n", + " Path to annotation File. Make sure to remove the SNP,\n", + " CHR, and BP columns from the annotation file if present\n", + " before running.\n", + " --output VAL (as str, required)\n", + " name of output file\n", + " --snplist 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, remove the A1 and A2 columns\n", + " for the Calculate LD Scores program\n", + " calc_enrichment:\n", + " Workflow Options:\n", + " --sumstats VAL (as str, required)\n", + " Path to Summary statistics File\n", + " --ref-ld VAL (as str, required)\n", + " Path to Reference LD Scores Files (Base Annotation +\n", + " Annotation you want to analyze, format like minimal\n", + " working example)\n", + " --w-ld VAL (as str, required)\n", + " Path to LD Weight Files (Format like minimal working\n", + " example)\n", + " --frq-file VAL (as str, required)\n", + " path to frequency files (Format like minimal working\n", + " example)\n", + " --output VAL (as str, required)\n", + " Output name\n" + ] + } + ], + "source": [ + "!sos run LDSC_DeepSea_Code.ipynb -h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Train Model:" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "\n", + "[train_model]\n", + "\n", + "bash: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", + "\n", + " python3.7 /mnt/mfs/statgen/Anmol/training_files/run_neuron_full_pipeline.py " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Make Full Annotation File Based on Trained Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "\n", + "[make_annot]\n", + "\n", + "bash: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", + "\n", + " python3.7 /mnt/mfs/statgen/Anmol/training_files/variant_pred_pipeline.py" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Format Annotation File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "\n", + "[format_annot]\n", + "\n", + "#path to full annotation file\n", + "parameter: full_annot = str\n", + "#path to output file directory\n", + "parameter: output = str\n", + "\n", + "R: container=\"/mnt/mfs/statgen/Anmol/r-packages.sif\"\n", + " library(data.table)\n", + " library(tidyverse)\n", + " data = fread({full_annot})\n", + " data$V1 = gsub(\"chr\",\"\",data$V1)\n", + " data$V1 = as.numeric(data$V1)\n", + " features = colnames(data)[9:ncol(data)]\n", + " features = data.frame(features)\n", + " features$encoding = paste0(\"feat_\",seq(1,nrow(features)))\n", + " fwrite(features,paste0({output},\"feature_encoding.txt\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " for (i in seq(1,22)){\n", + " data_2 = filter(data,V1==i)\n", + " data_2 = select(data_2,-c(seq(4,8)))\n", + " for (j in seq(4,ncol(data_2))){\n", + " data_3 = select(data_2,c(\"V1\",\"V2\",\"V3\",j))\n", + " data_3$CM = 0\n", + " colnames(data_3) = c(\"CHR\",\"BP\",\"SNP\",paste0(\"feat_\",j),\"CM\")\n", + " fwrite(data_3,paste0({output},\"chr_\",j,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " }\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Munge Summary Statistics (Option 1: No Signed Summary Statistic):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "#This option is for when the summary statistic file does not contain a signed summary statistic (Z or Beta). \n", + "#In this case,the program will calculate Z for you based on A1 being the risk allele\n", + "[munge_sumstats_no_sign]\n", + "\n", + "\n", + "\n", + "#path to summary statistic file\n", + "parameter: sumst = str\n", + "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", + "parameter: alleles = \"w_hm3.snplist\"\n", + "#path to output file\n", + "parameter: output = str\n", + "\n", + "bash: \n", + " python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} --a1-inc" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Munge Summary Statistics (Option 2: No Signed Summary Statistic):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta)\n", + "[munge_sumstats_sign]\n", + "\n", + "\n", + "\n", + "#path to summary statistic file\n", + "parameter: sumst = str\n", + "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", + "parameter: alleles = \"w_hm3.snplist\"\n", + "#path to output file\n", + "parameter: output = str\n", + "\n", + "bash: \n", + " python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Calculate LD Scores:\n", + "\n", + "**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "#Calculate LD Scores\n", + "#**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.**\n", + "[calc_ld_score]\n", + "\n", + "#Path to bim file\n", + "parameter: bim = str\n", + "#Path to annotation File. Make sure to remove the SNP, CHR, and BP columns from the annotation file if present before running.\n", + "parameter: annot_file = str\n", + "#name of output file\n", + "parameter: output = str\n", + "#path to Hapmap3 SNPs file, remove the A1 and A2 columns for the Calculate LD Scores program \n", + "parameter: snplist = \"w_hm3.snplist\"\n", + "\n", + "bash: \n", + " python2 ldsc.py --bfile {bim} --l2 --ld-wind-cm 1 --annot {annot_file} --thin-annot --out {output} --print-snps {snplist}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Calculate Functional Enrichment using Annotations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "#Calculate Enrichment Scores for Functional Annotations\n", + "\n", + "[calc_enrichment]\n", + "\n", + "#Path to Summary statistics File\n", + "parameter: sumstats = str\n", + "#Path to Reference LD Scores Files (Base Annotation + Annotation you want to analyze, format like minimal working example)\n", + "parameter: ref_ld = str\n", + "#Path to LD Weight Files (Format like minimal working example)\n", + "parameter: w_ld = str\n", + "#path to frequency files (Format like minimal working example)\n", + "parameter: frq_file = str\n", + "#Output name\n", + "parameter: output = str\n", + "\n", + "bash:\n", + " python2 ldsc.py --h2 {sumstats} --ref-ld-chr {ref_ld} --w-ld-chr {w_ld} --overlap-annot --frqfile-chr {frq_file} --out {output}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SoS", + "language": "sos", + "name": "sos" + }, + "language_info": { + "codemirror_mode": "sos", + "file_extension": ".sos", + "mimetype": "text/x-sos", + "name": "sos", + "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", + "pygments_lexer": "sos" + }, + "sos": { + "kernels": [ + [ + "Python 3 (ipykernel)", + "python3", + "python3", + "", + { + "name": "ipython", + "version": 3 + } + ], + [ + "SoS", + "sos", + "", + "", + "sos" + ] + ], + "panel": { + "displayed": true, + "height": 0 + }, + "version": "0.22.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From ca093b14008fe50f83a3fc1165213355b5b6fba8 Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Thu, 24 Feb 2022 20:51:55 -0500 Subject: [PATCH 08/63] Delete LDSC_DeepSea_Code.ipynb --- LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb | 403 --------------------- 1 file changed, 403 deletions(-) delete mode 100644 LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb b/LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb deleted file mode 100644 index d7e0c42..0000000 --- a/LDSC/Deep_Learning/LDSC_DeepSea_Code.ipynb +++ /dev/null @@ -1,403 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## SoS Workflow:\n", - "\n", - "This is the options and the SoS code to run the LDSC pipeline using your own data. \n", - "\n", - "## Command Interface:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "kernel": "SoS" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "usage: sos run LDSC_DeepSea_Code.ipynb\n", - " [workflow_name | -t targets] [options] [workflow_options]\n", - " workflow_name: Single or combined workflows defined in this script\n", - " targets: One or more targets to generate\n", - " options: Single-hyphen sos parameters (see \"sos run -h\" for details)\n", - " workflow_options: Double-hyphen workflow-specific parameters\n", - "\n", - "Workflows:\n", - " train_model\n", - " make_annot\n", - " format_annot\n", - " munge_sumstats_no_sign\n", - " munge_sumstats_sign\n", - " calc_ld_score\n", - " calc_enrichment\n", - "\n", - "Sections\n", - " train_model:\n", - " make_annot:\n", - " format_annot:\n", - " Workflow Options:\n", - " --full-annot VAL (as str, required)\n", - " path to full annotation file\n", - " --output VAL (as str, required)\n", - " path to output file directory\n", - " munge_sumstats_no_sign: This option is for when the summary statistic file\n", - " does not contain a signed summary statistic (Z or Beta).\n", - " In this case,the program will calculate Z for you based\n", - " on A1 being the risk allele\n", - " Workflow Options:\n", - " --sumst VAL (as str, required)\n", - " path to summary statistic file\n", - " --alleles 'w_hm3.snplist'\n", - " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", - " and A2) for the munge_sumstats program\n", - " --output VAL (as str, required)\n", - " path to output file\n", - " munge_sumstats_sign: This option is for when the summary statistic file does\n", - " contain a signed summary statistic (Z or Beta)\n", - " Workflow Options:\n", - " --sumst VAL (as str, required)\n", - " path to summary statistic file\n", - " --alleles 'w_hm3.snplist'\n", - " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", - " and A2) for the munge_sumstats program\n", - " --output VAL (as str, required)\n", - " path to output file\n", - " calc_ld_score: Calculate LD Scores **Make sure to delete SNP,CHR, and\n", - " BP columns from annotation files if they are present\n", - " otherwise this code will not work. Before deleting, if\n", - " these columns are present, make sure that the annotation\n", - " file is sorted.**\n", - " Workflow Options:\n", - " --bim VAL (as str, required)\n", - " Path to bim file\n", - " --annot-file VAL (as str, required)\n", - " Path to annotation File. Make sure to remove the SNP,\n", - " CHR, and BP columns from the annotation file if present\n", - " before running.\n", - " --output VAL (as str, required)\n", - " name of output file\n", - " --snplist 'w_hm3.snplist'\n", - " path to Hapmap3 SNPs file, remove the A1 and A2 columns\n", - " for the Calculate LD Scores program\n", - " calc_enrichment:\n", - " Workflow Options:\n", - " --sumstats VAL (as str, required)\n", - " Path to Summary statistics File\n", - " --ref-ld VAL (as str, required)\n", - " Path to Reference LD Scores Files (Base Annotation +\n", - " Annotation you want to analyze, format like minimal\n", - " working example)\n", - " --w-ld VAL (as str, required)\n", - " Path to LD Weight Files (Format like minimal working\n", - " example)\n", - " --frq-file VAL (as str, required)\n", - " path to frequency files (Format like minimal working\n", - " example)\n", - " --output VAL (as str, required)\n", - " Output name\n" - ] - } - ], - "source": [ - "!sos run LDSC_DeepSea_Code.ipynb -h" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "Python 3 (ipykernel)" - }, - "source": [ - "## Train Model:" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "\n", - "[train_model]\n", - "\n", - "bash: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", - "\n", - " python3.7 /mnt/mfs/statgen/Anmol/training_files/run_neuron_full_pipeline.py " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Make Full Annotation File Based on Trained Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "\n", - "[make_annot]\n", - "\n", - "bash: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", - "\n", - " python3.7 /mnt/mfs/statgen/Anmol/training_files/variant_pred_pipeline.py" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Format Annotation File" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "\n", - "[format_annot]\n", - "\n", - "#path to full annotation file\n", - "parameter: full_annot = str\n", - "#path to output file directory\n", - "parameter: output = str\n", - "\n", - "R: container=\"/mnt/mfs/statgen/Anmol/r-packages.sif\"\n", - " library(data.table)\n", - " library(tidyverse)\n", - " data = fread({full_annot})\n", - " data$V1 = gsub(\"chr\",\"\",data$V1)\n", - " data$V1 = as.numeric(data$V1)\n", - " features = colnames(data)[9:ncol(data)]\n", - " features = data.frame(features)\n", - " features$encoding = paste0(\"feat_\",seq(1,nrow(features)))\n", - " fwrite(features,paste0({output},\"feature_encoding.txt\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", - " for (i in seq(1,22)){\n", - " data_2 = filter(data,V1==i)\n", - " data_2 = select(data_2,-c(seq(4,8)))\n", - " for (j in seq(4,ncol(data_2))){\n", - " data_3 = select(data_2,c(\"V1\",\"V2\",\"V3\",j))\n", - " data_3$CM = 0\n", - " colnames(data_3) = c(\"CHR\",\"BP\",\"SNP\",paste0(\"feat_\",j),\"CM\")\n", - " fwrite(data_3,paste0({output},\"chr_\",j,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", - " }\n", - " }" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "Python 3 (ipykernel)" - }, - "source": [ - "## Munge Summary Statistics (Option 1: No Signed Summary Statistic):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "#This option is for when the summary statistic file does not contain a signed summary statistic (Z or Beta). \n", - "#In this case,the program will calculate Z for you based on A1 being the risk allele\n", - "[munge_sumstats_no_sign]\n", - "\n", - "\n", - "\n", - "#path to summary statistic file\n", - "parameter: sumst = str\n", - "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", - "parameter: alleles = \"w_hm3.snplist\"\n", - "#path to output file\n", - "parameter: output = str\n", - "\n", - "bash: \n", - " python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} --a1-inc" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Munge Summary Statistics (Option 2: No Signed Summary Statistic):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "# This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta)\n", - "[munge_sumstats_sign]\n", - "\n", - "\n", - "\n", - "#path to summary statistic file\n", - "parameter: sumst = str\n", - "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", - "parameter: alleles = \"w_hm3.snplist\"\n", - "#path to output file\n", - "parameter: output = str\n", - "\n", - "bash: \n", - " python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "Python 3 (ipykernel)" - }, - "source": [ - "## Calculate LD Scores:\n", - "\n", - "**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "#Calculate LD Scores\n", - "#**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.**\n", - "[calc_ld_score]\n", - "\n", - "#Path to bim file\n", - "parameter: bim = str\n", - "#Path to annotation File. Make sure to remove the SNP, CHR, and BP columns from the annotation file if present before running.\n", - "parameter: annot_file = str\n", - "#name of output file\n", - "parameter: output = str\n", - "#path to Hapmap3 SNPs file, remove the A1 and A2 columns for the Calculate LD Scores program \n", - "parameter: snplist = \"w_hm3.snplist\"\n", - "\n", - "bash: \n", - " python2 ldsc.py --bfile {bim} --l2 --ld-wind-cm 1 --annot {annot_file} --thin-annot --out {output} --print-snps {snplist}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "Python 3 (ipykernel)" - }, - "source": [ - "## Calculate Functional Enrichment using Annotations:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "#Calculate Enrichment Scores for Functional Annotations\n", - "\n", - "[calc_enrichment]\n", - "\n", - "#Path to Summary statistics File\n", - "parameter: sumstats = str\n", - "#Path to Reference LD Scores Files (Base Annotation + Annotation you want to analyze, format like minimal working example)\n", - "parameter: ref_ld = str\n", - "#Path to LD Weight Files (Format like minimal working example)\n", - "parameter: w_ld = str\n", - "#path to frequency files (Format like minimal working example)\n", - "parameter: frq_file = str\n", - "#Output name\n", - "parameter: output = str\n", - "\n", - "bash:\n", - " python2 ldsc.py --h2 {sumstats} --ref-ld-chr {ref_ld} --w-ld-chr {w_ld} --overlap-annot --frqfile-chr {frq_file} --out {output}" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "SoS", - "language": "sos", - "name": "sos" - }, - "language_info": { - "codemirror_mode": "sos", - "file_extension": ".sos", - "mimetype": "text/x-sos", - "name": "sos", - "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", - "pygments_lexer": "sos" - }, - "sos": { - "kernels": [ - [ - "Python 3 (ipykernel)", - "python3", - "python3", - "", - { - "name": "ipython", - "version": 3 - } - ], - [ - "SoS", - "sos", - "", - "", - "sos" - ] - ], - "panel": { - "displayed": true, - "height": 0 - }, - "version": "0.22.9" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 905f21556cd58703c3c4aa399f2b636098260aac Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Thu, 24 Feb 2022 20:52:10 -0500 Subject: [PATCH 09/63] Add files via upload --- .../LDSC_DeepSea_Minimal_Example.ipynb | 445 ++++++++++++++++++ .../LDSC_DeepSea_Minimal_Example.sos | 139 ++++++ 2 files changed, 584 insertions(+) create mode 100644 LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb create mode 100644 LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb new file mode 100644 index 0000000..ca0012d --- /dev/null +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## SoS Workflow:\n", + "\n", + "This is the options and the SoS code to run the LDSC pipeline using your own data. \n", + "\n", + "## Command Interface:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "kernel": "SoS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: sos run LDSC_DeepSea_Code.ipynb\n", + " [workflow_name | -t targets] [options] [workflow_options]\n", + " workflow_name: Single or combined workflows defined in this script\n", + " targets: One or more targets to generate\n", + " options: Single-hyphen sos parameters (see \"sos run -h\" for details)\n", + " workflow_options: Double-hyphen workflow-specific parameters\n", + "\n", + "Workflows:\n", + " train_model\n", + " make_annot\n", + " format_annot\n", + " munge_sumstats_no_sign\n", + " munge_sumstats_sign\n", + " calc_ld_score\n", + " calc_enrichment\n", + "\n", + "Sections\n", + " train_model:\n", + " make_annot:\n", + " Workflow Options:\n", + " --feature-list VAL (as str, required)\n", + " path to feature list file\n", + " --model VAL (as str, required)\n", + " path to trained model location\n", + " --output VAL (as str, required)\n", + " path to output directory\n", + " --num-features VAL (as int, required)\n", + " number of features\n", + " format_annot:\n", + " Workflow Options:\n", + " --full-annot VAL (as str, required)\n", + " path to full annotation file\n", + " --output VAL (as str, required)\n", + " path to output file directory\n", + " munge_sumstats_no_sign: This option is for when the summary statistic file\n", + " does not contain a signed summary statistic (Z or Beta).\n", + " In this case,the program will calculate Z for you based\n", + " on A1 being the risk allele\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output VAL (as str, required)\n", + " path to output file\n", + " munge_sumstats_sign: This option is for when the summary statistic file does\n", + " contain a signed summary statistic (Z or Beta)\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output VAL (as str, required)\n", + " path to output file\n", + " calc_ld_score: Calculate LD Scores **Make sure to delete SNP,CHR, and\n", + " BP columns from annotation files if they are present\n", + " otherwise this code will not work. Before deleting, if\n", + " these columns are present, make sure that the annotation\n", + " file is sorted.**\n", + " Workflow Options:\n", + " --bim VAL (as str, required)\n", + " Path to bim file\n", + " --annot-file VAL (as str, required)\n", + " Path to annotation File. Make sure to remove the SNP,\n", + " CHR, and BP columns from the annotation file if present\n", + " before running.\n", + " --output VAL (as str, required)\n", + " name of output file\n", + " --snplist 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, remove the A1 and A2 columns\n", + " for the Calculate LD Scores program\n", + " calc_enrichment:\n", + " Workflow Options:\n", + " --sumstats VAL (as str, required)\n", + " Path to Summary statistics File\n", + " --ref-ld VAL (as str, required)\n", + " Path to Reference LD Scores Files (Base Annotation +\n", + " Annotation you want to analyze, format like minimal\n", + " working example)\n", + " --w-ld VAL (as str, required)\n", + " Path to LD Weight Files (Format like minimal working\n", + " example)\n", + " --frq-file VAL (as str, required)\n", + " path to frequency files (Format like minimal working\n", + " example)\n", + " --output VAL (as str, required)\n", + " Output name\n" + ] + } + ], + "source": [ + "!sos run LDSC_DeepSea_Code.ipynb -h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Train Model:" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "\n", + "[train_model]\n", + "\n", + "bash: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", + "\n", + " python3.7 /mnt/mfs/statgen/Anmol/training_files/tutorial/run_neuron_full_tutorial.py " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Make Full Annotation File Based on Trained Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "\n", + "[make_annot]\n", + "\n", + "#path to feature list file\n", + "parameter: feature_list = str\n", + "#path to trained model location\n", + "parameter: model = str\n", + "#path to output directory\n", + "parameter: output = str\n", + "#number of features\n", + "parameter: num_features = int\n", + "\n", + "python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", + "\n", + " from selene_sdk.utils import load_path\n", + " from selene_sdk.utils import parse_configs_and_run\n", + " from selene_sdk.predict import AnalyzeSequences\n", + " from selene_sdk.sequences import Genome\n", + " from selene_sdk.utils import load_features_list\n", + " from selene_sdk.utils import NonStrandSpecific\n", + " from selene_sdk.utils import DeeperDeepSEA\n", + " import glob\n", + " import os\n", + " distinct_features = load_features_list({feature_list})\n", + "\n", + " model_predict = AnalyzeSequences(\n", + " NonStrandSpecific(DeeperDeepSEA(1000,{num_features})),\n", + " {model},\n", + " sequence_length=1000,\n", + " features=distinct_features,\n", + " reference_sequence=Genome(\"/mnt/mfs/statgen/Anmol/training_files/male.hg19.fasta\"),\n", + " use_cuda=False # update this to False if you do not have CUDA on your machine.\n", + " )\n", + "\n", + " for i in range(1,22):\n", + " model_predict.variant_effect_prediction(\n", + " \"/mnt/mfs/statgen/Anmol/training_files/testing/1000G_chr_\"+str(i)+\".vcf\",\n", + " save_data=[\"abs_diffs\"], # only want to save the absolute diff score data\n", + " output_dir={output})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Format Annotation File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "\n", + "[format_annot]\n", + "\n", + "#path to tsv files directory\n", + "parameter: tsv = path()\n", + "#path to output file directory\n", + "parameter: output = path()\n", + "\n", + "R: expand = \"${ }\", container=\"/mnt/mfs/statgen/Anmol/r-packages.sif\"\n", + " library(data.table)\n", + " library(tidyverse)\n", + " data = fread(paste0(\"${tsv}\",\"/tutorial_1000G_chr_\",22,\"_abs_diffs.tsv\"))\n", + " features = colnames(data)[9:ncol(data)]\n", + " features = data.frame(features)\n", + " features$encoding = paste0(\"feat_\",seq(1,nrow(features)))\n", + " fwrite(features,paste0(\"${output}\",\"/feature_encoding.txt\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " for (i in seq(1,22)){\n", + " data = fread(paste0(\"${tsv}\",\"/tutorial_1000G_chr_\",i,\"_abs_diffs.tsv\"))\n", + " data$V1 = gsub(\"chr\",\"\",data$V1)\n", + " data$V1 = as.numeric(data$V1)\n", + " data_2 = select(data,-c(seq(4,8)))\n", + " for (j in seq(4,ncol(data_2))){\n", + " data_3 = select(data_2,c(1,2,3,j))\n", + " data_3$CM = 0\n", + " colnames(data_3) = c(\"CHR\",\"BP\",\"SNP\",paste0(\"feat_\",j),\"CM\")\n", + " fwrite(data_3,paste0(\"${output}\",\"/feat_\",j,\"_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " }\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Munge Summary Statistics (Option 1: No Signed Summary Statistic):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "#This option is for when the summary statistic file does not contain a signed summary statistic (Z or Beta). \n", + "#In this case,the program will calculate Z for you based on A1 being the risk allele\n", + "[munge_sumstats_no_sign]\n", + "\n", + "\n", + "\n", + "#path to summary statistic file\n", + "parameter: sumst = str\n", + "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", + "parameter: alleles = \"w_hm3.snplist\"\n", + "#path to output file\n", + "parameter: output = str\n", + "\n", + "bash: \n", + " python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} --a1-inc" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Munge Summary Statistics (Option 2: No Signed Summary Statistic):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta)\n", + "[munge_sumstats_sign]\n", + "\n", + "\n", + "\n", + "#path to summary statistic file\n", + "parameter: sumst = str\n", + "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", + "parameter: alleles = \"w_hm3.snplist\"\n", + "#path to output file\n", + "parameter: output = str\n", + "\n", + "bash: \n", + " python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Calculate LD Scores:\n", + "\n", + "**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "#Calculate LD Scores\n", + "#**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.**\n", + "[calc_ld_score]\n", + "\n", + "#Path to bim file\n", + "parameter: bim = str\n", + "#Path to annotation File. Make sure to remove the SNP, CHR, and BP columns from the annotation file if present before running.\n", + "parameter: annot_file = str\n", + "#name of output file\n", + "parameter: output = str\n", + "#path to Hapmap3 SNPs file, remove the A1 and A2 columns for the Calculate LD Scores program \n", + "parameter: snplist = \"w_hm3.snplist\"\n", + "\n", + "bash: \n", + " python2 ldsc.py --bfile {bim} --l2 --ld-wind-cm 1 --annot {annot_file} --thin-annot --out {output} --print-snps {snplist}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Calculate Functional Enrichment using Annotations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "#Calculate Enrichment Scores for Functional Annotations\n", + "\n", + "[calc_enrichment]\n", + "\n", + "#Path to Summary statistics File\n", + "parameter: sumstats = str\n", + "#Path to Reference LD Scores Files (Base Annotation + Annotation you want to analyze, format like minimal working example)\n", + "parameter: ref_ld = str\n", + "#Path to LD Weight Files (Format like minimal working example)\n", + "parameter: w_ld = str\n", + "#path to frequency files (Format like minimal working example)\n", + "parameter: frq_file = str\n", + "#Output name\n", + "parameter: output = str\n", + "\n", + "bash:\n", + " python2 ldsc.py --h2 {sumstats} --ref-ld-chr {ref_ld} --w-ld-chr {w_ld} --overlap-annot --frqfile-chr {frq_file} --out {output}" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SoS", + "language": "sos", + "name": "sos" + }, + "language_info": { + "codemirror_mode": "sos", + "file_extension": ".sos", + "mimetype": "text/x-sos", + "name": "sos", + "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", + "pygments_lexer": "sos" + }, + "sos": { + "kernels": [ + [ + "Python 3 (ipykernel)", + "python3", + "python3", + "", + { + "name": "ipython", + "version": 3 + } + ], + [ + "SoS", + "sos", + "", + "", + "sos" + ] + ], + "panel": { + "displayed": true, + "height": 0 + }, + "version": "0.22.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos new file mode 100644 index 0000000..c082c8c --- /dev/null +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos @@ -0,0 +1,139 @@ +#!/usr/bin/env sos-runner +#fileformat=SOS1.0 + +[train_model] + +bash: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' + + python3.7 /mnt/mfs/statgen/Anmol/training_files/tutorial/run_neuron_full_tutorial.py + +[make_annot] + +#path to feature list file +parameter: feature_list = str +#path to trained model location +parameter: model = str +#path to output directory +parameter: output = str +#number of features +parameter: num_features = int + +python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' + + from selene_sdk.utils import load_path + from selene_sdk.utils import parse_configs_and_run + from selene_sdk.predict import AnalyzeSequences + from selene_sdk.sequences import Genome + from selene_sdk.utils import load_features_list + from selene_sdk.utils import NonStrandSpecific + from selene_sdk.utils import DeeperDeepSEA + import glob + import os + distinct_features = load_features_list({feature_list}) + + model_predict = AnalyzeSequences( + NonStrandSpecific(DeeperDeepSEA(1000,{num_features})), + {model}, + sequence_length=1000, + features=distinct_features, + reference_sequence=Genome("/mnt/mfs/statgen/Anmol/training_files/male.hg19.fasta"), + use_cuda=False # update this to False if you do not have CUDA on your machine. + ) + + for i in range(1,22): + model_predict.variant_effect_prediction( + "/mnt/mfs/statgen/Anmol/training_files/testing/1000G_chr_"+str(i)+".vcf", + save_data=["abs_diffs"], # only want to save the absolute diff score data + output_dir={output}) + +[format_annot] + +#path to tsv files directory +parameter: tsv = path() +#path to output file directory +parameter: output = path() + +R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" + library(data.table) + library(tidyverse) + data = fread(paste0("${tsv}","/tutorial_1000G_chr_",22,"_abs_diffs.tsv")) + features = colnames(data)[9:ncol(data)] + features = data.frame(features) + features$encoding = paste0("feat_",seq(1,nrow(features))) + fwrite(features,paste0("${output}","/feature_encoding.txt"),quote=F,sep="\t",row.names=F,col.names=T) + for (i in seq(1,22)){ + data = fread(paste0("${tsv}","/tutorial_1000G_chr_",i,"_abs_diffs.tsv")) + data$V1 = gsub("chr","",data$V1) + data$V1 = as.numeric(data$V1) + data_2 = select(data,-c(seq(4,8))) + for (j in seq(4,ncol(data_2))){ + data_3 = select(data_2,c(1,2,3,j)) + data_3$CM = 0 + colnames(data_3) = c("CHR","BP","SNP",paste0("feat_",j),"CM") + fwrite(data_3,paste0("${output}","/feat_",j,"_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) + } + } + +#This option is for when the summary statistic file does not contain a signed summary statistic (Z or Beta). +#In this case,the program will calculate Z for you based on A1 being the risk allele +[munge_sumstats_no_sign] + + + +#path to summary statistic file +parameter: sumst = str +#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program +parameter: alleles = "w_hm3.snplist" +#path to output file +parameter: output = str + +bash: + python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} --a1-inc + +# This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta) +[munge_sumstats_sign] + + + +#path to summary statistic file +parameter: sumst = str +#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program +parameter: alleles = "w_hm3.snplist" +#path to output file +parameter: output = str + +bash: + python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} + +#Calculate LD Scores +#**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.** +[calc_ld_score] + +#Path to bim file +parameter: bim = str +#Path to annotation File. Make sure to remove the SNP, CHR, and BP columns from the annotation file if present before running. +parameter: annot_file = str +#name of output file +parameter: output = str +#path to Hapmap3 SNPs file, remove the A1 and A2 columns for the Calculate LD Scores program +parameter: snplist = "w_hm3.snplist" + +bash: + python2 ldsc.py --bfile {bim} --l2 --ld-wind-cm 1 --annot {annot_file} --thin-annot --out {output} --print-snps {snplist} + +[calc_enrichment] + +#Path to Summary statistics File +parameter: sumstats = str +#Path to Reference LD Scores Files (Base Annotation + Annotation you want to analyze, format like minimal working example) +parameter: ref_ld = str +#Path to LD Weight Files (Format like minimal working example) +parameter: w_ld = str +#path to frequency files (Format like minimal working example) +parameter: frq_file = str +#Output name +parameter: output = str + +bash: + python2 ldsc.py --h2 {sumstats} --ref-ld-chr {ref_ld} --w-ld-chr {w_ld} --overlap-annot --frqfile-chr {frq_file} --out {output} + From 5c47a0a6610ab1e078587e6185aaf985e8cc80c6 Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Fri, 25 Feb 2022 11:57:31 -0500 Subject: [PATCH 10/63] add yml parameter --- GWAS/liftover.ipynb | 23 +++++++++++++++++++---- 1 file changed, 19 insertions(+), 4 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 7209a1e..e82c88c 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -59,6 +59,18 @@ " - if plink format, provide the path of `bim` file \n", " - if gvcf/vcf format, the file must have gvcf and vcf in suffixes\n", " - other format will be considered as sumstat format\n", + "- `--yml_file`, if the sumstat header doesn't have CHR, POS, A0 and A1 columns, you need to provide a ymal file to describe the format of your file, such as following. the first 5 row is required.\n", + "```\n", + "ID: CHR,POS,A0,A1\n", + "CHR: CHR\n", + "POS: POS\n", + "A0: REF\n", + "A1: ALT\n", + "SNP: SNP\n", + "STAT: BETA\n", + "SE: SE\n", + "P: P\n", + "```\n", "- `--ouput_file`, the name of ouput file which will be saved under `cwd` path\n", "- `--fr`, From reference genome, defaut is `hg19`\n", "- `--to`,To reference genome, defaut is `hg38`\n", @@ -129,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "4665b6b1-5e14-41b1-8537-70082c3f38dd", "metadata": { "kernel": "SoS", @@ -142,6 +154,8 @@ "parameter: cwd = path\n", "# Input file which can be plink format, gvcf/vcf format, sumstat format.\n", "parameter: input_file = path\n", + "# The path of yaml file with input file format, only for sumstat file.\n", + "parameter: yml_file = path #Fixme setting defualt to `None`\n", "# the name of ouput file which will be saved under cwd path\n", "parameter: output_file = path\n", "# From reference genome, defaut is hg19\n", @@ -175,7 +189,7 @@ " from LDtools.genodata import *\n", " from LDtools.sumstat import Sumstat\n", " from LDtools.liftover import Liftover\n", - " def liftover(input_path,output_path,fr='hg19',to='hg38',remove_missing=True,rename=True):\n", + " def liftover(input_path,output_path,yml=None,fr='hg19',to='hg38',remove_missing=True,rename=True):\n", " lf = Liftover(fr,to)\n", " print(\"liftover from \" + fr +\" to \" +to)\n", " print(\"Removing SNPs failed to liftover is\", remove_missing)\n", @@ -206,7 +220,7 @@ " lf.vcf_liftover(input_path,output_path,remove_missing)\n", " else:\n", " print(\"This file is considered as sumstat format file\")\n", - " sums = Sumstat(input_path,rename=rename)\n", + " sums = Sumstat(input_path,config_file=yml,rename=rename)\n", " new_sums = lf.sumstat_liftover(sums.ss,rename)\n", " idx = new_sums.CHR == 0\n", " if remove_missing:\n", @@ -243,8 +257,9 @@ " to = f'${to}'\n", " remove_missing=${remove_missing}\n", " rename = ${rename}\n", + " yml_file = f'${yml_file}'\n", " print(fr,to,remove_missing)\n", - " liftover(input_path,output_path,fr,to,remove_missing,rename)" + " liftover(input_path,output_path,yml_file,fr,to,remove_missing,rename)" ] } ], From 2f8ffdc8908e58d168b39f54ed4cd046fa894e44 Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Fri, 25 Feb 2022 13:26:50 -0500 Subject: [PATCH 11/63] update --- GWAS/liftover.ipynb | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index e82c88c..15dac8d 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -58,7 +58,7 @@ "- `--input_file`, the path of input file which can be plink format, gvcf/vcf format, sumstat format.\n", " - if plink format, provide the path of `bim` file \n", " - if gvcf/vcf format, the file must have gvcf and vcf in suffixes\n", - " - other format will be considered as sumstat format\n", + " - other format will be considered as sumstat format, whose header should have CHR, POS, A0 and A1 columns\n", "- `--yml_file`, if the sumstat header doesn't have CHR, POS, A0 and A1 columns, you need to provide a ymal file to describe the format of your file, such as following. the first 5 row is required.\n", "```\n", "ID: CHR,POS,A0,A1\n", @@ -155,7 +155,7 @@ "# Input file which can be plink format, gvcf/vcf format, sumstat format.\n", "parameter: input_file = path\n", "# The path of yaml file with input file format, only for sumstat file.\n", - "parameter: yml_file = path #Fixme setting defualt to `None`\n", + "parameter: yml_file = path() \n", "# the name of ouput file which will be saved under cwd path\n", "parameter: output_file = path\n", "# From reference genome, defaut is hg19\n", @@ -257,7 +257,10 @@ " to = f'${to}'\n", " remove_missing=${remove_missing}\n", " rename = ${rename}\n", - " yml_file = f'${yml_file}'\n", + " if yml_file.is_file(): \n", + " yml_file = f'${yml_file}'\n", + " else:\n", + " yml_file = None\n", " print(fr,to,remove_missing)\n", " liftover(input_path,output_path,yml_file,fr,to,remove_missing,rename)" ] From 6e9370652df733d4580edfd43794d2feec8c2345 Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Sun, 27 Feb 2022 19:18:58 -0500 Subject: [PATCH 12/63] Add files via upload --- .../LDSC_DeepSea_Minimal_Example.ipynb | 188 +++++++----------- .../LDSC_DeepSea_Minimal_Example.sos | 67 +++++-- 2 files changed, 119 insertions(+), 136 deletions(-) diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb index ca0012d..d2a9d44 100644 --- a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": { "kernel": "SoS" }, @@ -24,95 +24,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "usage: sos run LDSC_DeepSea_Code.ipynb\n", - " [workflow_name | -t targets] [options] [workflow_options]\n", - " workflow_name: Single or combined workflows defined in this script\n", - " targets: One or more targets to generate\n", - " options: Single-hyphen sos parameters (see \"sos run -h\" for details)\n", - " workflow_options: Double-hyphen workflow-specific parameters\n", - "\n", - "Workflows:\n", - " train_model\n", - " make_annot\n", - " format_annot\n", - " munge_sumstats_no_sign\n", - " munge_sumstats_sign\n", - " calc_ld_score\n", - " calc_enrichment\n", - "\n", - "Sections\n", - " train_model:\n", - " make_annot:\n", - " Workflow Options:\n", - " --feature-list VAL (as str, required)\n", - " path to feature list file\n", - " --model VAL (as str, required)\n", - " path to trained model location\n", - " --output VAL (as str, required)\n", - " path to output directory\n", - " --num-features VAL (as int, required)\n", - " number of features\n", - " format_annot:\n", - " Workflow Options:\n", - " --full-annot VAL (as str, required)\n", - " path to full annotation file\n", - " --output VAL (as str, required)\n", - " path to output file directory\n", - " munge_sumstats_no_sign: This option is for when the summary statistic file\n", - " does not contain a signed summary statistic (Z or Beta).\n", - " In this case,the program will calculate Z for you based\n", - " on A1 being the risk allele\n", - " Workflow Options:\n", - " --sumst VAL (as str, required)\n", - " path to summary statistic file\n", - " --alleles 'w_hm3.snplist'\n", - " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", - " and A2) for the munge_sumstats program\n", - " --output VAL (as str, required)\n", - " path to output file\n", - " munge_sumstats_sign: This option is for when the summary statistic file does\n", - " contain a signed summary statistic (Z or Beta)\n", - " Workflow Options:\n", - " --sumst VAL (as str, required)\n", - " path to summary statistic file\n", - " --alleles 'w_hm3.snplist'\n", - " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", - " and A2) for the munge_sumstats program\n", - " --output VAL (as str, required)\n", - " path to output file\n", - " calc_ld_score: Calculate LD Scores **Make sure to delete SNP,CHR, and\n", - " BP columns from annotation files if they are present\n", - " otherwise this code will not work. Before deleting, if\n", - " these columns are present, make sure that the annotation\n", - " file is sorted.**\n", - " Workflow Options:\n", - " --bim VAL (as str, required)\n", - " Path to bim file\n", - " --annot-file VAL (as str, required)\n", - " Path to annotation File. Make sure to remove the SNP,\n", - " CHR, and BP columns from the annotation file if present\n", - " before running.\n", - " --output VAL (as str, required)\n", - " name of output file\n", - " --snplist 'w_hm3.snplist'\n", - " path to Hapmap3 SNPs file, remove the A1 and A2 columns\n", - " for the Calculate LD Scores program\n", - " calc_enrichment:\n", - " Workflow Options:\n", - " --sumstats VAL (as str, required)\n", - " Path to Summary statistics File\n", - " --ref-ld VAL (as str, required)\n", - " Path to Reference LD Scores Files (Base Annotation +\n", - " Annotation you want to analyze, format like minimal\n", - " working example)\n", - " --w-ld VAL (as str, required)\n", - " Path to LD Weight Files (Format like minimal working\n", - " example)\n", - " --frq-file VAL (as str, required)\n", - " path to frequency files (Format like minimal working\n", - " example)\n", - " --output VAL (as str, required)\n", - " Output name\n" + "No help information is available for script run: Failed to locate LDSC_DeepSea_Code.ipynb.sos\r\n" ] } ], @@ -180,8 +92,7 @@ "parameter: model = str\n", "#path to output directory\n", "parameter: output = str\n", - "#number of features\n", - "parameter: num_features = int\n", + "\n", "\n", "python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", "\n", @@ -247,13 +158,14 @@ " fwrite(features,paste0(\"${output}\",\"/feature_encoding.txt\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " for (i in seq(1,22)){\n", " data = fread(paste0(\"${tsv}\",\"/tutorial_1000G_chr_\",i,\"_abs_diffs.tsv\"))\n", - " data$V1 = gsub(\"chr\",\"\",data$V1)\n", - " data$V1 = as.numeric(data$V1)\n", - " data_2 = select(data,-c(seq(4,8)))\n", + " data_2 = select(data,-seq(4,8))\n", + " base = data.frame(base=rep(1,nrow(data_2)))\n", + " fwrite(base,paste0(\"${output}\",\"/base_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " for (j in seq(4,ncol(data_2))){\n", " data_3 = select(data_2,c(1,2,3,j))\n", - " data_3$CM = 0\n", - " colnames(data_3) = c(\"CHR\",\"BP\",\"SNP\",paste0(\"feat_\",j),\"CM\")\n", + " colnames(data_3) = c(\"CHR\",\"BP\",\"SNP\",paste0(\"feat_\",j))\n", + " data_3 = setorder(data_3,BP)\n", + " data_3 = select(data_3,-c(\"CHR\",\"BP\",\"SNP\"))\n", " fwrite(data_3,paste0(\"${output}\",\"/feat_\",j,\"_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " }\n", " }" @@ -345,21 +257,71 @@ }, "outputs": [], "source": [ - "#Calculate LD Scores\n", - "#**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.**\n", + "\n", "[calc_ld_score]\n", "\n", - "#Path to bim file\n", - "parameter: bim = str\n", - "#Path to annotation File. Make sure to remove the SNP, CHR, and BP columns from the annotation file if present before running.\n", - "parameter: annot_file = str\n", - "#name of output file\n", - "parameter: output = str\n", - "#path to Hapmap3 SNPs file, remove the A1 and A2 columns for the Calculate LD Scores program \n", - "parameter: snplist = \"w_hm3.snplist\"\n", + "#Path to directory with bim files\n", + "parameter: bim = path()\n", + "#Path to directory with annotation files, output will appear here too. Make sure to remove the SNP, CHR, and BP columns from the annotation files if present before running.\n", + "parameter: annot_files = path()\n", + "#number of features\n", + "parameter: num_features = int\n", "\n", "bash: \n", - " python2 ldsc.py --bfile {bim} --l2 --ld-wind-cm 1 --annot {annot_file} --thin-annot --out {output} --print-snps {snplist}" + " #echo {annot_files} > out.txt\n", + " for i in $(seq 1 {num_features});do for j in {1..22}; do python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile {bim}/1000G.EUR.QC.${j} --l2 --ld-wind-cm 1 --annot {annot_files}/feat_${i}_chr_${j}.annot.gz --thin-annot --out {annot_files}/feat_${i}_chr_${j} --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt; done; done\n", + " for j in {1..22}; do python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile {bim}/1000G.EUR.QC.${j} --l2 --ld-wind-cm 1 --annot {annot_files}/base_chr_${j}.annot.gz --thin-annot --out {annot_files}/base_chr_${j} --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt; done" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Convert LD Score SNPs to AD Summary Statistic Format:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "\n", + "[convert_ld_snps]\n", + "\n", + "#Path to directory with ld score files AND annotation files\n", + "parameter: ld_scores = str\n", + "\n", + "parameter: num_features = int\n", + "\n", + "\n", + "R: expand = \"${ }\", container=\"/mnt/mfs/statgen/Anmol/r-packages.sif\"\n", + " library(tidyverse)\n", + " #library(R.utils)\n", + " library(data.table)\n", + " for (i in seq(1,22)){\n", + " data = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".l2.ldscore.gz\")))\n", + " data_2 = fread(paste0(\"${ld_scores}/base_chr_\",i,\".l2.M_5_50\"))\n", + " data_3 = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".annot.gz\")))\n", + " data$SNP = paste0(data$CHR,\":\",data$BP)\n", + " fwrite(data,paste0(\"${ld_scores}/AD_base_chr_\",i,\".l2.ldscore.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " fwrite(data_2,paste0(\"${ld_scores}/AD_base_chr_\",i,\".l2.M_5_50\"),quote=F,sep=\"\\t\",row.names=F,col.names=F)\n", + " fwrite(data_3,paste0(\"${ld_scores}/AD_base_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " for (j in seq(1,${num_features})){\n", + " data = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\")))\n", + " data_2 = fread(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.M_5_50\"))\n", + " data_3 = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".annot.gz\")))\n", + " data$SNP = paste0(data$CHR,\":\",data$BP)\n", + " fwrite(data,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " fwrite(data_2,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".l2.M_5_50\"),quote=F,sep=\"\\t\",row.names=F,col.names=F)\n", + " fwrite(data_3,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " }\n", + " }\n", + " \n" ] }, { @@ -415,16 +377,6 @@ }, "sos": { "kernels": [ - [ - "Python 3 (ipykernel)", - "python3", - "python3", - "", - { - "name": "ipython", - "version": 3 - } - ], [ "SoS", "sos", diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos index c082c8c..d3bee84 100644 --- a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos @@ -15,8 +15,7 @@ parameter: feature_list = str parameter: model = str #path to output directory parameter: output = str -#number of features -parameter: num_features = int + python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' @@ -63,13 +62,14 @@ R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" fwrite(features,paste0("${output}","/feature_encoding.txt"),quote=F,sep="\t",row.names=F,col.names=T) for (i in seq(1,22)){ data = fread(paste0("${tsv}","/tutorial_1000G_chr_",i,"_abs_diffs.tsv")) - data$V1 = gsub("chr","",data$V1) - data$V1 = as.numeric(data$V1) - data_2 = select(data,-c(seq(4,8))) + data_2 = select(data,-seq(4,8)) + base = data.frame(base=rep(1,nrow(data_2))) + fwrite(base,paste0("${output}","/base_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) for (j in seq(4,ncol(data_2))){ data_3 = select(data_2,c(1,2,3,j)) - data_3$CM = 0 - colnames(data_3) = c("CHR","BP","SNP",paste0("feat_",j),"CM") + colnames(data_3) = c("CHR","BP","SNP",paste0("feat_",j)) + data_3 = setorder(data_3,BP) + data_3 = select(data_3,-c("CHR","BP","SNP")) fwrite(data_3,paste0("${output}","/feat_",j,"_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) } } @@ -105,21 +105,52 @@ parameter: output = str bash: python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} -#Calculate LD Scores -#**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.** [calc_ld_score] -#Path to bim file -parameter: bim = str -#Path to annotation File. Make sure to remove the SNP, CHR, and BP columns from the annotation file if present before running. -parameter: annot_file = str -#name of output file -parameter: output = str -#path to Hapmap3 SNPs file, remove the A1 and A2 columns for the Calculate LD Scores program -parameter: snplist = "w_hm3.snplist" +#Path to directory with bim files +parameter: bim = path() +#Path to directory with annotation files, output will appear here too. Make sure to remove the SNP, CHR, and BP columns from the annotation files if present before running. +parameter: annot_files = path() +#number of features +parameter: num_features = int bash: - python2 ldsc.py --bfile {bim} --l2 --ld-wind-cm 1 --annot {annot_file} --thin-annot --out {output} --print-snps {snplist} + #echo {annot_files} > out.txt + for i in $(seq 1 {num_features});do for j in {1..22}; do python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile {bim}/1000G.EUR.QC.${j} --l2 --ld-wind-cm 1 --annot {annot_files}/feat_${i}_chr_${j}.annot.gz --thin-annot --out {annot_files}/feat_${i}_chr_${j} --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt; done; done + for j in {1..22}; do python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile {bim}/1000G.EUR.QC.${j} --l2 --ld-wind-cm 1 --annot {annot_files}/base_chr_${j}.annot.gz --thin-annot --out {annot_files}/base_chr_${j} --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt; done + +[convert_ld_snps] + +#Path to directory with ld score files AND annotation files +parameter: ld_scores = str + +parameter: num_features = int + + +R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" + library(tidyverse) + #library(R.utils) + library(data.table) + for (i in seq(1,22)){ + data = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".l2.ldscore.gz"))) + data_2 = fread(paste0("${ld_scores}/base_chr_",i,".l2.M_5_50")) + data_3 = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".annot.gz"))) + data$SNP = paste0(data$CHR,":",data$BP) + fwrite(data,paste0("${ld_scores}/AD_base_chr_",i,".l2.ldscore.gz"),quote=F,sep="\t",row.names=F,col.names=T) + fwrite(data_2,paste0("${ld_scores}/AD_base_chr_",i,".l2.M_5_50"),quote=F,sep="\t",row.names=F,col.names=F) + fwrite(data_3,paste0("${ld_scores}/AD_base_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) + for (j in seq(1,${num_features})){ + data = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.ldscore.gz"))) + data_2 = fread(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.M_5_50")) + data_3 = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".annot.gz"))) + data$SNP = paste0(data$CHR,":",data$BP) + fwrite(data,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".l2.ldscore.gz"),quote=F,sep="\t",row.names=F,col.names=T) + fwrite(data_2,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".l2.M_5_50"),quote=F,sep="\t",row.names=F,col.names=F) + fwrite(data_3,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) + } + } + + [calc_enrichment] From 0581278daf3d4b5bfa9660877ce81233b0f464eb Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Tue, 1 Mar 2022 12:28:24 -0500 Subject: [PATCH 13/63] updates to liftover --- GWAS/LMM.ipynb | 5 ++++- GWAS/data/mtag_template.yml | 15 +++++++-------- GWAS/liftover.ipynb | 10 +++++----- 3 files changed, 16 insertions(+), 14 deletions(-) diff --git a/GWAS/LMM.ipynb b/GWAS/LMM.ipynb index 4d6e6aa..506de68 100644 --- a/GWAS/LMM.ipynb +++ b/GWAS/LMM.ipynb @@ -1051,6 +1051,8 @@ "parameter: trait = 'bt'\n", "# in the case of bgen data from UKBB ref_first should be set to true\n", "parameter: ref_first= False\n", + "# Specify dominant or recessive test. Default is additiveß\n", + "parameter: test = ''\n", "input: genoFile, group_by = 1, group_with = dict(info=[(path(f'{cwd}/{phenoFile:bn}_' + \"_\".join([x for x in phenoCol]) + '.regenie_pred.list'))] * len(genoFile))\n", "input_options = f\"--bgen {_input} --sample {sampleFile}\" if _input.suffix == \".bgen\" else f\"--bed {_input:n}\"\n", "output: [f'{cwd}/cache/{_input:bn}_'+ str(phenoCol[i]) + '.regenie.gz' for i in range(len(phenoCol))]\n", @@ -1070,6 +1072,7 @@ " --bsize ${bsize} \\\n", " --minMAC ${minMAC} \\\n", " --minINFO ${bgenMinINFO}\\\n", + " ${('--test ' + test) if test in ['dominant','recessive','additive'] else ''} \\\n", " --threads ${numThreads} \\\n", " --out ${cwd}/cache/${_input:bn} && \\\n", " gzip -f --best ${_output:n}" @@ -2398,7 +2401,7 @@ "displayed": true, "height": 0 }, - "version": "0.22.9" + "version": "0.22.6" }, "toc-showcode": false }, diff --git a/GWAS/data/mtag_template.yml b/GWAS/data/mtag_template.yml index 83837ce..6c8fcd4 100644 --- a/GWAS/data/mtag_template.yml +++ b/GWAS/data/mtag_template.yml @@ -1,11 +1,10 @@ # mtag summary statistics template -snpid: ID -chr: CHROM -bpos: GENPOS -a1: ALLELE1 #A1 needs to be the effect allele -a2: ALLELE0 # The other allele -freq: A1FREQ +chr: CHR +bpos: POS +a2: A0 # The other allele +a1: A1 #A1 needs to be the effect allele +freq: freq beta: BETA se: SE -pval: LOG10P -n: N +pval: P +n: n diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index e82c88c..8757b14 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -155,7 +155,7 @@ "# Input file which can be plink format, gvcf/vcf format, sumstat format.\n", "parameter: input_file = path\n", "# The path of yaml file with input file format, only for sumstat file.\n", - "parameter: yml_file = path #Fixme setting defualt to `None`\n", + "parameter: yml_file = path('.') \n", "# the name of ouput file which will be saved under cwd path\n", "parameter: output_file = path\n", "# From reference genome, defaut is hg19\n", @@ -253,11 +253,11 @@ " \n", " input_path=${_input[0]:r}\n", " output_path=${_output[0]:r}\n", - " fr = f'${fr}'\n", - " to = f'${to}'\n", + " fr = '${fr}'\n", + " to = '${to}'\n", " remove_missing=${remove_missing}\n", " rename = ${rename}\n", - " yml_file = f'${yml_file}'\n", + " yml_file = '${yml_file}'\n", " print(fr,to,remove_missing)\n", " liftover(input_path,output_path,yml_file,fr,to,remove_missing,rename)" ] @@ -294,7 +294,7 @@ "sos" ] ], - "version": "0.22.7" + "version": "0.22.6" } }, "nbformat": 4, From 2e6a75da79b6defdef6ec28412e63dcc05701b00 Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Wed, 2 Mar 2022 08:30:14 -0500 Subject: [PATCH 14/63] Finished Minimal Example Code and Added yml file to run it --- .../LDSC_DeepSea_Minimal_Example.ipynb | 224 ++++++++++++++---- .../LDSC_DeepSea_Minimal_Example.sos | 85 ++++--- LDSC/Deep_Learning/all_neuron_tutorial.yml | 50 ++++ 3 files changed, 277 insertions(+), 82 deletions(-) create mode 100644 LDSC/Deep_Learning/all_neuron_tutorial.yml diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb index d2a9d44..e1f2147 100644 --- a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb @@ -6,16 +6,25 @@ "kernel": "SoS" }, "source": [ - "## SoS Workflow:\n", + "## Tutorial Workflow for LDSC with DeepSea Integration:\n", "\n", - "This is the options and the SoS code to run the LDSC pipeline using your own data. \n", + "This is the code to run the minimal working example for LDSC with DeepSea Integration. The code will train the deepsea model on the set of features provided using the .yml file on the google drive folder, get predictions on the reference genome from the trained model, and run LDSC on the resulting predictions to output enrichments. \n", "\n", - "## Command Interface:" + "If you would like to use a different set of features or change training parameters, please edit the .yml file provided and everything else will still work.\n", + "\n", + "This is the command to run the Minimal Working Example:\n", + "\n", + "\n", + "sos run LDSC_DeepSea_Code.ipynb --model /mnt/mfs/statgen/Anmol/training_files/tutorial/training_outputs/model --feature_list /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt --output_tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --annot_files /mnt/mfs/statgen/Anmol/training_files/tutorial/annot_files --sumst /mnt/mfs/statgen/Anmol/polyfun/Dey/PGCALZ2sumstatsExcluding23andMe.txt --output_sumst /mnt/mfs/statgen/Anmol/polyfun/Dey/2021.Updated.sumstats.gz --signed True --bim /mnt/mfs/statgen/Anmol/training_files/tutorial/plink_files --num_features 7 --ld_scores /mnt/mfs/statgen/Anmol/training_files/tutorial/annot_files --ctrl_sumstats /mnt/mfs/statgen/Anmol/polyfun/Dey/AMD.sumstats.gz --AD_sumstats /mnt/mfs/statgen/Anmol/polyfun/Dey/2021.Updated.sumstats.gz --w_ld_ctrl /mnt/mfs/statgen/Anmol/training_files/testing/ldsc/AD_Variants/tutorial_data/weights_hm3_no_hla/weights. --frq_file_ctrl /mnt/mfs/statgen/Anmol/training_files/testing/ldsc/AD_Variants/frq/1000G.EUR.QC. --w_ld_AD /mnt/mfs/statgen/Anmol/training_files/testing/ldsc/AD_Variants/tutorial_data/weights_hm3_no_hla/weights.2021. --frq_file_AD /mnt/mfs/statgen/Anmol/training_files/testing/ldsc/AD_Variants/frq/1000G.2021.EUR.QC. --ref_ld annot_files --pheno AMD\n", + "\n", + "## Command Interface:\n", + "\n", + "This is the list of commands and workflows with explanations for each one" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": { "kernel": "SoS" }, @@ -24,21 +33,116 @@ "name": "stdout", "output_type": "stream", "text": [ - "No help information is available for script run: Failed to locate LDSC_DeepSea_Code.ipynb.sos\r\n" + "usage: sos run LDSC_DeepSea_Minimal_Example.ipynb\n", + " [workflow_name | -t targets] [options] [workflow_options]\n", + " workflow_name: Single or combined workflows defined in this script\n", + " targets: One or more targets to generate\n", + " options: Single-hyphen sos parameters (see \"sos run -h\" for details)\n", + " workflow_options: Double-hyphen workflow-specific parameters\n", + "\n", + "Workflows:\n", + " train_model\n", + " make_annot\n", + " format_annot\n", + " munge_sumstats_no_sign\n", + " munge_sumstats_sign\n", + " calc_ld_score\n", + " convert_ld_snps\n", + " calc_enrichment\n", + "\n", + "Sections\n", + " train_model:\n", + " make_annot:\n", + " Workflow Options:\n", + " --feature-list VAL (as str, required)\n", + " path to feature list file\n", + " --model VAL (as str, required)\n", + " path to trained model location\n", + " --output-tsv VAL (as str, required)\n", + " path to output directory\n", + " format_annot:\n", + " Workflow Options:\n", + " --tsv . (as path)\n", + " path to tsv files directory\n", + " --annot-files . (as path)\n", + " path to output file directory\n", + " munge_sumstats_no_sign:\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output-sumst VAL (as str, required)\n", + " path to output file\n", + " --[no-]signed (default to False)\n", + " does summary statistic contain Z or Beta\n", + " munge_sumstats_sign: This option is for when the summary statistic file does\n", + " contain a signed summary statistic (Z or Beta)\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output-sumst-2 VAL (as str, required)\n", + " path to output file\n", + " --[no-]signed (default to False)\n", + " does summary statistic contain Z or Beta\n", + " calc_ld_score:\n", + " Workflow Options:\n", + " --bim . (as path)\n", + " Path to directory with bim files\n", + " --annot-files . (as path)\n", + " Path to directory with annotation files, output will\n", + " appear here too. Make sure to remove the SNP, CHR, and\n", + " BP columns from the annotation files if present before\n", + " running.\n", + " --num-features VAL (as int, required)\n", + " number of features\n", + " convert_ld_snps:\n", + " Workflow Options:\n", + " --ld-scores VAL (as str, required)\n", + " Path to directory with ld score files AND annotation\n", + " files\n", + " --num-features VAL (as int, required)\n", + " calc_enrichment:\n", + " Workflow Options:\n", + " --ctrl-sumstats VAL (as str, required)\n", + " Path to Control Summary statistics File\n", + " --AD-sumstats VAL (as str, required)\n", + " Path to AD Summary statistics File\n", + " --ref-ld VAL (as str, required)\n", + " Path to Reference LD Scores File Directory\n", + " --w-ld-ctrl VAL (as str, required)\n", + " Path to LD Weight Files for Control Sumstats (Format\n", + " like minimal working example)\n", + " --frq-file-ctrl VAL (as str, required)\n", + " path to frequency files for Control Sumstats (Format\n", + " like minimal working example)\n", + " --w-ld-AD VAL (as str, required)\n", + " Path to LD Weight Files for AD Sumstats (Format like\n", + " minimal working example)\n", + " --frq-file-AD VAL (as str, required)\n", + " path to frequency files for AD Sumstats (Format like\n", + " minimal working example)\n", + " --num-features VAL (as int, required)\n", + " Number of Features\n", + " --pheno VAL (as str, required)\n", + " Control Phenotype, For Output\n", + "\n" ] } ], "source": [ - "!sos run LDSC_DeepSea_Code.ipynb -h" + "!sos run LDSC_DeepSea_Code.ipynb -h\n" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { - "kernel": "SoS" + "kernel": "Python 3 (ipykernel)" }, - "outputs": [], "source": [] }, { @@ -83,6 +187,8 @@ }, "outputs": [], "source": [ + "# Get Predictions for Features based on Trained Model\n", + "\n", "\n", "[make_annot]\n", "\n", @@ -91,7 +197,7 @@ "#path to trained model location\n", "parameter: model = str\n", "#path to output directory\n", - "parameter: output = str\n", + "parameter: output_tsv = str\n", "\n", "\n", "python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", @@ -109,14 +215,14 @@ "\n", " model_predict = AnalyzeSequences(\n", " NonStrandSpecific(DeeperDeepSEA(1000,{num_features})),\n", - " {model},\n", + " {model}+\"/best_model.pth.tar\",\n", " sequence_length=1000,\n", " features=distinct_features,\n", " reference_sequence=Genome(\"/mnt/mfs/statgen/Anmol/training_files/male.hg19.fasta\"),\n", " use_cuda=False # update this to False if you do not have CUDA on your machine.\n", " )\n", "\n", - " for i in range(1,22):\n", + " for i in range(1,23):\n", " model_predict.variant_effect_prediction(\n", " \"/mnt/mfs/statgen/Anmol/training_files/testing/1000G_chr_\"+str(i)+\".vcf\",\n", " save_data=[\"abs_diffs\"], # only want to save the absolute diff score data\n", @@ -140,13 +246,15 @@ }, "outputs": [], "source": [ + "# Separate Annotation Files by Chromosome\n", + "\n", "\n", "[format_annot]\n", "\n", "#path to tsv files directory\n", "parameter: tsv = path()\n", "#path to output file directory\n", - "parameter: output = path()\n", + "parameter: annot_files = path()\n", "\n", "R: expand = \"${ }\", container=\"/mnt/mfs/statgen/Anmol/r-packages.sif\"\n", " library(data.table)\n", @@ -155,18 +263,18 @@ " features = colnames(data)[9:ncol(data)]\n", " features = data.frame(features)\n", " features$encoding = paste0(\"feat_\",seq(1,nrow(features)))\n", - " fwrite(features,paste0(\"${output}\",\"/feature_encoding.txt\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " fwrite(features,paste0(\"${annot_files}\",\"/feature_encoding.txt\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " for (i in seq(1,22)){\n", " data = fread(paste0(\"${tsv}\",\"/tutorial_1000G_chr_\",i,\"_abs_diffs.tsv\"))\n", " data_2 = select(data,-seq(4,8))\n", " base = data.frame(base=rep(1,nrow(data_2)))\n", - " fwrite(base,paste0(\"${output}\",\"/base_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " fwrite(base,paste0(\"${annot_files}\",\"/base_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " for (j in seq(4,ncol(data_2))){\n", " data_3 = select(data_2,c(1,2,3,j))\n", " colnames(data_3) = c(\"CHR\",\"BP\",\"SNP\",paste0(\"feat_\",j))\n", " data_3 = setorder(data_3,BP)\n", " data_3 = select(data_3,-c(\"CHR\",\"BP\",\"SNP\"))\n", - " fwrite(data_3,paste0(\"${output}\",\"/feat_\",j,\"_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " fwrite(data_3,paste0(\"${annot_files}\",\"/feat_\",j,\"_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " }\n", " }" ] @@ -188,8 +296,8 @@ }, "outputs": [], "source": [ - "#This option is for when the summary statistic file does not contain a signed summary statistic (Z or Beta). \n", - "#In this case,the program will calculate Z for you based on A1 being the risk allele\n", + "# Option when Summary Statistic File does not contain a Z or Beta Column (Signed Summary Statistic)\n", + "\n", "[munge_sumstats_no_sign]\n", "\n", "\n", @@ -199,10 +307,15 @@ "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", "parameter: alleles = \"w_hm3.snplist\"\n", "#path to output file\n", - "parameter: output = str\n", - "\n", - "bash: \n", - " python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} --a1-inc" + "parameter: output_sumst = str\n", + "#does summary statistic contain Z or Beta\n", + "parameter: signed = False\n", + "\n", + "bash: expand = '${ }'\n", + " if [${signed}==True]\n", + " then\n", + " python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst} --a1-inc\n", + " fi" ] }, { @@ -211,7 +324,7 @@ "kernel": "SoS" }, "source": [ - "## Munge Summary Statistics (Option 2: No Signed Summary Statistic):" + "## Munge Summary Statistics (Option 2: Contains Signed Summary Statistic):" ] }, { @@ -232,10 +345,15 @@ "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", "parameter: alleles = \"w_hm3.snplist\"\n", "#path to output file\n", - "parameter: output = str\n", - "\n", - "bash: \n", - " python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output}" + "parameter: output_sumst_2 = str\n", + "#does summary statistic contain Z or Beta\n", + "parameter: signed = False\n", + "\n", + "bash: expand = '${ }'\n", + " if [${signed}==False]\n", + " then\n", + " python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst_2}\n", + " fi" ] }, { @@ -267,10 +385,10 @@ "#number of features\n", "parameter: num_features = int\n", "\n", - "bash: \n", + "bash: expand = '${ }'\n", " #echo {annot_files} > out.txt\n", - " for i in $(seq 1 {num_features});do for j in {1..22}; do python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile {bim}/1000G.EUR.QC.${j} --l2 --ld-wind-cm 1 --annot {annot_files}/feat_${i}_chr_${j}.annot.gz --thin-annot --out {annot_files}/feat_${i}_chr_${j} --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt; done; done\n", - " for j in {1..22}; do python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile {bim}/1000G.EUR.QC.${j} --l2 --ld-wind-cm 1 --annot {annot_files}/base_chr_${j}.annot.gz --thin-annot --out {annot_files}/base_chr_${j} --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt; done" + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.22 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_22.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_22 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 22| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.j --l2 --ld-wind-cm 1 --annot ${annot_files}/base_chr_j.annot.gz --thin-annot --out ${annot_files}/base_chr_j --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt" ] }, { @@ -290,6 +408,8 @@ }, "outputs": [], "source": [ + "# Convert SNP format in LD Score Files to CHR:BP to match with AD Summary Statistic Format\n", + "\n", "\n", "[convert_ld_snps]\n", "\n", @@ -304,17 +424,17 @@ " #library(R.utils)\n", " library(data.table)\n", " for (i in seq(1,22)){\n", - " data = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".l2.ldscore.gz\")))\n", + " data = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".l2.ldscore.gz\")),header=T)\n", " data_2 = fread(paste0(\"${ld_scores}/base_chr_\",i,\".l2.M_5_50\"))\n", - " data_3 = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".annot.gz\")))\n", + " data_3 = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".annot.gz\")),header=T)\n", " data$SNP = paste0(data$CHR,\":\",data$BP)\n", " fwrite(data,paste0(\"${ld_scores}/AD_base_chr_\",i,\".l2.ldscore.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " fwrite(data_2,paste0(\"${ld_scores}/AD_base_chr_\",i,\".l2.M_5_50\"),quote=F,sep=\"\\t\",row.names=F,col.names=F)\n", " fwrite(data_3,paste0(\"${ld_scores}/AD_base_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " for (j in seq(1,${num_features})){\n", - " data = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\")))\n", + " data = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\")),header=T)\n", " data_2 = fread(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.M_5_50\"))\n", - " data_3 = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".annot.gz\")))\n", + " data_3 = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".annot.gz\")),header=T)\n", " data$SNP = paste0(data$CHR,\":\",data$BP)\n", " fwrite(data,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " fwrite(data_2,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".l2.M_5_50\"),quote=F,sep=\"\\t\",row.names=F,col.names=F)\n", @@ -341,23 +461,31 @@ }, "outputs": [], "source": [ - "#Calculate Enrichment Scores for Functional Annotations\n", "\n", "[calc_enrichment]\n", "\n", - "#Path to Summary statistics File\n", - "parameter: sumstats = str\n", - "#Path to Reference LD Scores Files (Base Annotation + Annotation you want to analyze, format like minimal working example)\n", + "#Path to Control Summary statistics File\n", + "parameter: ctrl_sumstats = str\n", + "#Path to AD Summary statistics File\n", + "parameter: AD_sumstats = str\n", + "#Path to Reference LD Scores File Directory \n", "parameter: ref_ld = str\n", - "#Path to LD Weight Files (Format like minimal working example)\n", - "parameter: w_ld = str\n", - "#path to frequency files (Format like minimal working example)\n", - "parameter: frq_file = str\n", - "#Output name\n", - "parameter: output = str\n", - "\n", - "bash:\n", - " python2 ldsc.py --h2 {sumstats} --ref-ld-chr {ref_ld} --w-ld-chr {w_ld} --overlap-annot --frqfile-chr {frq_file} --out {output}" + "#Path to LD Weight Files for Control Sumstats (Format like minimal working example)\n", + "parameter: w_ld_ctrl = str\n", + "#path to frequency files for Control Sumstats (Format like minimal working example)\n", + "parameter: frq_file_ctrl = str\n", + "#Path to LD Weight Files for AD Sumstats (Format like minimal working example)\n", + "parameter: w_ld_AD = str\n", + "#path to frequency files for AD Sumstats (Format like minimal working example)\n", + "parameter: frq_file_AD = str\n", + "#Number of Features\n", + "parameter: num_features = int \n", + "#Control Phenotype, For Output\n", + "parameter: pheno = str\n", + "\n", + "bash: expand = '${ }'\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${ctrl_sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld_ctrl} --overlap-annot --frqfile-chr ${frq_file_ctrl} --out ${ref_ld}/${pheno}_feat_j\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j" ] } ], diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos index d3bee84..ee3dd6f 100644 --- a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos @@ -14,7 +14,7 @@ parameter: feature_list = str #path to trained model location parameter: model = str #path to output directory -parameter: output = str +parameter: output_tsv = str python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' @@ -32,14 +32,14 @@ python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' model_predict = AnalyzeSequences( NonStrandSpecific(DeeperDeepSEA(1000,{num_features})), - {model}, + {model}+"/best_model.pth.tar", sequence_length=1000, features=distinct_features, reference_sequence=Genome("/mnt/mfs/statgen/Anmol/training_files/male.hg19.fasta"), use_cuda=False # update this to False if you do not have CUDA on your machine. ) - for i in range(1,22): + for i in range(1,23): model_predict.variant_effect_prediction( "/mnt/mfs/statgen/Anmol/training_files/testing/1000G_chr_"+str(i)+".vcf", save_data=["abs_diffs"], # only want to save the absolute diff score data @@ -50,7 +50,7 @@ python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' #path to tsv files directory parameter: tsv = path() #path to output file directory -parameter: output = path() +parameter: annot_files = path() R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" library(data.table) @@ -59,23 +59,21 @@ R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" features = colnames(data)[9:ncol(data)] features = data.frame(features) features$encoding = paste0("feat_",seq(1,nrow(features))) - fwrite(features,paste0("${output}","/feature_encoding.txt"),quote=F,sep="\t",row.names=F,col.names=T) + fwrite(features,paste0("${annot_files}","/feature_encoding.txt"),quote=F,sep="\t",row.names=F,col.names=T) for (i in seq(1,22)){ data = fread(paste0("${tsv}","/tutorial_1000G_chr_",i,"_abs_diffs.tsv")) data_2 = select(data,-seq(4,8)) base = data.frame(base=rep(1,nrow(data_2))) - fwrite(base,paste0("${output}","/base_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) + fwrite(base,paste0("${annot_files}","/base_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) for (j in seq(4,ncol(data_2))){ data_3 = select(data_2,c(1,2,3,j)) colnames(data_3) = c("CHR","BP","SNP",paste0("feat_",j)) data_3 = setorder(data_3,BP) data_3 = select(data_3,-c("CHR","BP","SNP")) - fwrite(data_3,paste0("${output}","/feat_",j,"_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) + fwrite(data_3,paste0("${annot_files}","/feat_",j,"_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) } } -#This option is for when the summary statistic file does not contain a signed summary statistic (Z or Beta). -#In this case,the program will calculate Z for you based on A1 being the risk allele [munge_sumstats_no_sign] @@ -85,10 +83,15 @@ parameter: sumst = str #path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program parameter: alleles = "w_hm3.snplist" #path to output file -parameter: output = str +parameter: output_sumst = str +#does summary statistic contain Z or Beta +parameter: signed = False -bash: - python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} --a1-inc +bash: expand = '${ }' + if [${signed}==True] + then + python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst} --a1-inc + fi # This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta) [munge_sumstats_sign] @@ -100,10 +103,15 @@ parameter: sumst = str #path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program parameter: alleles = "w_hm3.snplist" #path to output file -parameter: output = str +parameter: output_sumst_2 = str +#does summary statistic contain Z or Beta +parameter: signed = False -bash: - python2 munge_sumstats.py --sumstats {sumst} --merge-alleles {alleles} --out {output} +bash: expand = '${ }' + if [${signed}==False] + then + python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst_2} + fi [calc_ld_score] @@ -114,10 +122,10 @@ parameter: annot_files = path() #number of features parameter: num_features = int -bash: +bash: expand = '${ }' #echo {annot_files} > out.txt - for i in $(seq 1 {num_features});do for j in {1..22}; do python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile {bim}/1000G.EUR.QC.${j} --l2 --ld-wind-cm 1 --annot {annot_files}/feat_${i}_chr_${j}.annot.gz --thin-annot --out {annot_files}/feat_${i}_chr_${j} --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt; done; done - for j in {1..22}; do python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile {bim}/1000G.EUR.QC.${j} --l2 --ld-wind-cm 1 --annot {annot_files}/base_chr_${j}.annot.gz --thin-annot --out {annot_files}/base_chr_${j} --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt; done + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.22 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_22.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_22 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 22| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.j --l2 --ld-wind-cm 1 --annot ${annot_files}/base_chr_j.annot.gz --thin-annot --out ${annot_files}/base_chr_j --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt [convert_ld_snps] @@ -132,17 +140,17 @@ R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" #library(R.utils) library(data.table) for (i in seq(1,22)){ - data = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".l2.ldscore.gz"))) + data = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".l2.ldscore.gz")),header=T) data_2 = fread(paste0("${ld_scores}/base_chr_",i,".l2.M_5_50")) - data_3 = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".annot.gz"))) + data_3 = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".annot.gz")),header=T) data$SNP = paste0(data$CHR,":",data$BP) fwrite(data,paste0("${ld_scores}/AD_base_chr_",i,".l2.ldscore.gz"),quote=F,sep="\t",row.names=F,col.names=T) fwrite(data_2,paste0("${ld_scores}/AD_base_chr_",i,".l2.M_5_50"),quote=F,sep="\t",row.names=F,col.names=F) fwrite(data_3,paste0("${ld_scores}/AD_base_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) for (j in seq(1,${num_features})){ - data = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.ldscore.gz"))) + data = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.ldscore.gz")),header=T) data_2 = fread(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.M_5_50")) - data_3 = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".annot.gz"))) + data_3 = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".annot.gz")),header=T) data$SNP = paste0(data$CHR,":",data$BP) fwrite(data,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".l2.ldscore.gz"),quote=F,sep="\t",row.names=F,col.names=T) fwrite(data_2,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".l2.M_5_50"),quote=F,sep="\t",row.names=F,col.names=F) @@ -154,17 +162,26 @@ R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" [calc_enrichment] -#Path to Summary statistics File -parameter: sumstats = str -#Path to Reference LD Scores Files (Base Annotation + Annotation you want to analyze, format like minimal working example) +#Path to Control Summary statistics File +parameter: ctrl_sumstats = str +#Path to AD Summary statistics File +parameter: AD_sumstats = str +#Path to Reference LD Scores File Directory parameter: ref_ld = str -#Path to LD Weight Files (Format like minimal working example) -parameter: w_ld = str -#path to frequency files (Format like minimal working example) -parameter: frq_file = str -#Output name -parameter: output = str - -bash: - python2 ldsc.py --h2 {sumstats} --ref-ld-chr {ref_ld} --w-ld-chr {w_ld} --overlap-annot --frqfile-chr {frq_file} --out {output} +#Path to LD Weight Files for Control Sumstats (Format like minimal working example) +parameter: w_ld_ctrl = str +#path to frequency files for Control Sumstats (Format like minimal working example) +parameter: frq_file_ctrl = str +#Path to LD Weight Files for AD Sumstats (Format like minimal working example) +parameter: w_ld_AD = str +#path to frequency files for AD Sumstats (Format like minimal working example) +parameter: frq_file_AD = str +#Number of Features +parameter: num_features = int +#Control Phenotype, For Output +parameter: pheno = str + +bash: expand = '${ }' + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${ctrl_sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld_ctrl} --overlap-annot --frqfile-chr ${frq_file_ctrl} --out ${ref_ld}/${pheno}_feat_j + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j diff --git a/LDSC/Deep_Learning/all_neuron_tutorial.yml b/LDSC/Deep_Learning/all_neuron_tutorial.yml new file mode 100644 index 0000000..30ca02a --- /dev/null +++ b/LDSC/Deep_Learning/all_neuron_tutorial.yml @@ -0,0 +1,50 @@ +--- +ops: [train, evaluate] +model: { + path: /mnt/mfs/statgen/Anmol/training_files/deeperdeepsea.py,#UPDATE + class: DeeperDeepSEA, + class_args: { + sequence_length: 1000, + n_targets: 7, + }, + non_strand_specific: mean +} +sampler: !obj:selene_sdk.samplers.IntervalsSampler { + reference_sequence: !obj:selene_sdk.sequences.Genome { + input_path: /mnt/mfs/statgen/Anmol/training_files/male.hg19.fasta,#UPDATE + blacklist_regions: hg19 + }, + features: !obj:selene_sdk.utils.load_features_list { + input_path: /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt #UPDATE + }, + target_path: /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial.bed.gz, #UPDATE + intervals_path: /mnt/mfs/statgen/Anmol/training_files/DNase_Intervals_FULL.txt, #UPDATE + seed: 127, + # A positive example is an 1000bp sequence with at least 1 class/feature annotated to it. + # A negative sample has no classes/features annotated to the sequence. + sample_negative: True, + sequence_length: 1000, + center_bin_to_predict: 200, + test_holdout: 0.2, + validation_holdout: 0.3, + # The feature must take up 50% of the bin (200bp) for it to be considered + # a feature annotated to that sequence. + feature_thresholds: 0.25, + mode: train, + save_datasets: [validate, test] +} +train_model: !obj:selene_sdk.TrainModel { + batch_size: 64, + max_steps: 500, # update this value for longer training + report_stats_every_n_steps: 250, + n_validation_samples: 25000, + n_test_samples: 125000, + cpu_n_threads: 32, + use_cuda: False, # TODO: update this if CUDA is not on your machine + data_parallel: False +} +random_seed: 1447 +output_dir: ./tutorial/training_outputs/model #UPDATE +create_subdirectory: False +load_test_set: False +... \ No newline at end of file From c840df1b73bff8e241b5599de3af8b1c96388c4a Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Wed, 2 Mar 2022 17:08:06 -0500 Subject: [PATCH 15/63] change walltime and memory to be global variables easily modifiable --- GWAS/LMM.ipynb | 42 ++++++++++++++++++++++-------------------- 1 file changed, 22 insertions(+), 20 deletions(-) diff --git a/GWAS/LMM.ipynb b/GWAS/LMM.ipynb index 506de68..1790ad4 100644 --- a/GWAS/LMM.ipynb +++ b/GWAS/LMM.ipynb @@ -356,6 +356,8 @@ "parameter: bgenMinINFO = 0.8\n", "# For cluster jobs, number commands to run per job\n", "parameter: job_size = 1\n", + "parameter: mem = '15G'\n", + "parameter: walltime = '10h'\n", "# The container with the lmm software. Can be either a dockerhub image or a singularity `sif` file.\n", "# Default is set to using dockerhub image\n", "parameter: container_lmm = 'statisticalgenetics/lmm:2.4'\n", @@ -703,7 +705,7 @@ "input: genoFile, group_by = 1\n", "output: f'{cwd}/cache/{_input:bn}.{phenoFile:bn}_{phenoCol[0]}.boltlmm.snp_stats.gz'\n", "file_options=f\"--bfile {bfile:n} --bgenFile={_input} --bgenMinMAF={bgenMinMAF} --bgenMinINFO={bgenMinINFO} --sampleFile={sampleFile} --statsFileBgenSnps={_output} --statsFile={_output:nn}.ref_stats.gz \" if _input.suffix == \".bgen\" else f\"--bfile={_input:n} --statsFile={_output} \"\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '48h', mem = '60G', cores = numThreads, tags = f'{step_name}_{_output:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output:bn}'\n", "bash: container=container_lmm, expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', volumes = [f\"{cwd:a}:{cwd:a}\"]\n", " bolt \\\n", " --phenoFile=${phenoFile} \\\n", @@ -772,7 +774,7 @@ "output: f'{cwd}/cache/{_input:bn}.part_{_part_number}.grm.bin', \n", " f'{cwd}/cache/{_input:bn}.part_{_part_number}.grm.N.bin', \n", " f'{cwd}/cache/{_input:bn}.part_{_part_number}.grm.id'\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '48h', mem = '48G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "bash: container=container_lmm, expand = \"${ }\", stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout'\n", " gcta64 \\\n", " --bfile ${_input[0]:n} \\\n", @@ -804,7 +806,7 @@ "output: f'{cwd}/{bfile:bn}.grm.bin', \n", " f'{cwd}/{bfile:bn}.grm.N.bin', \n", " f'{cwd}/{bfile:bn}.grm.id' \n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '2h', mem = '6G', cores = 1, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = 1, tags = f'{step_name}_{_output[0]:bn}'\n", "bash: container=container_lmm, expand = \"${ }\", stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout'\n", " # here input is results all parts each having 3 items. We need to get the corresponding every other 3 items\n", " cat ${paths(_input[::3])} > ${_output[0]}\n", @@ -833,7 +835,7 @@ "# Make a sparse GRM from the merged full-dense GRM\n", "[gcta_3]\n", "output: f'{cwd}/{bfile:bn}.grm.sp' \n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '48h', mem = '48G', cores = 1, tags = f'{step_name}_{_output:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = 1, tags = f'{step_name}_{_output:bn}'\n", "bash: container=container_lmm, expand = \"${ }\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n", " gcta64 --grm ${_output:nn} --make-bK-sparse 0.05 --out ${_output:nn}" ] @@ -874,7 +876,7 @@ "input_options = f\"--bgen {_input} --info {bgenMinINFO} --sample {sampleFile}\" if _input.suffix == \".bgen\" else f\"--bfile {_input:n}\"\n", "output: f'{cwd}/cache/{_input:bnn}.{phenoFile:bn}.fastGWA.gz'\n", "fail_if(not path(f'{_input}.bgi').is_file() and _input.suffix == '.bgen', msg = f'Cannot find file ``{_input}.bgi``. Please generate it using command ``bgenix -g {_input} -index``.') if _input.suffix == \".bgen\" else f\"continue\"\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '48h', mem = '5G', cores = numThreads, tags = f'{step_name}_{_output:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output:bn}'\n", "bash: container=container_lmm, expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout'\n", " gcta64 \\\n", " ${input_options} \\\n", @@ -954,7 +956,7 @@ "parameter: mind_filter = 0.0\n", "input: bfile\n", "output: f'{cwd}/cache/{bfile:bn}.qc_pass.id', f'{cwd}/cache/{bfile:bn}.qc_pass.snplist' \n", - "task: trunk_workers = 1, walltime = '10h', mem = '30G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "bash: container=container_lmm, expand= \"${ }\", stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout' \n", " plink2 \\\n", " --bfile ${bfile:n} --mac 1 \\\n", @@ -1009,7 +1011,7 @@ "depends: f'{cwd}/cache/{bfile:bn}.qc_pass.snplist', f'{cwd}/cache/{bfile:bn}.qc_pass.id'\n", "input: geno = bfile, pheno = f\"{cwd}/{phenoFile:bn}.regenie_phenotype\", covar = f\"{cwd}/{phenoFile:bn}.regenie_covar\", qc = output_from(\"regenie_qc\")\n", "output: f'{cwd}/{phenoFile:bn}_' + \"_\".join([x for x in phenoCol]) + '.regenie_pred.list'\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '12h', mem = '15G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "bash: container=container_lmm, expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', volumes = [f\"{lowmem_dir:a}:{lowmem_dir:a}\"]\n", " regenie \\\n", " --step 1 \\\n", @@ -1052,11 +1054,11 @@ "# in the case of bgen data from UKBB ref_first should be set to true\n", "parameter: ref_first= False\n", "# Specify dominant or recessive test. Default is additiveß\n", - "parameter: test = ''\n", + "parameter: test = 'additive'\n", "input: genoFile, group_by = 1, group_with = dict(info=[(path(f'{cwd}/{phenoFile:bn}_' + \"_\".join([x for x in phenoCol]) + '.regenie_pred.list'))] * len(genoFile))\n", "input_options = f\"--bgen {_input} --sample {sampleFile}\" if _input.suffix == \".bgen\" else f\"--bed {_input:n}\"\n", "output: [f'{cwd}/cache/{_input:bn}_'+ str(phenoCol[i]) + '.regenie.gz' for i in range(len(phenoCol))]\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '12h', mem = '15G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "bash:container=container_lmm, expand = \"${ }\", stderr = f'{cwd}/cache/{_input:bn}.stderr', stdout = f'{cwd}/cache/{_input:bn}.stdout', volumes = [f\"{cwd:a}:{cwd:a}\"]\n", " set -e\n", " regenie \\\n", @@ -1071,8 +1073,8 @@ " --pred ${_input.info} \\\n", " --bsize ${bsize} \\\n", " --minMAC ${minMAC} \\\n", - " --minINFO ${bgenMinINFO}\\\n", - " ${('--test ' + test) if test in ['dominant','recessive','additive'] else ''} \\\n", + " --minINFO ${bgenMinINFO} \\\n", + " ${('--test ' + test) if test in ['dominant','recessive','additive'] else ''} \\\n", " --threads ${numThreads} \\\n", " --out ${cwd}/cache/${_input:bn} && \\\n", " gzip -f --best ${_output:n}" @@ -1120,7 +1122,7 @@ "input: genoFile, group_by = 1, group_with = dict(info=[(path(f'{cwd}/{phenoFile:bn}_' + \"_\".join([x for x in phenoCol]) + '.regenie_pred.list'))] * len(genoFile))\n", "input_options = f\"--bgen {_input} --sample {sampleFile}\" if _input.suffix == \".bgen\" else f\"--bed {_input:n}\"\n", "output: [f'{cwd}/cache/{_input:bn}_burden_'+ str(phenoCol[i]) + '.regenie.gz' for i in range(len(phenoCol))]\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '48h', mem = '15G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "bash:container=container_lmm, expand = \"${ }\", stderr = f'{cwd}/cache/{_input:bn}.stderr', stdout = f'{cwd}/cache/{_input:bn}.log', volumes = [f\"{cwd:a}:{cwd:a}\"]\n", " set -e\n", " regenie \\\n", @@ -1166,7 +1168,7 @@ "f = open(mask_file, \"r\")\n", "masks = [i.split(\" \")[0] for i in f.readlines()]\n", "output: [f'{cwd}/cache/{_input:bn}_'+ str(phenoCol[i]) + \"_\" + str(masks[j]) + \".\" + str(aaf_bins[k]) + '.regenie.gz' for i in range(len(phenoCol)) for j in range(len(masks)) for k in range(len(aaf_bins))] \n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '12h', mem = '15G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "python: container=container_lmm, expand = \"${ }\", stderr = f'{cwd}/cache/{_input:bn}.stderr', stdout = f'{cwd}/cache/{_input:bn}.stdout', volumes = [f\"{cwd:a}:{cwd:a}\"]\n", " import pandas as pd \n", "\n", @@ -1228,7 +1230,7 @@ "parameter: invNormalize = 'FALSE'\n", "input: bfile, phenoFile\n", "output: f'{cwd}/{bfile:bn}.{phenoFile:bn}.SAIGE.rda', f'{cwd}/{bfile:bn}.{phenoFile:bn}.SAIGE.varianceRatio.txt'\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '48h', mem = '60G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "bash: expand = \"${ }\", stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout', template_name='conda', env_name='RSAIGE'\n", " Rscript ${script_path} \\\n", " --plinkFile=${_input[0]:n} \\\n", @@ -1278,7 +1280,7 @@ "input_options = f\"--bgenFile={_genoFile} --bgenFileIndex=${_genoFile}.bgi --sampleFile=${cwd}/{sampleFile:bn}.SAIGE_sample --minInfo=${bgenMinINFO}\" if _input.suffix == \".bgen\" else f\"--plinkFile={_input:n}\"\n", "output: f'{cwd}/cache/{_genoFile:bn}.{phenoFile:bn}.SAIGE.gz'\n", "fail_if(not path(f'{_genoFile}.bgi').is_file() and _input.suffix == '.bgen', msg = f'Cannot find file ``{_genoFile}.bgi``. Please generate it using command ``bgenix -g {_genoFile} -index``.')\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '48h', mem = '60G', tags = f'{step_name}_{_output:bn}'\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, tags = f'{step_name}_{_output:bn}'\n", "bash: expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout', template_name='conda', env_name='RSAIGE'\n", " Rscript ${script_path} \\\n", " ${input_options}\\\n", @@ -1589,7 +1591,7 @@ "input: group_by = lambda x: [x[i::len(phenoCol)] for i in range(len(phenoCol))], group_with='phenoCol'\n", "output: f'{cwd}/{phenoFile:bn}_{_phenoCol}.{step_name.rsplit(\"_\",1)[0]}.snp_stats.gz',\n", " f'{cwd}/{phenoFile:bn}_{_phenoCol}.{step_name.rsplit(\"_\",1)[0]}.snp_counts.txt'\n", - "task: trunk_workers = 1, trunk_size = 1, walltime = '1h', mem = '36G', cores = 1, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = 1, walltime = walltime, mem = mem, cores = 1, tags = f'{step_name}_{_output[0]:bn}'\n", "python: container=container_lmm, expand ='${ }'\n", " import gzip\n", " import pandas as pd\n", @@ -1647,7 +1649,7 @@ " f'{cwd}/cache/nonsin.genelist',\n", " f'{cwd}/cache/nondup.snplist',\n", " f'{cwd}/cache/someannoslim.csv'\n", - "task: trunk_workers = 1, walltime = '10h', mem = '60G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "\n", "# Extract target fields (CADD and GWAS catelog) from snpannofile to a smaller file someanno.txt\n", "bash:container=container_lmm, expand = \"${ }\", stderr = f'{cwd}/{step_name}.stderr', stdout = f'{cwd}/{step_name}.stdout'\n", @@ -1731,7 +1733,7 @@ "bins=[str(phenoCol[i]) + f'.{step_name.rsplit(\"_\",1)[0]}.' + str(masks[j]) + \".\" + str(aaf_bins[k]) for i in range(len(phenoCol)) for j in range(len(masks)) for k in range(len(aaf_bins))]\n", "input: [f'{cwd}/{phenoFile:bn}_' + str(phenoCol[i]) +f'.{step_name.rsplit(\"_\",1)[0]}.snp_stats.gz' for i in range(len(phenoCol))]+[f'{cwd}/{phenoFile:bn}_' + str(phenoCol[i]) +f'.{step_name.rsplit(\"_\",1)[0]}.snp_counts.txt' for i in range(len(phenoCol))],group_by = lambda x: [x[i::len(phenoCol)] for i in range(len(phenoCol))], group_with='phenoCol'\n", "output: [f'{cwd}/{phenoFile:bn}_' + bins[n] + '.snp_stats.gz' for n in range(len(bins))]+[f'{cwd}/{phenoFile:bn}_' + bins[n] + '.snp_counts.txt' for n in range(len(bins))]+[f'{cwd}/{phenoFile:bn}_' + bins[n] + '.remove_sin.snp_stats.gz' for n in range(len(bins))]\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '3h', mem = '64G', tags = f'{step_name}_{_input[0]:bn}' \n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, tags = f'{step_name}_{_input[0]:bn}' \n", "python: container=container_lmm, expand = \"${ }\", stderr = f'{cwd}/{step_name}.stderr', stdout = f'{cwd}/{step_name}.stdout'\n", " import gzip\n", " import pandas as pd\n", @@ -1811,7 +1813,7 @@ " annotated_manhattan = f'{_input[0]:nn}.manhattan_annotated.png',\n", " analysis_summary = f'{_input[0]:nn}.analysis_summary.md',\n", " plot_data = f'{_input[0]:nn}.plot_data.rds'\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '3h', mem = '64G', tags = f'{step_name}_{_output[0]:bn}' \n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, tags = f'{step_name}_{_output[0]:bn}' \n", "bash: container=container_lmm, expand = \"${ }\"\n", " echo '''---\n", " theme: base-theme\n", @@ -2055,7 +2057,7 @@ " annotated_manhattan = f'{_input[0]:nnn}.manhattan_annotated.png',\n", " analysis_summary = f'{_input[0]:nnn}.analysis_summary.md',\n", " plot_data = f'{_input[0]:nnn}.plot_data.rds'\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '3h', mem = '64G', tags = f'{step_name}_{_output[0]:bn}' \n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, tags = f'{step_name}_{_output[0]:bn}' \n", "bash: container=container_lmm, expand = \"${ }\"\n", " echo '''---\n", " theme: base-theme\n", From 54a2d42e0baeb7474ed286db15c7bfe64a634bc5 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Wed, 2 Mar 2022 17:08:48 -0500 Subject: [PATCH 16/63] changes to parameters for perfect_genecov --- GWAS/MTAG.ipynb | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/GWAS/MTAG.ipynb b/GWAS/MTAG.ipynb index 52e747c..5a148c0 100644 --- a/GWAS/MTAG.ipynb +++ b/GWAS/MTAG.ipynb @@ -159,7 +159,7 @@ "## MWE\n", "```\n", "sumstatsFiles=`echo ~/output/*hg19.snp_stats_original_columns.gz`\n", - "sos dryrun ~/project/UKBB_GWAS_dev/workflow/MTAG.ipynb mtag \\\n", + "sos run ~/project/UKBB_GWAS_dev/workflow/MTAG.ipynb mtag \\\n", "--cwd ~/output \\\n", "--sumstatsFiles $sumstatsFiles \\\n", "--formatFile ~/project/bioworkflows/GWAS/data/mtag_template.yml \\\n", @@ -213,7 +213,7 @@ "# If there's no overlap between samples\n", "parameter: no_overlap = False\n", "# If the traits are perfectly correlated\n", - "parameter: perfect_gencov = False\n", + "parameter: perfect_gencov = True\n", "# Assume equal heritability of traits\n", "parameter: h2_equal = False\n", "# Reference Ld used by ldsc.py needs to be splitted by chromosome\n", @@ -302,9 +302,9 @@ "[mtag_2]\n", "parameter: job_name=''\n", "input: group_by='all'\n", - "output: f'{cwd}/{job_name}_sigma_hat.mtag.txt',\n", - " f'{cwd}/{job_name}_omega_hat.mtag.txt',\n", - " [f'{cwd}/{job_name}.trait_{x}.mtag.txt' for x in range(len(sumstatsFiles))]\n", + "output: f'{cwd}/{job_name}_sigma_hat.txt',\n", + " f'{cwd}/{job_name}_omega_hat.txt',\n", + " [f'{cwd}/{job_name}.trait_{x}.txt' for x in range(len(sumstatsFiles))]\n", "task: trunk_workers = 1, trunk_size = job_size, walltime = '10h', mem = '30G', cores = numThreads, tags = f'{step_name}_{_output[1]:bn}'\n", "bash: expand = \"${ }\", stderr = f'{_output[1]:n}.stderr', stdout = f'{_output[1]:n}.log'\n", "\n", @@ -321,9 +321,9 @@ " --out ${cwd}/${job_name} \\\n", " --n_min 0.0 \\\n", " ${('--ld_ref_panel ' + ld_ref_panel + '/') } \\\n", - " ${('--' + no_overlap ) if no_overlap is True else ''} \\\n", - " ${('--' + perfect_gencov) if perfect_gencov is True else ''} \\\n", - " ${('--' + h2_equal) if h2_equal is True else ''} \\\n", + " ${('--no_overlap') if no_overlap else ''} \\\n", + " ${('--perfect_gencov') if perfect_gencov else ''} \\\n", + " ${('--h2_equal') if h2_equal else ''} \\\n", " --force" ] }, From 6914175b5cce7db7a44cc17a276f2eaf15124357 Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Wed, 2 Mar 2022 21:31:26 -0500 Subject: [PATCH 17/63] Add files via upload --- .../LDSC_DeepSea_Minimal_Example.ipynb | 508 +++++++----------- .../LDSC_DeepSea_Minimal_Example.sos | 133 +---- 2 files changed, 196 insertions(+), 445 deletions(-) diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb index e1f2147..1e39798 100644 --- a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb @@ -6,25 +6,20 @@ "kernel": "SoS" }, "source": [ - "## Tutorial Workflow for LDSC with DeepSea Integration:\n", + "## DeepSea Pipeline:\n", "\n", "This is the code to run the minimal working example for LDSC with DeepSea Integration. The code will train the deepsea model on the set of features provided using the .yml file on the google drive folder, get predictions on the reference genome from the trained model, and run LDSC on the resulting predictions to output enrichments. \n", "\n", "If you would like to use a different set of features or change training parameters, please edit the .yml file provided and everything else will still work.\n", "\n", - "This is the command to run the Minimal Working Example:\n", - "\n", - "\n", - "sos run LDSC_DeepSea_Code.ipynb --model /mnt/mfs/statgen/Anmol/training_files/tutorial/training_outputs/model --feature_list /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt --output_tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --annot_files /mnt/mfs/statgen/Anmol/training_files/tutorial/annot_files --sumst /mnt/mfs/statgen/Anmol/polyfun/Dey/PGCALZ2sumstatsExcluding23andMe.txt --output_sumst /mnt/mfs/statgen/Anmol/polyfun/Dey/2021.Updated.sumstats.gz --signed True --bim /mnt/mfs/statgen/Anmol/training_files/tutorial/plink_files --num_features 7 --ld_scores /mnt/mfs/statgen/Anmol/training_files/tutorial/annot_files --ctrl_sumstats /mnt/mfs/statgen/Anmol/polyfun/Dey/AMD.sumstats.gz --AD_sumstats /mnt/mfs/statgen/Anmol/polyfun/Dey/2021.Updated.sumstats.gz --w_ld_ctrl /mnt/mfs/statgen/Anmol/training_files/testing/ldsc/AD_Variants/tutorial_data/weights_hm3_no_hla/weights. --frq_file_ctrl /mnt/mfs/statgen/Anmol/training_files/testing/ldsc/AD_Variants/frq/1000G.EUR.QC. --w_ld_AD /mnt/mfs/statgen/Anmol/training_files/testing/ldsc/AD_Variants/tutorial_data/weights_hm3_no_hla/weights.2021. --frq_file_AD /mnt/mfs/statgen/Anmol/training_files/testing/ldsc/AD_Variants/frq/1000G.2021.EUR.QC. --ref_ld annot_files --pheno AMD\n", - "\n", "## Command Interface:\n", "\n", - "This is the list of commands and workflows with explanations for each one" + "This is the list of commands and workflows with explanations for each one, detailed information for each step will be presented below." ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": { "kernel": "SoS" }, @@ -33,104 +28,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "usage: sos run LDSC_DeepSea_Minimal_Example.ipynb\n", - " [workflow_name | -t targets] [options] [workflow_options]\n", - " workflow_name: Single or combined workflows defined in this script\n", - " targets: One or more targets to generate\n", - " options: Single-hyphen sos parameters (see \"sos run -h\" for details)\n", - " workflow_options: Double-hyphen workflow-specific parameters\n", - "\n", - "Workflows:\n", - " train_model\n", - " make_annot\n", - " format_annot\n", - " munge_sumstats_no_sign\n", - " munge_sumstats_sign\n", - " calc_ld_score\n", - " convert_ld_snps\n", - " calc_enrichment\n", - "\n", - "Sections\n", - " train_model:\n", - " make_annot:\n", - " Workflow Options:\n", - " --feature-list VAL (as str, required)\n", - " path to feature list file\n", - " --model VAL (as str, required)\n", - " path to trained model location\n", - " --output-tsv VAL (as str, required)\n", - " path to output directory\n", - " format_annot:\n", - " Workflow Options:\n", - " --tsv . (as path)\n", - " path to tsv files directory\n", - " --annot-files . (as path)\n", - " path to output file directory\n", - " munge_sumstats_no_sign:\n", - " Workflow Options:\n", - " --sumst VAL (as str, required)\n", - " path to summary statistic file\n", - " --alleles 'w_hm3.snplist'\n", - " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", - " and A2) for the munge_sumstats program\n", - " --output-sumst VAL (as str, required)\n", - " path to output file\n", - " --[no-]signed (default to False)\n", - " does summary statistic contain Z or Beta\n", - " munge_sumstats_sign: This option is for when the summary statistic file does\n", - " contain a signed summary statistic (Z or Beta)\n", - " Workflow Options:\n", - " --sumst VAL (as str, required)\n", - " path to summary statistic file\n", - " --alleles 'w_hm3.snplist'\n", - " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", - " and A2) for the munge_sumstats program\n", - " --output-sumst-2 VAL (as str, required)\n", - " path to output file\n", - " --[no-]signed (default to False)\n", - " does summary statistic contain Z or Beta\n", - " calc_ld_score:\n", - " Workflow Options:\n", - " --bim . (as path)\n", - " Path to directory with bim files\n", - " --annot-files . (as path)\n", - " Path to directory with annotation files, output will\n", - " appear here too. Make sure to remove the SNP, CHR, and\n", - " BP columns from the annotation files if present before\n", - " running.\n", - " --num-features VAL (as int, required)\n", - " number of features\n", - " convert_ld_snps:\n", - " Workflow Options:\n", - " --ld-scores VAL (as str, required)\n", - " Path to directory with ld score files AND annotation\n", - " files\n", - " --num-features VAL (as int, required)\n", - " calc_enrichment:\n", - " Workflow Options:\n", - " --ctrl-sumstats VAL (as str, required)\n", - " Path to Control Summary statistics File\n", - " --AD-sumstats VAL (as str, required)\n", - " Path to AD Summary statistics File\n", - " --ref-ld VAL (as str, required)\n", - " Path to Reference LD Scores File Directory\n", - " --w-ld-ctrl VAL (as str, required)\n", - " Path to LD Weight Files for Control Sumstats (Format\n", - " like minimal working example)\n", - " --frq-file-ctrl VAL (as str, required)\n", - " path to frequency files for Control Sumstats (Format\n", - " like minimal working example)\n", - " --w-ld-AD VAL (as str, required)\n", - " Path to LD Weight Files for AD Sumstats (Format like\n", - " minimal working example)\n", - " --frq-file-AD VAL (as str, required)\n", - " path to frequency files for AD Sumstats (Format like\n", - " minimal working example)\n", - " --num-features VAL (as int, required)\n", - " Number of Features\n", - " --pheno VAL (as str, required)\n", - " Control Phenotype, For Output\n", - "\n" + "No help information is available for script run: Failed to locate LDSC_DeepSea_Code.ipynb.sos\r\n" ] } ], @@ -143,7 +41,172 @@ "metadata": { "kernel": "Python 3 (ipykernel)" }, - "source": [] + "source": [ + "## Background:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Training Model: \n", + "\n", + "**Workflow Command to train model for Minimal Example:** `sos run LDSC_DeepSea_Code.ipynb train_model`\n", + "\n", + "To train the model using different data you can use the .yml file provided to change the training parameters and files. An example of how this file looks for the minimal working example is shown below." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "kernel": "SoS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---\n", + "ops: [train, evaluate]\n", + "model: {\n", + " path: /mnt/mfs/statgen/Anmol/training_files/deeperdeepsea.py,#UPDATE\n", + " class: DeeperDeepSEA,\n", + " class_args: {\n", + " sequence_length: 1000,\n", + " n_targets: 7,\n", + " },\n", + " non_strand_specific: mean\n", + "}\n", + "sampler: !obj:selene_sdk.samplers.IntervalsSampler {\n", + " reference_sequence: !obj:selene_sdk.sequences.Genome {\n", + " input_path: /mnt/mfs/statgen/Anmol/training_files/male.hg19.fasta,#UPDATE\n", + " blacklist_regions: hg19\n", + " },\n", + " features: !obj:selene_sdk.utils.load_features_list {\n", + " input_path: /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt #UPDATE\n", + " },\n", + " target_path: /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial.bed.gz, #UPDATE\n", + " intervals_path: /mnt/mfs/statgen/Anmol/training_files/DNase_Intervals_FULL.txt, #UPDATE\n", + " seed: 127,\n", + " # A positive example is an 1000bp sequence with at least 1 class/feature annotated to it.\n", + " # A negative sample has no classes/features annotated to the sequence.\n", + " sample_negative: True,\n", + " sequence_length: 1000,\n", + " center_bin_to_predict: 200,\n", + " test_holdout: 0.2,\n", + " validation_holdout: 0.3,\n", + " # The feature must take up 50% of the bin (200bp) for it to be considered\n", + " # a feature annotated to that sequence.\n", + " feature_thresholds: 0.25,\n", + " mode: train,\n", + " save_datasets: [validate, test]\n", + "}\n", + "train_model: !obj:selene_sdk.TrainModel {\n", + " batch_size: 64,\n", + " max_steps: 501, # update this value for longer training\n", + " report_stats_every_n_steps: 250,\n", + " n_validation_samples: 6000,\n", + " n_test_samples: 22000,\n", + " cpu_n_threads: 32,\n", + " use_cuda: False, # TODO: update this if CUDA is not on your machine\n", + " data_parallel: False\n", + "}\n", + "random_seed: 1447\n", + "output_dir: ./tutorial/training_outputs/model #UPDATE\n", + "create_subdirectory: False\n", + "load_test_set: False\n", + "...\n" + ] + } + ], + "source": [ + "with open('all_neuron_tutorial.yml') as f:\n", + " contents = f.read()\n", + " print(contents)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "Above shows the .yml file used to train the minimal working example model in this workflow. \n", + "\n", + "To use your own data you must update the:\n", + "\n", + "1. n_targets (Number of Features you are training on):\n", + "\n", + "`n_targets: 7`\n", + "\n", + "2. Feature list file (a list of all the distinct features you are training on):\n", + "\n", + "`features: !obj:selene_sdk.utils.load_features_list {\n", + " input_path: /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt #UPDATE\n", + " }`\n", + " \n", + "3. Target Path File (A combined bed file for all of your features):\n", + "\n", + "`target_path: /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial.bed.gz, #UPDATE`\n", + "\n", + "4. Max_Steps (Maximum number of Training Steps), n_validation_samples (Number of Validation Samples), n_test_samples (Number of Testing Samples):\n", + "\n", + "`train_model: !obj:selene_sdk.TrainModel {\n", + " batch_size: 64,\n", + " max_steps: 501, # update this value for longer training\n", + " report_stats_every_n_steps: 250,\n", + " n_validation_samples: 6000,\n", + " n_test_samples: 22000,\n", + " cpu_n_threads: 32,\n", + " use_cuda: False, # TODO: update this if CUDA is not on your machine\n", + " data_parallel: False\n", + "}`\n", + "\n", + "Generally you want to train until the validation and training loss are not decreasing anymore. For the full 2032 features example, I have found that this occurs at around 250,000 training steps which is what I set the max_steps parameter to for that case. Depending on how many features you are using, this could be more or less. You will want to change how frequently the model statistics (ROC,AUC, etc) are outputted especially if you are training on a large number of steps. For 250,000 steps, I would recommend setting the `report_stats_every_n_steps` parameter to 10,000 so you can assess how the model is training frequently enough but you are not wasting too much time calculating the ROC and AUC too frequently. For Validation and Testing Samples, I used 40,000 and 500,000 respectively for 2032 features. Again this will depend on the amount of features you have so adjust this number to be more or less depending on the number of features you are training the model on. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Getting Predictions from Trained Model:\n", + "\n", + "**Workflow Command to get predictions from trained model for Minimal Example:** `sos run LDSC_DeepSea_Minimal_Example.ipynb make_annot --feature_list /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt --model /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial/training_outputs/model --output_tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --num_features 7 --vcf /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_1000G_chr_`\n", + "\n", + "**Explanations of Parameters so that you can change to run with your own data:**\n", + "\n", + "1. feature_list: Path to list of distinct features, same as one used in .yml file\n", + "\n", + "2. model: Path to location of trained model folder, is the output_dir parameter in the .yml training file\n", + "\n", + "3. output_tsv: Path to directory where you want to output the predictions to \n", + "\n", + "4. num_features: Number of Features you trained the model on\n", + "\n", + "5. vcf: Path to location of reference genome vcf files you want to use for predictions, program will append the chromosome number and .vcf to the end of file name automatically to loop over all the chromosomes so format file name in command as 1000G_chr_ and leave the chr numbers and .vcf out. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Formatting Resulting Predictions to Annotation File for LDSC: \n", + "\n", + "**Workflow Command to format Prediction files to LDSC Annotation Files:** `sos run LDSC_DeepSea_Minimal_Example.ipynb format_annot --tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --annot_files /mnt/mfs/statgen/Anmol/training_files/tutorial/annot_files\n", + "\n", + "**Explanations of Parameters so that you can change to run with your own data:**\n", + "\n", + "1. tsv: Path to where prediction files (.tsv files) are located\n", + "\n", + "2. annot_files: Path to location where you want the annotation files to be outputted" + ] }, { "cell_type": "markdown", @@ -198,9 +261,13 @@ "parameter: model = str\n", "#path to output directory\n", "parameter: output_tsv = str\n", + "#number of features\n", + "parameter: num_features = int\n", + "#VCF files for Reference Genome [Give in this format: tutorial_1000G_chr_, as program will loop over chromsomes and add vcf extension automatically]\n", + "parameter: vcf = str()\n", "\n", "\n", - "python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif'\n", + "python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif',expand = \"${ }\"\n", "\n", " from selene_sdk.utils import load_path\n", " from selene_sdk.utils import parse_configs_and_run\n", @@ -211,11 +278,11 @@ " from selene_sdk.utils import DeeperDeepSEA\n", " import glob\n", " import os\n", - " distinct_features = load_features_list({feature_list})\n", + " distinct_features = load_features_list('${feature_list}')\n", "\n", " model_predict = AnalyzeSequences(\n", - " NonStrandSpecific(DeeperDeepSEA(1000,{num_features})),\n", - " {model}+\"/best_model.pth.tar\",\n", + " NonStrandSpecific(DeeperDeepSEA(1000,${num_features})),\n", + " '${model}'+\"/best_model.pth.tar\",\n", " sequence_length=1000,\n", " features=distinct_features,\n", " reference_sequence=Genome(\"/mnt/mfs/statgen/Anmol/training_files/male.hg19.fasta\"),\n", @@ -224,9 +291,9 @@ "\n", " for i in range(1,23):\n", " model_predict.variant_effect_prediction(\n", - " \"/mnt/mfs/statgen/Anmol/training_files/testing/1000G_chr_\"+str(i)+\".vcf\",\n", + " ${vcf}+str(i)+\".vcf\",\n", " save_data=[\"abs_diffs\"], # only want to save the absolute diff score data\n", - " output_dir={output})" + " output_dir='${output_tsv}')" ] }, { @@ -251,7 +318,7 @@ "\n", "[format_annot]\n", "\n", - "#path to tsv files directory\n", + "#path to tsv files [Give in this format: tutorial_1000G_chr_, as program will loop over chromsomes and add tsv extension automatically]\n", "parameter: tsv = path()\n", "#path to output file directory\n", "parameter: annot_files = path()\n", @@ -259,13 +326,13 @@ "R: expand = \"${ }\", container=\"/mnt/mfs/statgen/Anmol/r-packages.sif\"\n", " library(data.table)\n", " library(tidyverse)\n", - " data = fread(paste0(\"${tsv}\",\"/tutorial_1000G_chr_\",22,\"_abs_diffs.tsv\"))\n", + " data = fread(paste0(\"${tsv}\",22,\"_abs_diffs.tsv\"))\n", " features = colnames(data)[9:ncol(data)]\n", " features = data.frame(features)\n", " features$encoding = paste0(\"feat_\",seq(1,nrow(features)))\n", " fwrite(features,paste0(\"${annot_files}\",\"/feature_encoding.txt\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", " for (i in seq(1,22)){\n", - " data = fread(paste0(\"${tsv}\",\"/tutorial_1000G_chr_\",i,\"_abs_diffs.tsv\"))\n", + " data = fread(paste0(\"${tsv}\",i,\"_abs_diffs.tsv\"))\n", " data_2 = select(data,-seq(4,8))\n", " base = data.frame(base=rep(1,nrow(data_2)))\n", " fwrite(base,paste0(\"${annot_files}\",\"/base_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", @@ -278,215 +345,6 @@ " }\n", " }" ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "Python 3 (ipykernel)" - }, - "source": [ - "## Munge Summary Statistics (Option 1: No Signed Summary Statistic):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "# Option when Summary Statistic File does not contain a Z or Beta Column (Signed Summary Statistic)\n", - "\n", - "[munge_sumstats_no_sign]\n", - "\n", - "\n", - "\n", - "#path to summary statistic file\n", - "parameter: sumst = str\n", - "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", - "parameter: alleles = \"w_hm3.snplist\"\n", - "#path to output file\n", - "parameter: output_sumst = str\n", - "#does summary statistic contain Z or Beta\n", - "parameter: signed = False\n", - "\n", - "bash: expand = '${ }'\n", - " if [${signed}==True]\n", - " then\n", - " python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst} --a1-inc\n", - " fi" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Munge Summary Statistics (Option 2: Contains Signed Summary Statistic):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "# This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta)\n", - "[munge_sumstats_sign]\n", - "\n", - "\n", - "\n", - "#path to summary statistic file\n", - "parameter: sumst = str\n", - "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", - "parameter: alleles = \"w_hm3.snplist\"\n", - "#path to output file\n", - "parameter: output_sumst_2 = str\n", - "#does summary statistic contain Z or Beta\n", - "parameter: signed = False\n", - "\n", - "bash: expand = '${ }'\n", - " if [${signed}==False]\n", - " then\n", - " python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst_2}\n", - " fi" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "Python 3 (ipykernel)" - }, - "source": [ - "## Calculate LD Scores:\n", - "\n", - "**Make sure to delete SNP,CHR, and BP columns from annotation files if they are present otherwise this code will not work. Before deleting, if these columns are present, make sure that the annotation file is sorted.**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "\n", - "[calc_ld_score]\n", - "\n", - "#Path to directory with bim files\n", - "parameter: bim = path()\n", - "#Path to directory with annotation files, output will appear here too. Make sure to remove the SNP, CHR, and BP columns from the annotation files if present before running.\n", - "parameter: annot_files = path()\n", - "#number of features\n", - "parameter: num_features = int\n", - "\n", - "bash: expand = '${ }'\n", - " #echo {annot_files} > out.txt\n", - " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.22 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_22.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_22 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", - " seq 1 22| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.j --l2 --ld-wind-cm 1 --annot ${annot_files}/base_chr_j.annot.gz --thin-annot --out ${annot_files}/base_chr_j --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Convert LD Score SNPs to AD Summary Statistic Format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "# Convert SNP format in LD Score Files to CHR:BP to match with AD Summary Statistic Format\n", - "\n", - "\n", - "[convert_ld_snps]\n", - "\n", - "#Path to directory with ld score files AND annotation files\n", - "parameter: ld_scores = str\n", - "\n", - "parameter: num_features = int\n", - "\n", - "\n", - "R: expand = \"${ }\", container=\"/mnt/mfs/statgen/Anmol/r-packages.sif\"\n", - " library(tidyverse)\n", - " #library(R.utils)\n", - " library(data.table)\n", - " for (i in seq(1,22)){\n", - " data = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".l2.ldscore.gz\")),header=T)\n", - " data_2 = fread(paste0(\"${ld_scores}/base_chr_\",i,\".l2.M_5_50\"))\n", - " data_3 = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".annot.gz\")),header=T)\n", - " data$SNP = paste0(data$CHR,\":\",data$BP)\n", - " fwrite(data,paste0(\"${ld_scores}/AD_base_chr_\",i,\".l2.ldscore.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", - " fwrite(data_2,paste0(\"${ld_scores}/AD_base_chr_\",i,\".l2.M_5_50\"),quote=F,sep=\"\\t\",row.names=F,col.names=F)\n", - " fwrite(data_3,paste0(\"${ld_scores}/AD_base_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", - " for (j in seq(1,${num_features})){\n", - " data = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\")),header=T)\n", - " data_2 = fread(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.M_5_50\"))\n", - " data_3 = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".annot.gz\")),header=T)\n", - " data$SNP = paste0(data$CHR,\":\",data$BP)\n", - " fwrite(data,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", - " fwrite(data_2,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".l2.M_5_50\"),quote=F,sep=\"\\t\",row.names=F,col.names=F)\n", - " fwrite(data_3,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", - " }\n", - " }\n", - " \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "Python 3 (ipykernel)" - }, - "source": [ - "## Calculate Functional Enrichment using Annotations:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "\n", - "[calc_enrichment]\n", - "\n", - "#Path to Control Summary statistics File\n", - "parameter: ctrl_sumstats = str\n", - "#Path to AD Summary statistics File\n", - "parameter: AD_sumstats = str\n", - "#Path to Reference LD Scores File Directory \n", - "parameter: ref_ld = str\n", - "#Path to LD Weight Files for Control Sumstats (Format like minimal working example)\n", - "parameter: w_ld_ctrl = str\n", - "#path to frequency files for Control Sumstats (Format like minimal working example)\n", - "parameter: frq_file_ctrl = str\n", - "#Path to LD Weight Files for AD Sumstats (Format like minimal working example)\n", - "parameter: w_ld_AD = str\n", - "#path to frequency files for AD Sumstats (Format like minimal working example)\n", - "parameter: frq_file_AD = str\n", - "#Number of Features\n", - "parameter: num_features = int \n", - "#Control Phenotype, For Output\n", - "parameter: pheno = str\n", - "\n", - "bash: expand = '${ }'\n", - " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${ctrl_sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld_ctrl} --overlap-annot --frqfile-chr ${frq_file_ctrl} --out ${ref_ld}/${pheno}_feat_j\n", - " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j" - ] } ], "metadata": { diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos index ee3dd6f..31b9dd3 100644 --- a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.sos @@ -15,9 +15,13 @@ parameter: feature_list = str parameter: model = str #path to output directory parameter: output_tsv = str +#number of features +parameter: num_features = int +#VCF files for Reference Genome [Give in this format: tutorial_1000G_chr_, as program will loop over chromsomes and add vcf extension automatically] +parameter: vcf = str() -python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' +python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif',expand = "${ }" from selene_sdk.utils import load_path from selene_sdk.utils import parse_configs_and_run @@ -28,11 +32,11 @@ python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' from selene_sdk.utils import DeeperDeepSEA import glob import os - distinct_features = load_features_list({feature_list}) + distinct_features = load_features_list('${feature_list}') model_predict = AnalyzeSequences( - NonStrandSpecific(DeeperDeepSEA(1000,{num_features})), - {model}+"/best_model.pth.tar", + NonStrandSpecific(DeeperDeepSEA(1000,${num_features})), + '${model}'+"/best_model.pth.tar", sequence_length=1000, features=distinct_features, reference_sequence=Genome("/mnt/mfs/statgen/Anmol/training_files/male.hg19.fasta"), @@ -41,13 +45,13 @@ python3: container='/mnt/mfs/statgen/Anmol/deepsea_latest.sif' for i in range(1,23): model_predict.variant_effect_prediction( - "/mnt/mfs/statgen/Anmol/training_files/testing/1000G_chr_"+str(i)+".vcf", + ${vcf}+str(i)+".vcf", save_data=["abs_diffs"], # only want to save the absolute diff score data - output_dir={output}) + output_dir='${output_tsv}') [format_annot] -#path to tsv files directory +#path to tsv files [Give in this format: tutorial_1000G_chr_, as program will loop over chromsomes and add tsv extension automatically] parameter: tsv = path() #path to output file directory parameter: annot_files = path() @@ -55,13 +59,13 @@ parameter: annot_files = path() R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" library(data.table) library(tidyverse) - data = fread(paste0("${tsv}","/tutorial_1000G_chr_",22,"_abs_diffs.tsv")) + data = fread(paste0("${tsv}",22,"_abs_diffs.tsv")) features = colnames(data)[9:ncol(data)] features = data.frame(features) features$encoding = paste0("feat_",seq(1,nrow(features))) fwrite(features,paste0("${annot_files}","/feature_encoding.txt"),quote=F,sep="\t",row.names=F,col.names=T) for (i in seq(1,22)){ - data = fread(paste0("${tsv}","/tutorial_1000G_chr_",i,"_abs_diffs.tsv")) + data = fread(paste0("${tsv}",i,"_abs_diffs.tsv")) data_2 = select(data,-seq(4,8)) base = data.frame(base=rep(1,nrow(data_2))) fwrite(base,paste0("${annot_files}","/base_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) @@ -74,114 +78,3 @@ R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" } } -[munge_sumstats_no_sign] - - - -#path to summary statistic file -parameter: sumst = str -#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program -parameter: alleles = "w_hm3.snplist" -#path to output file -parameter: output_sumst = str -#does summary statistic contain Z or Beta -parameter: signed = False - -bash: expand = '${ }' - if [${signed}==True] - then - python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst} --a1-inc - fi - -# This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta) -[munge_sumstats_sign] - - - -#path to summary statistic file -parameter: sumst = str -#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program -parameter: alleles = "w_hm3.snplist" -#path to output file -parameter: output_sumst_2 = str -#does summary statistic contain Z or Beta -parameter: signed = False - -bash: expand = '${ }' - if [${signed}==False] - then - python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst_2} - fi - -[calc_ld_score] - -#Path to directory with bim files -parameter: bim = path() -#Path to directory with annotation files, output will appear here too. Make sure to remove the SNP, CHR, and BP columns from the annotation files if present before running. -parameter: annot_files = path() -#number of features -parameter: num_features = int - -bash: expand = '${ }' - #echo {annot_files} > out.txt - seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.22 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_22.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_22 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt - seq 1 22| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.j --l2 --ld-wind-cm 1 --annot ${annot_files}/base_chr_j.annot.gz --thin-annot --out ${annot_files}/base_chr_j --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt - -[convert_ld_snps] - -#Path to directory with ld score files AND annotation files -parameter: ld_scores = str - -parameter: num_features = int - - -R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" - library(tidyverse) - #library(R.utils) - library(data.table) - for (i in seq(1,22)){ - data = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".l2.ldscore.gz")),header=T) - data_2 = fread(paste0("${ld_scores}/base_chr_",i,".l2.M_5_50")) - data_3 = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".annot.gz")),header=T) - data$SNP = paste0(data$CHR,":",data$BP) - fwrite(data,paste0("${ld_scores}/AD_base_chr_",i,".l2.ldscore.gz"),quote=F,sep="\t",row.names=F,col.names=T) - fwrite(data_2,paste0("${ld_scores}/AD_base_chr_",i,".l2.M_5_50"),quote=F,sep="\t",row.names=F,col.names=F) - fwrite(data_3,paste0("${ld_scores}/AD_base_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) - for (j in seq(1,${num_features})){ - data = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.ldscore.gz")),header=T) - data_2 = fread(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.M_5_50")) - data_3 = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".annot.gz")),header=T) - data$SNP = paste0(data$CHR,":",data$BP) - fwrite(data,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".l2.ldscore.gz"),quote=F,sep="\t",row.names=F,col.names=T) - fwrite(data_2,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".l2.M_5_50"),quote=F,sep="\t",row.names=F,col.names=F) - fwrite(data_3,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) - } - } - - - -[calc_enrichment] - -#Path to Control Summary statistics File -parameter: ctrl_sumstats = str -#Path to AD Summary statistics File -parameter: AD_sumstats = str -#Path to Reference LD Scores File Directory -parameter: ref_ld = str -#Path to LD Weight Files for Control Sumstats (Format like minimal working example) -parameter: w_ld_ctrl = str -#path to frequency files for Control Sumstats (Format like minimal working example) -parameter: frq_file_ctrl = str -#Path to LD Weight Files for AD Sumstats (Format like minimal working example) -parameter: w_ld_AD = str -#path to frequency files for AD Sumstats (Format like minimal working example) -parameter: frq_file_AD = str -#Number of Features -parameter: num_features = int -#Control Phenotype, For Output -parameter: pheno = str - -bash: expand = '${ }' - seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${ctrl_sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld_ctrl} --overlap-annot --frqfile-chr ${frq_file_ctrl} --out ${ref_ld}/${pheno}_feat_j - seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j - From 62812e9db6cd3228e4ee2f82fc269a1887f572a1 Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Thu, 3 Mar 2022 13:22:50 -0500 Subject: [PATCH 18/63] Add files via upload --- .../LDSC_DeepSea_Minimal_Example.ipynb | 49 +++++++++++++------ 1 file changed, 35 insertions(+), 14 deletions(-) diff --git a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb index 1e39798..4ca5ada 100644 --- a/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb +++ b/LDSC/Deep_Learning/LDSC_DeepSea_Minimal_Example.ipynb @@ -37,12 +37,29 @@ ] }, { + "attachments": { + "image.png": { + "image/png": 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" + } + }, "cell_type": "markdown", "metadata": { "kernel": "Python 3 (ipykernel)" }, "source": [ - "## Background:" + "## Background:\n", + "\n", + "DeepSea is a deep learning model developed by researchers at Princeton University to integrate epigenomic feature data and generate functional predictions for these features on input variants given by the user. DeepSEA can accurately predict the epigenetic state of a sequence, including transcription factors binding, DNase I sensitivities and histone marks in multiple cell types, and further utilize this capability to predict the chromatin effects of sequence variants and prioritize regulatory variants. This model allows for the user to predict effects based on an increased context such as a 1 kB window around the variant which allows for better accuracy and predictions from the model. \n", + "\n", + "![image.png](attachment:image.png)\n", + "\n", + "This diagram shows the DeepSea pipeline. First the model takes an input of 1 kB sequence from the reference genome and trains the model on epigenomic features such as DNase Sites and TFs. Then, using the trained model you can generate functional predictions, predictions of the effect the variant will have on the features in the model, on your own set of variants. These predictions can be used to identify and priortize functionally important variants for certain features in your model.\n", + "\n", + "For more background and information please refer two two papers written by the creaters at Princeton University:\n", + "\n", + "1. [Predicting effects of noncoding variants with deep learning–based sequence model](https://www.nature.com/articles/nmeth.3547)\n", + "\n", + "2. [Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk](https://www.nature.com/articles/s41588-019-0420-0)" ] }, { @@ -138,21 +155,21 @@ "\n", "To use your own data you must update the:\n", "\n", - "1. n_targets (Number of Features you are training on):\n", + "**1. n_targets (Number of Features you are training on):\n", "\n", "`n_targets: 7`\n", "\n", - "2. Feature list file (a list of all the distinct features you are training on):\n", + "**2. Feature list file (a list of all the distinct features you are training on):\n", "\n", "`features: !obj:selene_sdk.utils.load_features_list {\n", " input_path: /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt #UPDATE\n", " }`\n", " \n", - "3. Target Path File (A combined bed file for all of your features):\n", + "**3. Target Path File (A combined bed file for all of your features):\n", "\n", "`target_path: /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial.bed.gz, #UPDATE`\n", "\n", - "4. Max_Steps (Maximum number of Training Steps), n_validation_samples (Number of Validation Samples), n_test_samples (Number of Testing Samples):\n", + "**4. Max_Steps (Maximum number of Training Steps), n_validation_samples (Number of Validation Samples), n_test_samples (Number of Testing Samples):\n", "\n", "`train_model: !obj:selene_sdk.TrainModel {\n", " batch_size: 64,\n", @@ -176,19 +193,21 @@ "source": [ "## Getting Predictions from Trained Model:\n", "\n", - "**Workflow Command to get predictions from trained model for Minimal Example:** `sos run LDSC_DeepSea_Minimal_Example.ipynb make_annot --feature_list /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt --model /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial/training_outputs/model --output_tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --num_features 7 --vcf /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_1000G_chr_`\n", + "**Workflow Command to get predictions from trained model for Minimal Example:** \n", + "\n", + "`sos run LDSC_DeepSea_Minimal_Example.ipynb make_annot --feature_list /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_features.txt --model /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial/training_outputs/model --output_tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --num_features 7 --vcf /mnt/mfs/statgen/Anmol/training_files/tutorial/tutorial_1000G_chr_`\n", "\n", "**Explanations of Parameters so that you can change to run with your own data:**\n", "\n", - "1. feature_list: Path to list of distinct features, same as one used in .yml file\n", + "**1. feature_list: Path to list of distinct features, same as one used in .yml file\n", "\n", - "2. model: Path to location of trained model folder, is the output_dir parameter in the .yml training file\n", + "**2. model: Path to location of trained model folder, is the output_dir parameter in the .yml training file\n", "\n", - "3. output_tsv: Path to directory where you want to output the predictions to \n", + "**3. output_tsv: Path to directory where you want to output the predictions to \n", "\n", - "4. num_features: Number of Features you trained the model on\n", + "**4. num_features: Number of Features you trained the model on\n", "\n", - "5. vcf: Path to location of reference genome vcf files you want to use for predictions, program will append the chromosome number and .vcf to the end of file name automatically to loop over all the chromosomes so format file name in command as 1000G_chr_ and leave the chr numbers and .vcf out. " + "**5. vcf: Path to location of reference genome vcf files you want to use for predictions, program will append the chromosome number and .vcf to the end of file name automatically to loop over all the chromosomes so format file name in command as `1000G_chr_` and leave the chr numbers and .vcf out. " ] }, { @@ -199,13 +218,15 @@ "source": [ "## Formatting Resulting Predictions to Annotation File for LDSC: \n", "\n", - "**Workflow Command to format Prediction files to LDSC Annotation Files:** `sos run LDSC_DeepSea_Minimal_Example.ipynb format_annot --tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --annot_files /mnt/mfs/statgen/Anmol/training_files/tutorial/annot_files\n", + "**Workflow Command to format Prediction files to LDSC Annotation Files:** \n", + "\n", + "`sos run LDSC_DeepSea_Minimal_Example.ipynb format_annot --tsv /mnt/mfs/statgen/Anmol/training_files/tutorial/testing --annot_files /mnt/mfs/statgen/Anmol/training_files/tutorial/annot_files`\n", "\n", "**Explanations of Parameters so that you can change to run with your own data:**\n", "\n", - "1. tsv: Path to where prediction files (.tsv files) are located\n", + "**1. tsv: Path to where prediction files (.tsv files) are located\n", "\n", - "2. annot_files: Path to location where you want the annotation files to be outputted" + "**2. annot_files: Path to location where you want the annotation files to be outputted" ] }, { From 49c376529c8b7db6481d7329ccaec3ae78f6ca05 Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Tue, 8 Mar 2022 12:10:08 -0500 Subject: [PATCH 19/63] Updated LDSC Code Workflow with new sections and improvements --- LDSC/LDSC_Code.ipynb | 402 +++++++++++++++++++++++++++++++++++++++++++ LDSC/LDSC_Code.sos | 127 ++++++++++++++ 2 files changed, 529 insertions(+) create mode 100644 LDSC/LDSC_Code.ipynb create mode 100644 LDSC/LDSC_Code.sos diff --git a/LDSC/LDSC_Code.ipynb b/LDSC/LDSC_Code.ipynb new file mode 100644 index 0000000..73b5fc1 --- /dev/null +++ b/LDSC/LDSC_Code.ipynb @@ -0,0 +1,402 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## SoS Workflow:\n", + "\n", + "This is the options and the SoS code to run the LDSC pipeline using your own data. \n", + "\n", + "## Command Interface:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "kernel": "SoS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: sos run LDSC_Code.ipynb [workflow_name | -t targets] [options] [workflow_options]\n", + " workflow_name: Single or combined workflows defined in this script\n", + " targets: One or more targets to generate\n", + " options: Single-hyphen sos parameters (see \"sos run -h\" for details)\n", + " workflow_options: Double-hyphen workflow-specific parameters\n", + "\n", + "Workflows:\n", + " make_annot\n", + " munge_sumstats_no_sign\n", + " munge_sumstats_sign\n", + " calc_ld_score\n", + " convert_ld_snps\n", + " calc_enrichment\n", + "\n", + "Sections\n", + " make_annot:\n", + " Workflow Options:\n", + " --bed VAL (as str, required)\n", + " path to bed file\n", + " --bim VAL (as str, required)\n", + " path to bim file\n", + " --annot VAL (as str, required)\n", + " name of output annotation file\n", + " munge_sumstats_no_sign:\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output-sumst VAL (as str, required)\n", + " path to output file\n", + " --[no-]signed (default to False)\n", + " does summary statistic contain Z or Beta\n", + " munge_sumstats_sign: This option is for when the summary statistic file does\n", + " contain a signed summary statistic (Z or Beta)\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output-sumst-2 VAL (as str, required)\n", + " path to output file\n", + " --[no-]signed (default to False)\n", + " does summary statistic contain Z or Beta\n", + " calc_ld_score:\n", + " Workflow Options:\n", + " --bim . (as path)\n", + " Path to directory with bim files\n", + " --annot-files . (as path)\n", + " Path to directory with annotation files, output will\n", + " appear here too. Make sure to remove the SNP, CHR, and\n", + " BP columns from the annotation files if present before\n", + " running.\n", + " --num-features VAL (as int, required)\n", + " number of features\n", + " convert_ld_snps:\n", + " Workflow Options:\n", + " --ld-scores VAL (as str, required)\n", + " Path to directory with ld score files AND annotation\n", + " files\n", + " --num-features VAL (as int, required)\n", + " calc_enrichment:\n", + " Workflow Options:\n", + " --ctrl-sumstats VAL (as str, required)\n", + " Path to Control Summary statistics File\n", + " --AD-sumstats VAL (as str, required)\n", + " Path to AD Summary statistics File\n", + " --ref-ld VAL (as str, required)\n", + " Path to Reference LD Scores File Directory\n", + " --w-ld-ctrl VAL (as str, required)\n", + " Path to LD Weight Files for Control Sumstats (Format\n", + " like minimal working example)\n", + " --frq-file-ctrl VAL (as str, required)\n", + " path to frequency files for Control Sumstats (Format\n", + " like minimal working example)\n", + " --w-ld-AD VAL (as str, required)\n", + " Path to LD Weight Files for AD Sumstats (Format like\n", + " minimal working example)\n", + " --frq-file-AD VAL (as str, required)\n", + " path to frequency files for AD Sumstats (Format like\n", + " minimal working example)\n", + " --num-features VAL (as int, required)\n", + " Number of Features\n", + " --pheno VAL (as str, required)\n", + " Control Phenotype, For Output\n" + ] + } + ], + "source": [ + "!sos run LDSC_Code.ipynb -h" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Python 3 (ipykernel)" + }, + "source": [ + "## Make Annotation File:" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "\n", + "[make_annot]\n", + "\n", + "# Make Annotated Bed File\n", + "\n", + "# path to bed file\n", + "parameter: bed = str \n", + "#path to bim file\n", + "parameter: bim = str\n", + "#name of output annotation file\n", + "parameter: annot = str\n", + "bash: \n", + " python2 /mnt/mfs/statgen/Anmol/ldsc/make_annot.py --bed-file {bed} --bimfile {bim} --annot-file {annot}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Munge Summary Statistics (Option 1: No Signed Summary Statistic File)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# Option when Summary Statistic File does not contain a Z or Beta Column (Signed Summary Statistic)\n", + "\n", + "[munge_sumstats_no_sign]\n", + "\n", + "\n", + "\n", + "#path to summary statistic file\n", + "parameter: sumst = str\n", + "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", + "parameter: alleles = \"w_hm3.snplist\"\n", + "#path to output file\n", + "parameter: output_sumst = str\n", + "#does summary statistic contain Z or Beta\n", + "parameter: signed = False\n", + "\n", + "bash: expand = '${ }'\n", + " if [${signed}==True]\n", + " then\n", + " python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst} --a1-inc\n", + " fi" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Munge Summary Statistics (Option 2: Signed Summary Statistic File)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta)\n", + "[munge_sumstats_sign]\n", + "\n", + "\n", + "\n", + "#path to summary statistic file\n", + "parameter: sumst = str\n", + "#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program\n", + "parameter: alleles = \"w_hm3.snplist\"\n", + "#path to output file\n", + "parameter: output_sumst_2 = str\n", + "#does summary statistic contain Z or Beta\n", + "parameter: signed = False\n", + "\n", + "bash: expand = '${ }'\n", + " if [${signed}==False]\n", + " then\n", + " python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst_2}\n", + " fi" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Calculate LD Scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "[calc_ld_score]\n", + "\n", + "#Path to directory with bim files\n", + "parameter: bim = path()\n", + "#Path to directory with annotation files, output will appear here too. Make sure to remove the SNP, CHR, and BP columns from the annotation files if present before running.\n", + "parameter: annot_files = path()\n", + "#number of features\n", + "parameter: num_features = int\n", + "\n", + "bash: expand = '${ }'\n", + " #echo {annot_files} > out.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.22 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_22.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_22 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 22| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.j --l2 --ld-wind-cm 1 --annot ${annot_files}/base_chr_j.annot.gz --thin-annot --out ${annot_files}/base_chr_j --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Create Separate LD Score Files for AD Summary Statistic SNP Format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# Convert SNP format in LD Score Files to CHR:BP to match with AD Summary Statistic Format\n", + "\n", + "\n", + "[convert_ld_snps]\n", + "\n", + "#Path to directory with ld score files AND annotation files\n", + "parameter: ld_scores = str\n", + "\n", + "parameter: num_features = int\n", + "\n", + "\n", + "R: expand = \"${ }\", container=\"/mnt/mfs/statgen/Anmol/r-packages.sif\"\n", + " library(tidyverse)\n", + " #library(R.utils)\n", + " library(data.table)\n", + " for (i in seq(1,22)){\n", + " data = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".l2.ldscore.gz\")),header=T)\n", + " data_2 = fread(paste0(\"${ld_scores}/base_chr_\",i,\".l2.M_5_50\"))\n", + " data_3 = read.table(gzfile(paste0(\"${ld_scores}/base_chr_\",i,\".annot.gz\")),header=T)\n", + " data$SNP = paste0(data$CHR,\":\",data$BP)\n", + " fwrite(data,paste0(\"${ld_scores}/AD_base_chr_\",i,\".l2.ldscore.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " fwrite(data_2,paste0(\"${ld_scores}/AD_base_chr_\",i,\".l2.M_5_50\"),quote=F,sep=\"\\t\",row.names=F,col.names=F)\n", + " fwrite(data_3,paste0(\"${ld_scores}/AD_base_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " for (j in seq(1,${num_features})){\n", + " data = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\")),header=T)\n", + " data_2 = fread(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".l2.M_5_50\"))\n", + " data_3 = read.table(gzfile(paste0(\"${ld_scores}/feat_\",j,\"_chr_\",i,\".annot.gz\")),header=T)\n", + " data$SNP = paste0(data$CHR,\":\",data$BP)\n", + " fwrite(data,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".l2.ldscore.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " fwrite(data_2,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".l2.M_5_50\"),quote=F,sep=\"\\t\",row.names=F,col.names=F)\n", + " fwrite(data_3,paste0(\"${ld_scores}/AD_feat_\",j,\"_chr_\",i,\".annot.gz\"),quote=F,sep=\"\\t\",row.names=F,col.names=T)\n", + " }\n", + " }\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Calculate Enrichments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "[calc_enrichment]\n", + "\n", + "#Path to Control Summary statistics File\n", + "parameter: ctrl_sumstats = str\n", + "#Path to AD Summary statistics File\n", + "parameter: AD_sumstats = str\n", + "#Path to Reference LD Scores File Directory \n", + "parameter: ref_ld = str\n", + "#Path to LD Weight Files for Control Sumstats (Format like minimal working example)\n", + "parameter: w_ld_ctrl = str\n", + "#path to frequency files for Control Sumstats (Format like minimal working example)\n", + "parameter: frq_file_ctrl = str\n", + "#Path to LD Weight Files for AD Sumstats (Format like minimal working example)\n", + "parameter: w_ld_AD = str\n", + "#path to frequency files for AD Sumstats (Format like minimal working example)\n", + "parameter: frq_file_AD = str\n", + "#Number of Features\n", + "parameter: num_features = int \n", + "#Control Phenotype, For Output\n", + "parameter: pheno = str\n", + "\n", + "bash: expand = '${ }'\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${ctrl_sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld_ctrl} --overlap-annot --frqfile-chr ${frq_file_ctrl} --out ${ref_ld}/${pheno}_feat_j\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SoS", + "language": "sos", + "name": "sos" + }, + "language_info": { + "codemirror_mode": "sos", + "file_extension": ".sos", + "mimetype": "text/x-sos", + "name": "sos", + "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", + "pygments_lexer": "sos" + }, + "sos": { + "kernels": [ + [ + "Python 3 (ipykernel)", + "python3", + "python3", + "", + { + "name": "ipython", + "version": 3 + } + ], + [ + "SoS", + "sos", + "", + "", + "sos" + ] + ], + "panel": { + "displayed": true, + "height": 0 + }, + "version": "0.22.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/LDSC/LDSC_Code.sos b/LDSC/LDSC_Code.sos new file mode 100644 index 0000000..6d928fa --- /dev/null +++ b/LDSC/LDSC_Code.sos @@ -0,0 +1,127 @@ +#!/usr/bin/env sos-runner +#fileformat=SOS1.0 + +[make_annot] + +# Make Annotated Bed File + +# path to bed file +parameter: bed = str +#path to bim file +parameter: bim = str +#name of output annotation file +parameter: annot = str +bash: + python2 /mnt/mfs/statgen/Anmol/ldsc/make_annot.py --bed-file {bed} --bimfile {bim} --annot-file {annot} + +[munge_sumstats_no_sign] + + + +#path to summary statistic file +parameter: sumst = str +#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program +parameter: alleles = "w_hm3.snplist" +#path to output file +parameter: output_sumst = str +#does summary statistic contain Z or Beta +parameter: signed = False + +bash: expand = '${ }' + if [${signed}==True] + then + python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst} --a1-inc + fi + +# This option is for when the summary statistic file does contain a signed summary statistic (Z or Beta) +[munge_sumstats_sign] + + + +#path to summary statistic file +parameter: sumst = str +#path to Hapmap3 SNPs file, keep all columns (SNP, A1, and A2) for the munge_sumstats program +parameter: alleles = "w_hm3.snplist" +#path to output file +parameter: output_sumst_2 = str +#does summary statistic contain Z or Beta +parameter: signed = False + +bash: expand = '${ }' + if [${signed}==False] + then + python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst_2} + fi + +[calc_ld_score] + +#Path to directory with bim files +parameter: bim = path() +#Path to directory with annotation files, output will appear here too. Make sure to remove the SNP, CHR, and BP columns from the annotation files if present before running. +parameter: annot_files = path() +#number of features +parameter: num_features = int + +bash: expand = '${ }' + #echo {annot_files} > out.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.22 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_22.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_22 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 22| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.j --l2 --ld-wind-cm 1 --annot ${annot_files}/base_chr_j.annot.gz --thin-annot --out ${annot_files}/base_chr_j --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + +[convert_ld_snps] + +#Path to directory with ld score files AND annotation files +parameter: ld_scores = str + +parameter: num_features = int + + +R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" + library(tidyverse) + #library(R.utils) + library(data.table) + for (i in seq(1,22)){ + data = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".l2.ldscore.gz")),header=T) + data_2 = fread(paste0("${ld_scores}/base_chr_",i,".l2.M_5_50")) + data_3 = read.table(gzfile(paste0("${ld_scores}/base_chr_",i,".annot.gz")),header=T) + data$SNP = paste0(data$CHR,":",data$BP) + fwrite(data,paste0("${ld_scores}/AD_base_chr_",i,".l2.ldscore.gz"),quote=F,sep="\t",row.names=F,col.names=T) + fwrite(data_2,paste0("${ld_scores}/AD_base_chr_",i,".l2.M_5_50"),quote=F,sep="\t",row.names=F,col.names=F) + fwrite(data_3,paste0("${ld_scores}/AD_base_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) + for (j in seq(1,${num_features})){ + data = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.ldscore.gz")),header=T) + data_2 = fread(paste0("${ld_scores}/feat_",j,"_chr_",i,".l2.M_5_50")) + data_3 = read.table(gzfile(paste0("${ld_scores}/feat_",j,"_chr_",i,".annot.gz")),header=T) + data$SNP = paste0(data$CHR,":",data$BP) + fwrite(data,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".l2.ldscore.gz"),quote=F,sep="\t",row.names=F,col.names=T) + fwrite(data_2,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".l2.M_5_50"),quote=F,sep="\t",row.names=F,col.names=F) + fwrite(data_3,paste0("${ld_scores}/AD_feat_",j,"_chr_",i,".annot.gz"),quote=F,sep="\t",row.names=F,col.names=T) + } + } + + + +[calc_enrichment] + +#Path to Control Summary statistics File +parameter: ctrl_sumstats = str +#Path to AD Summary statistics File +parameter: AD_sumstats = str +#Path to Reference LD Scores File Directory +parameter: ref_ld = str +#Path to LD Weight Files for Control Sumstats (Format like minimal working example) +parameter: w_ld_ctrl = str +#path to frequency files for Control Sumstats (Format like minimal working example) +parameter: frq_file_ctrl = str +#Path to LD Weight Files for AD Sumstats (Format like minimal working example) +parameter: w_ld_AD = str +#path to frequency files for AD Sumstats (Format like minimal working example) +parameter: frq_file_AD = str +#Number of Features +parameter: num_features = int +#Control Phenotype, For Output +parameter: pheno = str + +bash: expand = '${ }' + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${ctrl_sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld_ctrl} --overlap-annot --frqfile-chr ${frq_file_ctrl} --out ${ref_ld}/${pheno}_feat_j + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j + From caac1a743b2aaf42344ca626c4b34459a3c8732e Mon Sep 17 00:00:00 2001 From: asingh100 <55717171+asingh100@users.noreply.github.com> Date: Wed, 9 Mar 2022 23:01:50 -0500 Subject: [PATCH 20/63] Finished LDSC Code Notebook and Minimal Working Example (Tutorial) Notebook --- LDSC/LDSC.ipynb | 433 +++++++++++++++++++++++++++++++++++++++++++ LDSC/LDSC.sos | 3 + LDSC/LDSC_Code.ipynb | 54 ++++-- LDSC/LDSC_Code.sos | 54 ++++-- 4 files changed, 504 insertions(+), 40 deletions(-) create mode 100644 LDSC/LDSC.ipynb create mode 100644 LDSC/LDSC.sos diff --git a/LDSC/LDSC.ipynb b/LDSC/LDSC.ipynb new file mode 100644 index 0000000..240f180 --- /dev/null +++ b/LDSC/LDSC.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "# Stratified LD Score Regression \n", + "This notebook implements the pipepline of [S-LDSC](https://github.com/bulik/ldsc/wiki) for LD score and functional enrichment analysis. It is written by Anmol Singh (singh.anmol@columbia.edu), with input from Dr. Gao Wang.\n", + "\n", + "**FIXME: the initial draft is complete but pending Gao's review and documentation with minimal working example**" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Markdown" + }, + "source": [ + "The pipeline is developed to integrate GWAS summary statistics data, annotation data, and LD reference panel data to compute functional enrichment for each of the epigenomic annotations that the user provides using the S-LDSC model. We will first start off with an introduction, instructions to set up, and the minimal working examples. Then the workflow code that can be run using SoS on any data will be at the end. \n", + "\n", + "## A brief review on Stratified LD score regression\n", + "\n", + "Here I briefly review LD Score Regression and what it is used for. For more in depth information on LD Score Regression please read the following three papers:\n", + "\n", + "1. \"LD Score regression distinguishes confounding from polygenicity in genome-wide association studies\" by Sullivan et al (2015)\n", + "\n", + "2. \"Partitioning heritability by functional annotation using genome-wide association summary statistics\" by Finucane et al (2015)\n", + "\n", + "3. \"Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection\" by Gazal et al (2017)\n", + "\n", + "As stated in Sullivan et al 2015, confounding factors and polygenic effects can cause inflated test statistics and other methods cannot distinguish between inflation from confounding bias and a true signal. LD Score Regression (LDSC) is a technique that aims to identify the impact of confounding factors and polygenic effects using information from GWAS summary statistics. \n", + "\n", + "This approach involves using regression to mesaure the relationship between Linkage Disequilibrium (LD) scores and test statistics of SNPs from the GWAS summary statistics. Variants in LD with a \"causal\" variant show an elevation in test statistics in association analysis proportional to their LD (measured by $r^2$) with the causal variant within a certain window size (could be 1 cM, 1kB, etc.). In contrast, inflation from confounders such as population stratification that occur purely from genetic drift will not correlate with LD. For a polygenic trait, SNPs with a high LD score will have more significant χ2 statistics on average than SNPs with a low LD score. Thus, if we regress the $\\chi^2$ statistics from GWAS against LD Score, the intercept minus one is an estimator of the mean contribution of confounding bias to the inflation in the test statistics. The regression model is known as LD Score regression. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "### LDSC model\n", + "\n", + "Under a polygenic assumption, in which effect sizes for variants are drawn independently from distributions with variance proportional to $1/(p(1-p))$ where p is the minor allele frequency (MAF), the expected $\\chi^2$ statistic of variant j is:\n", + "\n", + "$$E[\\chi^2|l_j] = Nh^2l_j/M + Na + 1 \\quad (1)$$\n", + "\n", + "where $N$ is the sample size; $M$ is the number of SNPs, such that $h^2/M$ is the average heritability explained per SNP; $a$ measures the contribution of confounding biases, such as cryptic relatedness and population stratification; and $l_j = \\sum_k r^2_{jk}$ is the LD Score of variant $j$, which measures the amount of genetic variation tagged by $j$. A full derivation of this equation is provided in the Supplementary Note of Sullivan et al (2015). An alternative derivation is provided in Supplementary Note of Zhu and Stephens (2017) AoAS.\n", + "\n", + "From this we can see that LD Score regression can be used to compute SNP-based heritability for a phenotype or trait, from GWAS summary statistics and does not require genotype information like other methods such as REML do. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "### Stratified LDSC\n", + "\n", + "Heritability is the proportion of phenotypic variation (VP) that is due to variation in genetic values (VG) and thus can tell us how much of the difference in observed phenotypes in a sample is due to difference in genetics in the sample. It can also be extended to analyze partitioned heritability for a phenotype/trait split over categories. \n", + "\n", + "For Partitioned Heritability or Stratified LD Score Regression (S-LDSC) more power is added to our analysis by leveraging LD Score information as well as using SNPs that haven't reached Genome Wide Significance to partition heritability for a trait over categories which many other methods do not do. \n", + "\n", + "\n", + "S-LDSC relies on the fact that the $\\chi^2$ association statistic for a given SNP includes the effects of all SNPs tagged by this SNP meaning that in a region of high LD in the genome the given SNP from the GWAS represents the effects of a group of SNPs in that region.\n", + "\n", + "S-LDSC determines that a category of SNPs is enriched for heritability if SNPs with high LD to that category have more significant $\\chi^2$ statistics than SNPs with low LD to that category.\n", + "\n", + "Here, enrichment of a category is defined as the proportion of SNP heritability in the category divided by the proportion of SNPs in that category.\n", + "\n", + "More precisely, under a polygenic model, the expected $\\chi^2$ statistic of SNP $j$ is\n", + "\n", + "$$E[\\chi^2_j] = N\\sum_CT_Cl(j,C) + Na + 1 \\quad (2)$$\n", + "\n", + "where $N$ is sample size, C indexes categories, $ℓ(j, C)$ is the LD score of SNP j with respect to category $l(j,C) = \\sum_{k\\epsilon C} r^2_{jk}$, $a$ is a term that measures the contribution of confounding biases, and if the categories are disjoint, $\\tau_C$ is the per-SNP heritability in category $C$; if the categories overlap, then the per-SNP heritability of SNP j is $\\sum_{C:j\\epsilon C} \\tau_C$. Equation 2 allows us to estimate $\\tau_C$ via a (computationally simple) multiple regression of $\\chi^2$ against $ℓ(j, C)$, for either a quantitative or case-control study. \n", + "\n", + "To see how these methods have been applied to real world data as well as a further discussion on methods and comparisons to other methods please read the three papers listed at the top of the document." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Command Interface" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "kernel": "SoS" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: sos run LDSC_Code.ipynb [workflow_name | -t targets] [options] [workflow_options]\n", + " workflow_name: Single or combined workflows defined in this script\n", + " targets: One or more targets to generate\n", + " options: Single-hyphen sos parameters (see \"sos run -h\" for details)\n", + " workflow_options: Double-hyphen workflow-specific parameters\n", + "\n", + "Workflows:\n", + " make_annot\n", + " munge_sumstats_no_sign\n", + " munge_sumstats_sign\n", + " calc_ld_score\n", + " convert_ld_snps\n", + " calc_enrichment\n", + "\n", + "Sections\n", + " make_annot:\n", + " Workflow Options:\n", + " --bed VAL (as str, required)\n", + " path to bed file\n", + " --bim VAL (as str, required)\n", + " path to bim file\n", + " --annot VAL (as str, required)\n", + " name of output annotation file\n", + " munge_sumstats_no_sign:\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output-sumst VAL (as str, required)\n", + " path to output file\n", + " --[no-]signed (default to False)\n", + " does summary statistic contain Z or Beta\n", + " munge_sumstats_sign: This option is for when the summary statistic file does\n", + " contain a signed summary statistic (Z or Beta)\n", + " Workflow Options:\n", + " --sumst VAL (as str, required)\n", + " path to summary statistic file\n", + " --alleles 'w_hm3.snplist'\n", + " path to Hapmap3 SNPs file, keep all columns (SNP, A1,\n", + " and A2) for the munge_sumstats program\n", + " --output-sumst-2 VAL (as str, required)\n", + " path to output file\n", + " --[no-]signed (default to False)\n", + " does summary statistic contain Z or Beta\n", + " calc_ld_score:\n", + " Workflow Options:\n", + " --bim . (as path)\n", + " Path to directory with bim files\n", + " --annot-files . (as path)\n", + " Path to directory with annotation files, output will\n", + " appear here too. Make sure to remove the SNP, CHR, and\n", + " BP columns from the annotation files if present before\n", + " running.\n", + " --num-features VAL (as int, required)\n", + " number of features\n", + " convert_ld_snps:\n", + " Workflow Options:\n", + " --ld-scores VAL (as str, required)\n", + " Path to directory with ld score files AND annotation\n", + " files\n", + " --num-features VAL (as int, required)\n", + " calc_enrichment:\n", + " Workflow Options:\n", + " --ctrl-sumstats VAL (as str, required)\n", + " Path to Control Summary statistics File\n", + " --AD-sumstats VAL (as str, required)\n", + " Path to AD Summary statistics File\n", + " --ref-ld VAL (as str, required)\n", + " Path to Reference LD Scores File Directory\n", + " --w-ld-ctrl VAL (as str, required)\n", + " Path to LD Weight Files for Control Sumstats (Format\n", + " like minimal working example)\n", + " --frq-file-ctrl VAL (as str, required)\n", + " path to frequency files for Control Sumstats (Format\n", + " like minimal working example)\n", + " --w-ld-AD VAL (as str, required)\n", + " Path to LD Weight Files for AD Sumstats (Format like\n", + " minimal working example)\n", + " --frq-file-AD VAL (as str, required)\n", + " path to frequency files for AD Sumstats (Format like\n", + " minimal working example)\n", + " --num-features VAL (as int, required)\n", + " Number of Features\n", + " --pheno VAL (as str, required)\n", + " Control Phenotype, For Output\n" + ] + } + ], + "source": [ + "!sos run LDSC_Code.ipynb -h" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Example Analysis 1: Setting up Summary Statistic File\n", + "\n", + "This section will go over how to set up the summary statistic file for the phenotype you are trying to analyze. The summary statistic file we will use is for BMI and it can be downloaded here: http://www.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files. For the tutorial you also need the list of hapmap snps to restrict the summary statistic file to the recommended HapMap Phase 3 SNPs that will be used in the regression. The authors recommend restricting the analysis to HapMap Phase 3 SNPs because most GWAS summary statistics do not have information about imputation quality, thus using HapMap SNPs insures that you are using well-imputed and common variants for the analysis. This file can be downloaded here: https://storage.googleapis.com/broad-alkesgroup-public/LDSCORE/w_hm3.snplist.bz2. The summary statistic file should have the following columns with the following names for the analysis to work:\n", + "\n", + "SNP -- SNP identifier (e.g., rs number)\n", + "\n", + "N -- sample size (which may vary from SNP to SNP).\n", + "\n", + "P -- p-value.\n", + "\n", + "A1 -- first allele (effect allele)\n", + "\n", + "A2-- second allele (other allele)\n", + "\n", + "Signed Summary Statistic (Can be Z, BETA, or Odds Ratio(label as OR)), is optional if A1 is the risk increasing allele as you can use the other munge_sumstats option in the workflow.\n", + "\n", + "For the tutorial, the BMI summary statistic file is not signed so we will use the not_signed option in the workflow. Once you have set up the summary statistic file with these column headers you can reformat it for the analysis using the following command in the SoS workflow:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "sos run LDSC_Code.ipynb munge_sumstats_no_sign --sumstats GIANT_BMI_Speliotes2010_publicrelease_HapMapCeuFreq.txt\\\n", + "--output-sumst-2 BMI\\\n", + "--alleles w_hm3.snplist\\\n", + "--signed False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Markdown" + }, + "source": [ + "Explanation of flags:\n", + " \n", + " 1. `--sumstats`: path to summary statistic file\n", + " \n", + " 2. `--output-sumst-2`: Prefix for output file name\n", + " \n", + " 3. `--alleles`: path to hm3 alleles for merging\n", + " \n", + " 4. `--signed`: is the summary statistic file signed or not, logical flag (True or False)\n", + " \n", + "This will return a file called BMI.sumstats.gz which is a gzipped file that will be used as the summary statistic file in our analysis. It contains a row for each variant as well as the Allele Information and the Z score calculated by the workflow.\n", + "\n", + "## Example Analysis 2: Partitioned LD Score Regression\n", + "We first make the annotation file with respect to a specific annotation bed file using the make_annot option in the workflow. For the purposes of this tutorial we will use a Histone Mark annotation from Adipose Tissue, Adipose_Tissue.H3K27ac. I have provided the bed file for this annotation on a google drive folder (https://drive.google.com/drive/folders/1HdG-QsCl6fAspSxGsuoOCapwfnXCyfnU?usp=sharing) so you can download it to run the commands below. **Please place the plink files into a folder called plink files and make the output annotation directory annot_files before running the command below. When running on your own data in the future, please encode the annotation file names with feat_#_chr_# as the prefix so that the future commands in the pipeline will work. The command to make the annotation file for this annotation for one chromosome of the 1000 Genome Phase 3 variants (the reference data) for the tutorial is listed here:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "sos run LDSC_code.ipynb make_annot \\\n", + "\t\t--bed Adipose_Tissue.H3K27ac.bed \\\n", + "\t\t--bim plink_files/1000G.EUR.QC.22.bim \\\n", + "\t\t--annot annot_files/feat_1_chr_22.annot.gz" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "Markdown" + }, + "source": [ + "Explanation of flags:\n", + " \n", + " 1. `--bim`: path to 1000 Genome bim file\n", + " \n", + " 2. `--bed`: Path to bed file for annotation\n", + " \n", + " 3. `--annot`: output file name\n", + " \n", + "This command will output a file with 0/1 for each variant in the bim file which corresponds to whether this specific variant is within the regions described in the annotation file.\n", + "\n", + "## Example Analysis 3: Calculate LD Scores\n", + "\n", + "After the annotation file is made we can use it to calculate the LD Scores for this annotation. In this case the program recommends that you only print LD Scores for HapMap Phase 3 SNPs. This can be achieved by using the hapmap snplist file which can be found here: https://storage.googleapis.com/broad-alkesgroup-public/LDSCORE/w_hm3.snplist.bz2. You must get rid of the A1 and A2 columns in this file and keep only the SNP column before using the command below\n", + "\n", + "Make sure your annotation files have the same prefix as your LD Score files that you will create as the workflow will not be able to read the annotation files if they have a different prefix when you try to conduct the regression.\n", + "\n", + "The code below shows how to use the workflow to calculate LD Scores for the tutorial annotation. The workflow will automatically calculate LD Scores for each chromosome and the base or reference annotation using one simple command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "sos run LDSC_code.ipynb calc_ld_score \\\n", + "\t\t--bim plink_files \\\n", + "\t\t--annot_files annot_files \\\n", + "\t\t--num_features 1" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "Explanation of flags:\n", + " \n", + " 1. `--bim`: path to bim files directory\n", + " \n", + " 2. `--num_features`: Number of Epigenomic Features in analysis\n", + " \n", + " 3. `--annot`: path to annotation files directory\n", + " \n", + " \n", + "This command outputs the same gzipped LD score file as the simple case but instead of just an LD Score column, it will have one LD Score column for each annotation that you are calculating LD Scores for.\n", + "\n", + "![image.png](attachment:image.png)\n", + "\n", + "Now that we have calculated the LD Scores for each chromosome for our annotation, we can use these LD Scores to conduct the Partitioned LD Score Regression for our annotation. In this case we have to make sure that our annotation files are in the same folder and have the same prefix name as our LD Score files. Now we can conduct the Regression for our annotation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "sos run LDSC_code.ipynb calc_enrichment --sumstats BMI.sumstats.gz\\\n", + " --ref_ld annot_files/base.,annot_files/feat_1_chr_\\ \n", + " --w_ld weights_hm3_no_hla/weights.\\\n", + " --overlap-annot\\\n", + " --frq_file 1000G_frq/1000G.mac5eur.\\\n", + " --pheno BMI\\\n", + " --num_features 1" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "Here the comma indicates that we are concatinating the baseline model with the new annotation.\n", + "\n", + "The results our outputted in a .results file which shows the proportion of heritability and enrichment attributable to each category for the trait you are studying, in this case BMI.\n", + "\n", + "The results file for this analysis looks like this, where L2_1 represents our Adipose Tissue Annotation and baseL2_0 describes the baseline annotation:\n", + "\n", + "![image.png](attachment:image.png)\n", + "\n", + "\n", + "Explanation of flags:\n", + " \n", + " \n", + " 1. `sumstats`: #Path to Summary statistics File\n", + "\n", + " 2. `ref_ld`: Path to Reference LD Scores File Directory \n", + " \n", + " 3. `w_ld`: Path to LD Weight Files\n", + " \n", + " 4. `frq_file`: Path to Frequency Files\n", + " \n", + " 5. `pheno`: Phenotype of Summary statistic file, for output prefix\n", + " \n", + " 6. `num_features`: Number of Features in Analysis \n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SoS", + "language": "sos", + "name": "sos" + }, + "language_info": { + "codemirror_mode": "sos", + "file_extension": ".sos", + "mimetype": "text/x-sos", + "name": "sos", + "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", + "pygments_lexer": "sos" + }, + "sos": { + "kernels": [ + [ + "Markdown", + "markdown", + "markdown", + "", + "" + ], + [ + "SoS", + "sos", + "", + "", + "sos" + ] + ], + "panel": { + "displayed": true, + "height": 0 + }, + "version": "0.22.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/LDSC/LDSC.sos b/LDSC/LDSC.sos new file mode 100644 index 0000000..3acb973 --- /dev/null +++ b/LDSC/LDSC.sos @@ -0,0 +1,3 @@ +#!/usr/bin/env sos-runner +#fileformat=SOS1.0 + diff --git a/LDSC/LDSC_Code.ipynb b/LDSC/LDSC_Code.ipynb index 73b5fc1..4193e03 100644 --- a/LDSC/LDSC_Code.ipynb +++ b/LDSC/LDSC_Code.ipynb @@ -146,8 +146,8 @@ "parameter: bim = str\n", "#name of output annotation file\n", "parameter: annot = str\n", - "bash: \n", - " python2 /mnt/mfs/statgen/Anmol/ldsc/make_annot.py --bed-file {bed} --bimfile {bim} --annot-file {annot}" + "bash: expand = '${ }'\n", + " python2 /mnt/mfs/statgen/Anmol/ldsc/make_annot.py --bed-file ${bed} --bimfile ${bim} --annot-file ${annot}" ] }, { @@ -183,7 +183,7 @@ "parameter: signed = False\n", "\n", "bash: expand = '${ }'\n", - " if [${signed}==True]\n", + " if [${signed}==False]\n", " then\n", " python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst} --a1-inc\n", " fi" @@ -221,7 +221,7 @@ "parameter: signed = False\n", "\n", "bash: expand = '${ }'\n", - " if [${signed}==False]\n", + " if [${signed}==True]\n", " then\n", " python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst_2}\n", " fi" @@ -252,9 +252,30 @@ "parameter: annot_files = path()\n", "#number of features\n", "parameter: num_features = int\n", - "\n", + " \n", "bash: expand = '${ }'\n", " #echo {annot_files} > out.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.1 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_1.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_1 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.2 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_2.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_2 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.3 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_3.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_3 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.4 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_4.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_4 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.5 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_5.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_5 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.6 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_6.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_6 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.7 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_7.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_7 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.8 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_8.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_8 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.9 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_9.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_9 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.10 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_10.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_10 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.11 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_11.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_11 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.12 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_12.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_12 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.13 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_13.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_13 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.14 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_14.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_14 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.15 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_15.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_15 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.16 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_16.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_16 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.17 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_17.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_17 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.18 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_18.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_18 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.19 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_19.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_19 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.20 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_20.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_20 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.21 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_21.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_21 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.22 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_22.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_22 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt\n", " seq 1 22| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.j --l2 --ld-wind-cm 1 --annot ${annot_files}/base_chr_j.annot.gz --thin-annot --out ${annot_files}/base_chr_j --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt" ] @@ -331,28 +352,21 @@ "source": [ "[calc_enrichment]\n", "\n", - "#Path to Control Summary statistics File\n", - "parameter: ctrl_sumstats = str\n", - "#Path to AD Summary statistics File\n", - "parameter: AD_sumstats = str\n", + "#Path to Summary statistics File\n", + "parameter: sumstats = str\n", "#Path to Reference LD Scores File Directory \n", "parameter: ref_ld = str\n", - "#Path to LD Weight Files for Control Sumstats (Format like minimal working example)\n", - "parameter: w_ld_ctrl = str\n", - "#path to frequency files for Control Sumstats (Format like minimal working example)\n", - "parameter: frq_file_ctrl = str\n", - "#Path to LD Weight Files for AD Sumstats (Format like minimal working example)\n", - "parameter: w_ld_AD = str\n", - "#path to frequency files for AD Sumstats (Format like minimal working example)\n", - "parameter: frq_file_AD = str\n", - "#Number of Features\n", + "#Path to LD Weight Files (Format like minimal working example)\n", + "parameter: w_ld = str\n", + "#path to frequency files (Format like minimal working example)\n", + "parameter: frq_file = str\n", "parameter: num_features = int \n", "#Control Phenotype, For Output\n", "parameter: pheno = str\n", "\n", "bash: expand = '${ }'\n", - " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${ctrl_sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld_ctrl} --overlap-annot --frqfile-chr ${frq_file_ctrl} --out ${ref_ld}/${pheno}_feat_j\n", - " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j" + " seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld} --overlap-annot --frqfile-chr ${frq_file} --out ${ref_ld}/${pheno}_feat_j\n", + " #seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j" ] } ], diff --git a/LDSC/LDSC_Code.sos b/LDSC/LDSC_Code.sos index 6d928fa..454c612 100644 --- a/LDSC/LDSC_Code.sos +++ b/LDSC/LDSC_Code.sos @@ -11,8 +11,8 @@ parameter: bed = str parameter: bim = str #name of output annotation file parameter: annot = str -bash: - python2 /mnt/mfs/statgen/Anmol/ldsc/make_annot.py --bed-file {bed} --bimfile {bim} --annot-file {annot} +bash: expand = '${ }' + python2 /mnt/mfs/statgen/Anmol/ldsc/make_annot.py --bed-file ${bed} --bimfile ${bim} --annot-file ${annot} [munge_sumstats_no_sign] @@ -28,7 +28,7 @@ parameter: output_sumst = str parameter: signed = False bash: expand = '${ }' - if [${signed}==True] + if [${signed}==False] then python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst} --a1-inc fi @@ -48,7 +48,7 @@ parameter: output_sumst_2 = str parameter: signed = False bash: expand = '${ }' - if [${signed}==False] + if [${signed}==True] then python2 /mnt/mfs/statgen/Anmol/ldsc/munge_sumstats.py --sumstats ${sumst} --merge-alleles ${alleles} --out ${output_sumst_2} fi @@ -61,9 +61,30 @@ parameter: bim = path() parameter: annot_files = path() #number of features parameter: num_features = int - + bash: expand = '${ }' #echo {annot_files} > out.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.1 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_1.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_1 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.2 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_2.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_2 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.3 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_3.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_3 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.4 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_4.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_4 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.5 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_5.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_5 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.6 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_6.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_6 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.7 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_7.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_7 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.8 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_8.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_8 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.9 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_9.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_9 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.10 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_10.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_10 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.11 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_11.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_11 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.12 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_12.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_12 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.13 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_13.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_13 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.14 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_14.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_14 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.15 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_15.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_15 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.16 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_16.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_16 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.17 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_17.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_17 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.18 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_18.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_18 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.19 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_19.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_19 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.20 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_20.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_20 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.21 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_21.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_21 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.22 --l2 --ld-wind-cm 1 --annot ${annot_files}/feat_j_chr_22.annot.gz --thin-annot --out ${annot_files}/feat_j_chr_22 --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt seq 1 22| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --bfile ${bim}/1000G.EUR.QC.j --l2 --ld-wind-cm 1 --annot ${annot_files}/base_chr_j.annot.gz --thin-annot --out ${annot_files}/base_chr_j --print-snps /mnt/mfs/statgen/Anmol/ldsc/tutorial_data/w_hm3.snplist/snplist.txt @@ -102,26 +123,19 @@ R: expand = "${ }", container="/mnt/mfs/statgen/Anmol/r-packages.sif" [calc_enrichment] -#Path to Control Summary statistics File -parameter: ctrl_sumstats = str -#Path to AD Summary statistics File -parameter: AD_sumstats = str +#Path to Summary statistics File +parameter: sumstats = str #Path to Reference LD Scores File Directory parameter: ref_ld = str -#Path to LD Weight Files for Control Sumstats (Format like minimal working example) -parameter: w_ld_ctrl = str -#path to frequency files for Control Sumstats (Format like minimal working example) -parameter: frq_file_ctrl = str -#Path to LD Weight Files for AD Sumstats (Format like minimal working example) -parameter: w_ld_AD = str -#path to frequency files for AD Sumstats (Format like minimal working example) -parameter: frq_file_AD = str -#Number of Features +#Path to LD Weight Files (Format like minimal working example) +parameter: w_ld = str +#path to frequency files (Format like minimal working example) +parameter: frq_file = str parameter: num_features = int #Control Phenotype, For Output parameter: pheno = str bash: expand = '${ }' - seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${ctrl_sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld_ctrl} --overlap-annot --frqfile-chr ${frq_file_ctrl} --out ${ref_ld}/${pheno}_feat_j - seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j + seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${sumstats} --ref-ld-chr ${ref_ld}/base_chr_,${ref_ld}/feat_j_chr_ --w-ld-chr ${w_ld} --overlap-annot --frqfile-chr ${frq_file} --out ${ref_ld}/${pheno}_feat_j + #seq 1 ${num_features}| xargs -n 1 -I j -P 4 python2 /mnt/mfs/statgen/Anmol/ldsc/ldsc.py --h2 ${AD_sumstats} --ref-ld-chr ${ref_ld}/AD_base_chr_,${ref_ld}/AD_feat_j_chr_ --w-ld-chr ${w_ld_AD} --overlap-annot --frqfile-chr ${frq_file_AD} --out ${ref_ld}/AD_feat_j From 824514541e10975d38dc53e489cb2d4ae84f5f43 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Tue, 15 Mar 2022 09:54:53 -0400 Subject: [PATCH 21/63] fixes to burden part --- GWAS/LMM.ipynb | 57 ++++++++++++++++++++++++++++---------------------- 1 file changed, 32 insertions(+), 25 deletions(-) diff --git a/GWAS/LMM.ipynb b/GWAS/LMM.ipynb index 1790ad4..57aec3c 100644 --- a/GWAS/LMM.ipynb +++ b/GWAS/LMM.ipynb @@ -956,7 +956,7 @@ "parameter: mind_filter = 0.0\n", "input: bfile\n", "output: f'{cwd}/cache/{bfile:bn}.qc_pass.id', f'{cwd}/cache/{bfile:bn}.qc_pass.snplist' \n", - "task: trunk_workers = 1, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, walltime = '10h', mem = '20G', cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "bash: container=container_lmm, expand= \"${ }\", stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout' \n", " plink2 \\\n", " --bfile ${bfile:n} --mac 1 \\\n", @@ -1160,7 +1160,7 @@ "outputs": [], "source": [ "[regenie_burden_3]\n", - "# Select the annotations to be used in the mask file. format: mask# annotatio type\n", + "# Select the annotations to be used in the mask file. format: mask# annotation type\n", "parameter: mask_file = path(\".\")\n", "# Select the upper MAF to generate masks\n", "parameter: aaf_bins =[0.05]\n", @@ -1645,35 +1645,39 @@ "# Annotation file format: variantID, gene and functional annotation (space/tab delimited)\n", "parameter: anno_file = path\n", "input: snpannofile, anno_file\n", - "output: f'{cwd}/cache/someanno.txt',\n", - " f'{cwd}/cache/nonsin.genelist',\n", - " f'{cwd}/cache/nondup.snplist',\n", - " f'{cwd}/cache/someannoslim.csv'\n", + "output: f'{cwd}/cache/{snpannofile:nn}.subset.csv',\n", + " f'{cwd}/cache/non_singleton.genelist',\n", + " f'{cwd}/cache/non_duplicated.snplist',\n", + " f'{cwd}/cache/{anno_file:bnn}.burden_variants.csv'\n", "task: trunk_workers = 1, walltime = walltime, mem = mem, cores = numThreads, tags = f'{step_name}_{_output[0]:bn}'\n", "\n", - "# Extract target fields (CADD and GWAS catelog) from snpannofile to a smaller file someanno.txt\n", - "bash:container=container_lmm, expand = \"${ }\", stderr = f'{cwd}/{step_name}.stderr', stdout = f'{cwd}/{step_name}.stdout'\n", - " awk -F \",\" -v OFS=\"\\t\" '\n", - " NR==1 {\n", - " for (i=1; i<=NF; i++){\n", - " f[$i] = i}\n", - " } \n", - " {print $(f[\"Chr\"]), $(f[\"Start\"]), $(f[\"End\"]), $(f[\"Ref\"]),$(f[\"Alt\"]),$(f[\"avsnp150\"]),$(f[\"gwasCatalog\"]), $(f[\"CADD_raw\"]), $(f[\"CADD_phred\"])}\n", - " ' ${_input[0]} > ${_output[0]}\n", - "\n", "python: container=container_lmm, expand= \"${ }\", stderr = f'{cwd}/{step_name}.stderr', stdout = f'{cwd}/{step_name}.stdout'\n", " import pandas as pd\n", " import os\n", + " # Get the columns from the annotation file needed for this analysis and make a smaller file\n", + " combined_cols= ['Chr', 'Start', 'End', 'Ref', 'Alt', 'Func.refGene', 'Gene.refGene', 'avsnp150','CADD_phred', 'AF_nfe_exome']\n", + " df = pd.read_csv(${snpannofile:r}, compression='gzip', quotechar = '\"', dtype=\"string\", usecols=combined_cols)\n", + " print('Reading the annotation file done')\n", + " df.to_csv(${_output[0]:r}, index=False)\n", + " print('Saving subset of columns from the annotation file to output finished')\n", + "\n", + "\n", " snplist=[]\n", " nonsingenelist=[]\n", - " for file in os.listdir(\"${cwd}/cache/\"):\n", + " for file in os.listdir(\"${cwd}/cache\"):\n", " if file.endswith(\"burden_masks.snplist\"):\n", - " data=pd.read_csv(os.path.join(\"${cwd}/cache/\", file),header=None, sep=\"\\t\")\n", - "\n", + " data=pd.read_csv(os.path.join(\"${cwd}/cache\", file),header=None, sep=\"\\t\")\n", " # Gather all the SNPs \n", " snpset=data.iloc[:,1].str.split(\",\")\n", " snplist=snplist+[snp for snps in snpset for snp in snps]\n", - " # Remove the single variant genes and gather all the non-sin genes\n", + " def get_single_var_genes(list):\n", + " single_var = 0\n", + " for snp in range(len(snpset)):\n", + " single_var += len(snpset[snp])==1\n", + " return single_var\n", + " print('There are', get_single_var_genes(snpset), 'genes with only one variant')\n", + " print('There are', len(snpset) - get_single_var_genes(snpset), 'genes with more than one variant')\n", + " # Remove the single variant genes and gather all the non-single variant genes\n", " nonsingeneindex=[]\n", " for i in range(len(snpset)):\n", " if len(snpset[i])!=1:\n", @@ -1694,14 +1698,16 @@ " snplistfile.close()\n", "\n", " # Extract target SNPs from someanno.txt to an even smaller file someannoslim.csv\n", - " data=pd.read_csv(${_output[0]:r},sep=\"\\t\",lineterminator=\"\\n\",header=None,names=['Chr', 'Start', 'End', 'Ref', 'Alt', 'avsnp150', 'gwasCatalog', 'CADD_raw', 'CADD_phred'])\n", + " data=pd.read_csv(${_output[0]:r},lineterminator=\"\\n\")\n", " data=data.iloc[1:,:]\n", " data = data.astype(str)\n", " data['Chr'] = 'chr' + data['Chr'].astype(str)\n", " data[\"varID\"] = data.Chr.str.cat(others=[data.Start, data.Ref, data.Alt], sep=':')\n", - " anno_file=pd.read_csv(${_input[1]:r},sep=\" \",header=None,names=[snptag,'gene','funcion'])\n", + " anno_file=pd.read_csv(${_input[1]:r},sep=\" \",header=None,names=[snptag,'gene','function'])\n", + " print('Annotation input file for regenie contains', len(anno_file),'variants')\n", " minianno=data[data[snptag].isin(snplistslim)]\n", " minianno=pd.merge(minianno,anno_file[[snptag,'gene']],on=[snptag],how=\"inner\")\n", + " print('After merging non duplicated variants in burden_mask.snplist with the annotation file',len(minianno),'variants remain')\n", " minianno.to_csv(${_output[3]:r},index=False)" ] }, @@ -1722,7 +1728,7 @@ "# A given list of gene for annotation\n", "parameter: genelist = \"\"\n", "# Path to the SNP counts\n", - "parameter: nonsingenelist = f'{cwd}/cache/nonsin.genelist'\n", + "parameter: nonsingenelist = f'{cwd}/cache/non_singleton.genelist'\n", "# Select the annotations to be used in the mask file. format: mask# annotatio type\n", "parameter: mask_file = path(\".\")\n", "# Select the upper MAF to generate masks\n", @@ -1742,7 +1748,7 @@ " nonsingenelist=pd.read_csv(\"${nonsingenelist}\",sep=\"\\n\",header=None).iloc[:,0].to_list()\n", "\n", " for bin in binlist:\n", - " # Separate regenie resulta into mask and MAF bins\n", + " # Separate regenie results into mask and MAF bins\n", " data[data['ALT']==bin].to_csv(\"${_input[0]:nn}.\"+bin+'.snp_stats.gz', compression='gzip', sep='\\t', header = True, index = False)\n", " # Remove the single variant genes\n", " data2=data[data['SNP'].isin(nonsingenelist)]\n", @@ -2036,7 +2042,8 @@ "# A given list of gene for annotation\n", "parameter: genelist = \"\"\n", "# Annotation file\n", - "parameter: snpsomeanno = f'{cwd}/cache/someannoslim.csv'\n", + "parameter: anno_file = path\n", + "parameter: snpsomeanno = f'{cwd}/cache/{anno_file:b nn}.burden_variants.csv'\n", "# Select the annotations to be used in the mask file. format: mask# annotatio type\n", "parameter: mask_file = path(\".\")\n", "# Select the upper MAF to generate masks\n", From 0d2815f9d81219c288fb27225581ad974da3a343 Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Tue, 15 Mar 2022 11:31:19 -0400 Subject: [PATCH 22/63] deal with one input geno --- GWAS/Region_Extraction.ipynb | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/GWAS/Region_Extraction.ipynb b/GWAS/Region_Extraction.ipynb index 1cb5a27..3dc2a27 100644 --- a/GWAS/Region_Extraction.ipynb +++ b/GWAS/Region_Extraction.ipynb @@ -151,11 +151,11 @@ "# Work directory where output will be saved to\n", "parameter: cwd = path\n", "# Region specifications\n", - "parameter: region_file = path\n", + "parameter: region_file = path()\n", "# Genotype file inventory\n", - "parameter: geno_path = path\n", + "parameter: geno_path = path()\n", "# Phenotype path\n", - "parameter: pheno_path = path\n", + "parameter: pheno_path = path()\n", "# Sample file path, for bgen format\n", "parameter: bgen_sample_path = path('.')\n", "# Path to summary stats file\n", @@ -163,13 +163,13 @@ "# Path to summary stats format configuration\n", "parameter: format_config_path = path('.')\n", "# Path to samples of unrelated individuals\n", - "parameter: unrelated_samples = path\n", + "parameter: unrelated_samples = path()\n", + "# imputed Genotype file inventory\n", + "parameter: imp_geno_path = path()\n", + "# Path to summary stats file\n", + "parameter: imp_sumstats_path = path()\n", "# Number of tasks to run in each job on cluster\n", "parameter: job_size = int\n", - "# Number of tasks to run in each job on cluster\n", - "parameter: imp_geno_path = path\n", - "# Path to summary stats file\n", - "parameter: imp_sumstats_path = path\n", "# The reference genome of imputed genotype data\n", "parameter: imp_ref = str\n", "parameter: walltime = '12h'\n", @@ -348,11 +348,14 @@ " \n", " input_format_config = ${format_config_path:r} if ${format_config_path.is_file()} else None\n", "\n", - " \n", + " chrom = \"${_regions[0]}\"\n", " # Load genotype file for the region of interest\n", " geno_inventory = dict([x.strip().split() for x in open(input_geno_path).readlines() if x.strip()])\n", - " imp_geno_inventory = dict([x.strip().split() for x in open(imp_geno_path).readlines() if x.strip()])\n", - " chrom = \"${_regions[0]}\"\n", + " if yml_path.is_file(): \n", + " imp_geno_inventory = dict([x.strip().split() for x in open(imp_geno_path).readlines() if x.strip()])\n", + " else:\n", + " imp_geno_inventory={'0':None,chrom:None}\n", + " \n", " if chrom.startswith('chr'):\n", " chrom = chrom[3:]\n", " if chrom not in geno_inventory:\n", From e2f6d4b82f8c10ff1bb373542039bab1cf1ecb7a Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Thu, 17 Mar 2022 09:27:41 -0400 Subject: [PATCH 23/63] added Gao's comment on the specificaton of regenie_qc --- GWAS/LMM.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/GWAS/LMM.ipynb b/GWAS/LMM.ipynb index 57aec3c..ffca4ee 100644 --- a/GWAS/LMM.ipynb +++ b/GWAS/LMM.ipynb @@ -937,7 +937,9 @@ "kernel": "SoS" }, "source": [ - "Documentation can be found [here](https://rgcgithub.github.io/regenie/). Binary and quantitative traits should be analyzed separately. " + "Documentation can be found [here](https://rgcgithub.github.io/regenie/). Binary and quantitative traits should be analyzed separately. \n", + "\n", + "**step regenie_qc needs a minimum of 20G and 10h to be able to run on the cluster. FIXME: determine this variables in a better way so that they are not hard-coded refer to [PR](https://github.com/cumc/bioworkflows/pull/152)** " ] }, { From d6e29caad7b24cfcc7874c15a903dc3233a27c53 Mon Sep 17 00:00:00 2001 From: yuliu426 Date: Thu, 31 Mar 2022 16:43:40 -0400 Subject: [PATCH 24/63] update GMMAT--add bhat and sbhat --- GWAS/LMM.ipynb | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/GWAS/LMM.ipynb b/GWAS/LMM.ipynb index ffca4ee..acf218c 100644 --- a/GWAS/LMM.ipynb +++ b/GWAS/LMM.ipynb @@ -1452,6 +1452,10 @@ " MAF.range = c(${bgenMinMAF},1-${bgenMinMAF}), \n", " miss.cutoff = ${geno_filter},\n", " nperbatch = ${nperbatch})\n", + " score=read.table('${cwd}/${_input:bn}.${phenoFile:bn}.gmmat.score.txt', header=T)\n", + " score$BHAT=score$SCORE/score$VAR\n", + " score$SBHAT=1/sqrt(score$VAR)\n", + " write.table(score,'${cwd}/${_input:bn}.${phenoFile:bn}.gmmat.score.txt', sep = '\\t', quote = F, col.names = T, row.names = F)\n", "bash: container=container_lmm,expand='${ }', stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout'\n", " gzip ${cwd}/${_input:bn}.${phenoFile:bn}.gmmat.score.txt" ] @@ -1520,7 +1524,7 @@ "#maximum minor allele frequencies of variants\n", "parameter: maf_max_filter = float\n", "output: f'{cwd}/{_input[0]:bn}.smmat.burden.txt.gz'\n", - "task: trunk_workers = 1, trunk_size = 1, walltime = '10h', mem = '120G', cores = 1, tags = f'{step_name}_{_output[0]:bn}'\n", + "task: trunk_workers = 1, trunk_size = 1, walltime = '10h', mem = '12G', cores = 1, tags = f'{step_name}_{_output[0]:bn}'\n", "R: container=container_lmm, expand='${ }', stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout'\n", " library('dplyr')\n", " library('GMMAT')\n", From 147d24ddf08fd4079b2160a78fefd2d0c1cc0fcb Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Mon, 18 Apr 2022 16:23:31 -0400 Subject: [PATCH 25/63] change LDtools to cugg --- GWAS/liftover.ipynb | 24 ++++++++++++++---------- 1 file changed, 14 insertions(+), 10 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 635bc44..566aab2 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -33,7 +33,7 @@ "Make sure you install the pre-requisited before running this notebook:\n", "\n", "```\n", - "pip install LDtoolsets -U\n", + "pip install cugg -U\n", "```" ] }, @@ -155,7 +155,7 @@ "# Input file which can be plink format, gvcf/vcf format, sumstat format.\n", "parameter: input_file = path\n", "# The path of yaml file with input file format, only for sumstat file.\n", - "parameter: yml_file = path('.') \n", + "parameter: yml_file = path() \n", "# the name of ouput file which will be saved under cwd path\n", "parameter: output_file = path\n", "# From reference genome, defaut is hg19\n", @@ -180,15 +180,15 @@ "outputs": [], "source": [ "[default_1 (export utils script)]\n", - "depends: Py_Module('LDtools'), Py_Module('pathlib'),Py_Module('pandas')\n", + "depends: Py_Module('cugg'), Py_Module('pathlib'),Py_Module('pandas')\n", "output: f'{cwd:a}/utils.py'\n", "report: expand = '${ }', output=f'{cwd:a}/utils.py'\n", "\n", " import pandas as pd\n", " from pathlib import Path\n", - " from LDtools.genodata import *\n", - " from LDtools.sumstat import Sumstat\n", - " from LDtools.liftover import Liftover\n", + " from cugg.genodata import *\n", + " from cugg.sumstat import Sumstat\n", + " from cugg.liftover import Liftover\n", " def liftover(input_path,output_path,yml=None,fr='hg19',to='hg38',remove_missing=True,rename=True):\n", " lf = Liftover(fr,to)\n", " print(\"liftover from \" + fr +\" to \" +to)\n", @@ -251,13 +251,17 @@ "output: f'{cwd}/{output_file}'\n", "python: input = f'{cwd:a}/utils.py', expand = '${ }', stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout'\n", " \n", + " \n", + " import os.path\n", " input_path=${_input[0]:r}\n", " output_path=${_output[0]:r}\n", - " fr = '${fr}'\n", - " to = '${to}'\n", + " fr = f'${fr}'\n", + " to = f'${to}'\n", " remove_missing=${remove_missing}\n", " rename = ${rename}\n", - " yml_file = '${yml_file}'\n", + " yml_file = f'${yml_file}'\n", + " if not os.path.isfile(yml_file):\n", + " yml_file = None\n", " print(fr,to,remove_missing)\n", " liftover(input_path,output_path,yml_file,fr,to,remove_missing,rename)" ] @@ -294,7 +298,7 @@ "sos" ] ], - "version": "0.22.6" + "version": "0.22.7" } }, "nbformat": 4, From 27b0f1d44c6c6372fada5a0e262ad3f64793dca3 Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Mon, 18 Apr 2022 16:29:06 -0400 Subject: [PATCH 26/63] update --- GWAS/Region_Extraction.ipynb | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/GWAS/Region_Extraction.ipynb b/GWAS/Region_Extraction.ipynb index 3dc2a27..9fb3530 100644 --- a/GWAS/Region_Extraction.ipynb +++ b/GWAS/Region_Extraction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7a09f4ce", + "id": "feda1754-b5fc-4c37-beb7-4f43b39174fa", "metadata": { "kernel": "SoS" }, @@ -12,7 +12,7 @@ }, { "cell_type": "markdown", - "id": "8259e900", + "id": "2a3789d2-ef07-4513-bcb8-881ba984b967", "metadata": { "kernel": "SoS" }, @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "185fb76b", + "id": "212f861a-0038-4112-a1d3-00b7b40183c3", "metadata": { "kernel": "SoS" }, @@ -32,7 +32,7 @@ }, { "cell_type": "markdown", - "id": "c94dcb53", + "id": "de3fdee2-9312-4d3e-82c6-78c1c52d69d0", "metadata": { "kernel": "SoS" }, @@ -56,7 +56,6 @@ "cell_type": "markdown", "id": "9070e9aa", "metadata": { - "jp-MarkdownHeadingCollapsed": true, "kernel": "SoS", "tags": [] }, @@ -151,11 +150,11 @@ "# Work directory where output will be saved to\n", "parameter: cwd = path\n", "# Region specifications\n", - "parameter: region_file = path()\n", + "parameter: region_file = path\n", "# Genotype file inventory\n", - "parameter: geno_path = path()\n", + "parameter: geno_path = path\n", "# Phenotype path\n", - "parameter: pheno_path = path()\n", + "parameter: pheno_path = path\n", "# Sample file path, for bgen format\n", "parameter: bgen_sample_path = path('.')\n", "# Path to summary stats file\n", From 940c0aa93b12ecd140dc4321468f692871cf313d Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Wed, 20 Apr 2022 15:16:52 -0400 Subject: [PATCH 27/63] update --- GWAS/liftover.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 566aab2..9c8ab05 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -255,11 +255,11 @@ " import os.path\n", " input_path=${_input[0]:r}\n", " output_path=${_output[0]:r}\n", - " fr = f'${fr}'\n", - " to = f'${to}'\n", + " fr = ${fr}\n", + " to = ${to}\n", " remove_missing=${remove_missing}\n", " rename = ${rename}\n", - " yml_file = f'${yml_file}'\n", + " yml_file = ${yml_file}\n", " if not os.path.isfile(yml_file):\n", " yml_file = None\n", " print(fr,to,remove_missing)\n", From f9c2588a499dfd3b4eff7871cdb3ec415e511bd4 Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Fri, 29 Apr 2022 16:43:52 -0400 Subject: [PATCH 28/63] fix path issue --- GWAS/Region_Extraction.ipynb | 36 +++++++++++++++++++++--------------- 1 file changed, 21 insertions(+), 15 deletions(-) diff --git a/GWAS/Region_Extraction.ipynb b/GWAS/Region_Extraction.ipynb index 9fb3530..578d1c9 100644 --- a/GWAS/Region_Extraction.ipynb +++ b/GWAS/Region_Extraction.ipynb @@ -48,7 +48,7 @@ "Make sure you install the pre-requisited before running this notebook:\n", "\n", "```\n", - "pip install LDtoolsets\n", + "pip install cugg\n", "```" ] }, @@ -71,7 +71,7 @@ "- `--pheno-path`, the path of a phenotype. Only for one genotype data. If `None`, only `pld` will be calculated.\n", " - The phenotype file should have a column with the name `IID`, which is used to represent the sample ID.\n", "- `--sumstats-path`, the path of the GWAS file, including all summary statistics (eg, $\\hat{\\beta}$, $SE(\\hat{\\beta})$ and p-values)\n", - " - These summary statistics should contain at least these columns: `chrom, pos, ref, alt, snp_id, bhat, sbhat, p`\n", + " - These summary statistics should contain at least these columns: `CHR,POS,A0,A1,BETA,SE,P`\n", "- `--unrelated-samples`, the file path of unrelated samples with a column named `IID`. If `None`, all samples will be considered unrelative. \n", "- `--cwd`, the path of output directory\n", "\n", @@ -81,7 +81,7 @@ " - The first column is chromosome ID, the 2nd file is genotype for that chromosome.\n", " - When chromosome ID is 0, it implies that the genotype file contains all the genotypes.\n", "- `--imp-sumstats-path`, the path of the GWAS file, including all summary statistics (eg, $\\hat{\\beta}$, $SE(\\hat{\\beta})$ and p-values)\n", - " - These summary statistics should contain at least these columns: `chrom, pos, ref, alt, snp_id, bhat, sbhat, p`\n", + " - These summary statistics should contain at least these columns: `CHR,POS,A0,A1,BETA,SE,P`\n", "- `--imp-ref`, the reference genome if exome genotype and imputed genotype are different. If `None`, The two genotype data will be considered from the same " ] }, @@ -155,12 +155,10 @@ "parameter: geno_path = path\n", "# Phenotype path\n", "parameter: pheno_path = path\n", - "# Sample file path, for bgen format\n", - "parameter: bgen_sample_path = path('.')\n", "# Path to summary stats file\n", "parameter: sumstats_path = path\n", - "# Path to summary stats format configuration\n", - "parameter: format_config_path = path('.')\n", + "# Sample file path, for bgen format\n", + "parameter: bgen_sample_path = path()\n", "# Path to samples of unrelated individuals\n", "parameter: unrelated_samples = path()\n", "# imputed Genotype file inventory\n", @@ -170,7 +168,7 @@ "# Number of tasks to run in each job on cluster\n", "parameter: job_size = int\n", "# The reference genome of imputed genotype data\n", - "parameter: imp_ref = str\n", + "parameter: imp_ref = str('')\n", "parameter: walltime = '12h'\n", "parameter: mem = '60G'\n", "fail_if(not region_file.is_file(), msg = 'Cannot find regions to extract. Please specify them using ``--region-file`` option.')\n", @@ -189,18 +187,18 @@ "outputs": [], "source": [ "[default_1 (export utils script)]\n", - "depends: Py_Module('pandas'), Py_Module('numpy'), Py_Module('dask'), Py_Module('LDtools')\n", + "depends: Py_Module('pandas'), Py_Module('numpy'), Py_Module('dask'), Py_Module('cugg')\n", "parameter: scan_window = 500000\n", "output: f'{cwd:a}/utils.py'\n", "report: expand = '${ }', output=f'{cwd:a}/utils.py'\n", " import pandas as pd\n", " import numpy as np\n", " import dask.array as da\n", - " from LDtools.liftover import *\n", - " from LDtools.genodata import *\n", - " from LDtools.sumstat import *\n", - " from LDtools.ldmatrix import *\n", - " from LDtools.utils import *\n", + " from cugg.liftover import *\n", + " from cugg.genodata import *\n", + " from cugg.sumstat import *\n", + " from cugg.ldmatrix import *\n", + " from cugg.utils import *\n", "\n", "\n", " def main(region,geno_path,sumstats_path,pheno_path,unr_path,imp_geno_path,imp_sumstats_path,imp_ref,output_sumstats,output_LD,bgen_sample_path):\n", @@ -344,16 +342,24 @@ " imp_sumstats_path = ${_input[5]:r}\n", " bgen_sample_path = ${_input[6]:r}\n", " imp_ref = '${imp_ref}'\n", + "\n", + " if not imp_ref:\n", + " imp_ref=None\n", + "\n", + " if not os.path.isfile(bgen_sample_path):\n", + " bgen_sample_path=None\n", + " print('If the genotype data is bgen format, please provide the path of bgen sample')\n", " \n", " input_format_config = ${format_config_path:r} if ${format_config_path.is_file()} else None\n", "\n", " chrom = \"${_regions[0]}\"\n", " # Load genotype file for the region of interest\n", " geno_inventory = dict([x.strip().split() for x in open(input_geno_path).readlines() if x.strip()])\n", - " if yml_path.is_file(): \n", + " if os.path.isfile(imp_geno_path): \n", " imp_geno_inventory = dict([x.strip().split() for x in open(imp_geno_path).readlines() if x.strip()])\n", " else:\n", " imp_geno_inventory={'0':None,chrom:None}\n", + " imp_sumstats_path = None\n", " \n", " if chrom.startswith('chr'):\n", " chrom = chrom[3:]\n", From 9e331e687116be1a42e422edff4a5fdede905c35 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Thu, 12 May 2022 16:20:42 -0400 Subject: [PATCH 29/63] fix path in burden test --- GWAS/LMM.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/GWAS/LMM.ipynb b/GWAS/LMM.ipynb index acf218c..d43b5fe 100644 --- a/GWAS/LMM.ipynb +++ b/GWAS/LMM.ipynb @@ -1651,7 +1651,7 @@ "# Annotation file format: variantID, gene and functional annotation (space/tab delimited)\n", "parameter: anno_file = path\n", "input: snpannofile, anno_file\n", - "output: f'{cwd}/cache/{snpannofile:nn}.subset.csv',\n", + "output: f'{cwd}/cache/{snpannofile:bnn}.subset.csv',\n", " f'{cwd}/cache/non_singleton.genelist',\n", " f'{cwd}/cache/non_duplicated.snplist',\n", " f'{cwd}/cache/{anno_file:bnn}.burden_variants.csv'\n", @@ -2049,7 +2049,7 @@ "parameter: genelist = \"\"\n", "# Annotation file\n", "parameter: anno_file = path\n", - "parameter: snpsomeanno = f'{cwd}/cache/{anno_file:b nn}.burden_variants.csv'\n", + "parameter: snpsomeanno = f'{cwd}/cache/{anno_file:bnn}.burden_variants.csv'\n", "# Select the annotations to be used in the mask file. format: mask# annotatio type\n", "parameter: mask_file = path(\".\")\n", "# Select the upper MAF to generate masks\n", @@ -2226,7 +2226,7 @@ " topgene<-data[data$P<=${plim},] \n", " } \n", " if (${1 if k !=\"\" else 0} ){\n", - " topgene<-data[order(data$P),][1:${k},] \n", + " topgene<-data[order(data$P),][1:as.double(${k}),] \n", " } \n", " \n", " if (${1 if genelist !=\"\" else 0}){\n", From 9fef882f8b9026bd1dd470167f6e835991b5bfcb Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 12 May 2022 20:51:23 -0400 Subject: [PATCH 30/63] Fix the PR #156 --- GWAS/LMM.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/GWAS/LMM.ipynb b/GWAS/LMM.ipynb index d43b5fe..ea2af99 100644 --- a/GWAS/LMM.ipynb +++ b/GWAS/LMM.ipynb @@ -2042,7 +2042,7 @@ "# ylim set to 0 to use maximum -log10(p) in data\n", "parameter: ylim = 0\n", "# Top k genes to be annotated\n", - "parameter: k = \"\"\n", + "parameter: k = 0\n", "# P value limitation for annotation\n", "parameter: plim = 2.5E-6\n", "# A given list of gene for annotation\n", @@ -2225,8 +2225,8 @@ " if (${1 if plim !=\"\" else 0}){\n", " topgene<-data[data$P<=${plim},] \n", " } \n", - " if (${1 if k !=\"\" else 0} ){\n", - " topgene<-data[order(data$P),][1:as.double(${k}),] \n", + " if (${k}>0){\n", + " topgene<-data[order(data$P),][1:${k},] \n", " } \n", " \n", " if (${1 if genelist !=\"\" else 0}){\n", @@ -2416,7 +2416,7 @@ "displayed": true, "height": 0 }, - "version": "0.22.6" + "version": "0.22.4" }, "toc-showcode": false }, From 8deee736b5a235395aca6c051e80a073276f8a08 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Tue, 17 May 2022 16:09:05 -0400 Subject: [PATCH 31/63] Update GATK to latest version --- variant-calling/README.md | 7 +++++++ variant-calling/gatk4-annovar.dockerfile | 22 ++++++++++------------ 2 files changed, 17 insertions(+), 12 deletions(-) diff --git a/variant-calling/README.md b/variant-calling/README.md index a9941eb..68881d0 100644 --- a/variant-calling/README.md +++ b/variant-calling/README.md @@ -4,3 +4,10 @@ To build and upload the docker container for the GATK+ANNOVAR pipeline, docker build --build-arg DUMMY=`date +%s` -t gaow/gatk4-annovar -f gatk4-annovar.dockerfile . docker push gaow/gatk4-annovar ``` + +to build singularity container, + +``` +spython recipe gatk4-annovar.dockerfile | sed 's/Stage: spython-base//g' &> gatk4-annovar.def +singularity build --fakeroot gatk4-annovar.sif gatk4-annovar.def +``` diff --git a/variant-calling/gatk4-annovar.dockerfile b/variant-calling/gatk4-annovar.dockerfile index 0bfed94..8c6ef45 100644 --- a/variant-calling/gatk4-annovar.dockerfile +++ b/variant-calling/gatk4-annovar.dockerfile @@ -1,32 +1,30 @@ -# Add GATK4 to debian-ngs +FROM debian:stable-slim -FROM gaow/debian-ngs:latest - -# :) MAINTAINER Gao Wang, wang.gao@columbia.edu # Install tools WORKDIR /tmp -## https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=863199 -RUN mkdir -p /usr/share/man/man1 + + RUN apt-get update -y \ && apt-get install -qq -y --no-install-recommends \ + curl ca-certificates \ + tabix samtools \ default-jdk python3 python3-matplotlib r-base \ build-essential zlib1g-dev libbz2-dev liblzma-dev \ && apt-get autoclean \ && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log -ENV GATK_VERSION 4.1.6.0 -ADD https://raw.githubusercontent.com/broadinstitute/gatk/${GATK_VERSION}/scripts/docker/gatkbase/install_R_packages.R /opt +ENV GATK_VERSION 4.2.6.1 +ADD https://raw.githubusercontent.com/broadinstitute/gatk/4.1.6.0/scripts/docker/gatkbase/install_R_packages.R /opt +RUN Rscript /opt/install_R_packages.R && rm -rf /tmp/* RUN curl -L \ https://github.com/broadinstitute/gatk/releases/download/${GATK_VERSION}/gatk-${GATK_VERSION}.zip -o gatk.zip \ && unzip gatk.zip \ && mv gatk-${GATK_VERSION} /opt \ && ln -s /opt/gatk-${GATK_VERSION}/gatk /usr/local/bin/gatk \ - && rm -rf /tmp/* -#RUN Rscript /opt/install_R_packages.R && rm -rf /tmp/* + && rm -rf /tmp/gatk.zip RUN ln -s /usr/bin/python3 /usr/local/bin/python -COPY Annovar.tar.gz /tmp -RUN tar zxvf Annovar.tar.gz -C /usr/local/bin && rm -f /tmp/* +RUN curl http://www.openbioinformatics.org/annovar/download/0wgxR2rIVP/annovar.latest.tar.gz -o /tmp/annovar.latest.tar.gz && tar zxvf /tmp/annovar.latest.tar.gz -C /usr/local/bin && rm -f /tmp/annovar.latest.tar.gz # Default command CMD ["bash"] From 362af6492748d5da3b597ccf76e30fad1cd6ceed Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Tue, 17 May 2022 16:12:10 -0400 Subject: [PATCH 32/63] Minor change --- variant-calling/gatk4-annovar.dockerfile | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/variant-calling/gatk4-annovar.dockerfile b/variant-calling/gatk4-annovar.dockerfile index 8c6ef45..66418ac 100644 --- a/variant-calling/gatk4-annovar.dockerfile +++ b/variant-calling/gatk4-annovar.dockerfile @@ -5,7 +5,6 @@ MAINTAINER Gao Wang, wang.gao@columbia.edu # Install tools WORKDIR /tmp - RUN apt-get update -y \ && apt-get install -qq -y --no-install-recommends \ curl ca-certificates \ @@ -14,9 +13,9 @@ RUN apt-get update -y \ build-essential zlib1g-dev libbz2-dev liblzma-dev \ && apt-get autoclean \ && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log -ENV GATK_VERSION 4.2.6.1 ADD https://raw.githubusercontent.com/broadinstitute/gatk/4.1.6.0/scripts/docker/gatkbase/install_R_packages.R /opt RUN Rscript /opt/install_R_packages.R && rm -rf /tmp/* +ENV GATK_VERSION 4.2.6.1 RUN curl -L \ https://github.com/broadinstitute/gatk/releases/download/${GATK_VERSION}/gatk-${GATK_VERSION}.zip -o gatk.zip \ && unzip gatk.zip \ From fba718efb9cd13794aa8671ed2f06ccabd797d3d Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 19 May 2022 17:32:36 -0400 Subject: [PATCH 33/63] Add progress track for LDSC --- ldpred/ldpred.ipynb | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ldpred/ldpred.ipynb b/ldpred/ldpred.ipynb index 6e1d88a..9d8ae14 100644 --- a/ldpred/ldpred.ipynb +++ b/ldpred/ldpred.ipynb @@ -1080,7 +1080,7 @@ " # Open a temporary file\n", " tmp = tempfile(tmpdir = \"${cwd}/ld-cache\")\n", " on.exit(file.remove(paste0(tmp, \".sbk\")), add = TRUE)\n", - " \n", + " print(\"Computing LD matrix\") \n", " for (chr in 1:22) {\n", " # Extract SNPs that are included in the chromosome\n", " ind.chr <- which(info_snp$chr == chr)\n", @@ -1101,12 +1101,13 @@ " corr$add_columns(corr0, nrow(corr))\n", " }\n", " }\n", - " \n", + " print(\"LD matrix computed. Performing LDSC\")\n", " ldsc <- snp_ldsc(ld, \n", " length(ld), \n", " chi2 = (info_snp$beta / info_snp$beta_se)^2,\n", " sample_size = info_snp$n_eff,\n", " blocks = NULL)\n", + " print(\"LDSC completed\")\n", " saveRDS(list(ld=ld,corr=corr,ldsc=ldsc), file = \"${_output}\")" ] }, @@ -1504,7 +1505,7 @@ "displayed": true, "height": 0 }, - "version": "0.22.4" + "version": "0.22.9" }, "toc-autonumbering": false, "toc-showmarkdowntxt": false From 64fde8bf40cf48502ca5304f730bc0753ce8c88e Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Tue, 21 Jun 2022 17:39:55 -0400 Subject: [PATCH 34/63] Add chmod +x to docker image --- variant-calling/gatk4-annovar.dockerfile | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/variant-calling/gatk4-annovar.dockerfile b/variant-calling/gatk4-annovar.dockerfile index 66418ac..25439bb 100644 --- a/variant-calling/gatk4-annovar.dockerfile +++ b/variant-calling/gatk4-annovar.dockerfile @@ -23,7 +23,13 @@ RUN curl -L \ && ln -s /opt/gatk-${GATK_VERSION}/gatk /usr/local/bin/gatk \ && rm -rf /tmp/gatk.zip RUN ln -s /usr/bin/python3 /usr/local/bin/python -RUN curl http://www.openbioinformatics.org/annovar/download/0wgxR2rIVP/annovar.latest.tar.gz -o /tmp/annovar.latest.tar.gz && tar zxvf /tmp/annovar.latest.tar.gz -C /usr/local/bin && rm -f /tmp/annovar.latest.tar.gz +#RUN curl http://www.openbioinformatics.org/annovar/download/0wgxR2rIVP/annovar.latest.tar.gz -o /tmp/annovar.latest.tar.gz +COPY annovar.latest.tar.gz /tmp +RUN tar zxvf /tmp/annovar.latest.tar.gz -C /usr/local/bin && rm -f /tmp/annovar.latest.tar.gz && chmod +x /usr/local/bin/*.pl # Default command CMD ["bash"] + +# To build singularity image out of this: +# spython recipe gatk4-annovar.dockerfile | sed 's/Stage: spython-base//g' &> gatk4-annovar.def +# singularity build --fakeroot gatk4-annovar.sif gatk4-annovar.def From 73307f711b487bb57af676b25ee6ba0dfac5fe14 Mon Sep 17 00:00:00 2001 From: UxxUnet Date: Thu, 23 Jun 2022 15:56:43 -0400 Subject: [PATCH 35/63] Fix the unzip folder for .pl and bugs in install_R_packages.R --- variant-calling/gatk4-annovar.dockerfile | 7 +++-- variant-calling/install_R_packages.R | 34 ++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 2 deletions(-) create mode 100644 variant-calling/install_R_packages.R diff --git a/variant-calling/gatk4-annovar.dockerfile b/variant-calling/gatk4-annovar.dockerfile index 25439bb..9e2bfed 100644 --- a/variant-calling/gatk4-annovar.dockerfile +++ b/variant-calling/gatk4-annovar.dockerfile @@ -13,7 +13,7 @@ RUN apt-get update -y \ build-essential zlib1g-dev libbz2-dev liblzma-dev \ && apt-get autoclean \ && rm -rf /var/lib/apt/lists/* /var/log/dpkg.log -ADD https://raw.githubusercontent.com/broadinstitute/gatk/4.1.6.0/scripts/docker/gatkbase/install_R_packages.R /opt +ADD https://raw.githubusercontent.com/cumc/bioworkflows/master/variant-calling/install_R_packages.R /opt RUN Rscript /opt/install_R_packages.R && rm -rf /tmp/* ENV GATK_VERSION 4.2.6.1 RUN curl -L \ @@ -25,7 +25,10 @@ RUN curl -L \ RUN ln -s /usr/bin/python3 /usr/local/bin/python #RUN curl http://www.openbioinformatics.org/annovar/download/0wgxR2rIVP/annovar.latest.tar.gz -o /tmp/annovar.latest.tar.gz COPY annovar.latest.tar.gz /tmp -RUN tar zxvf /tmp/annovar.latest.tar.gz -C /usr/local/bin && rm -f /tmp/annovar.latest.tar.gz && chmod +x /usr/local/bin/*.pl +RUN tar zxvf /tmp/annovar.latest.tar.gz \ + && mv /tmp/annovar/* /usr/local/bin/ \ + && rm -f /tmp/annovar.latest.tar.gz \ + && chmod +x /usr/local/bin/*.pl # Default command CMD ["bash"] diff --git a/variant-calling/install_R_packages.R b/variant-calling/install_R_packages.R new file mode 100644 index 0000000..06455a2 --- /dev/null +++ b/variant-calling/install_R_packages.R @@ -0,0 +1,34 @@ +############################################################################### +# If you edit this file you MUST release a new version of the gatkbase docker # +# built with the updated r dependencies # +# # +# you MUST also manually clear the travis cache for master before running the # +# pull request tests in order to make sure it's still working # +############################################################################### + +options(warn = 2) # treat warnings as errors, otherwise script can fail silently if a package fails to install + +InstallPackageFromArchive = function(packageName, packageURL) { + # make sure to use http not https as this will give an "unsupported URL scheme" error + if (!(packageName %in% rownames(installed.packages()))) { + install.packages(packageURL, repos = NULL, type = "source", clean = TRUE) + } +} + +dependencies = c("gplots", + "digest", "gtable", "MASS", "plyr", "reshape2", "scales", "tibble", "lazyeval", # for ggplot2 + "tidyselect", "BH", "plogr") # for dplyr +repos <- c("http://cran.r-project.org") +install.packages(dependencies, repos = repos, clean = TRUE) + +InstallPackageFromArchive("getopt", "http://cran.r-project.org/src/contrib/Archive/getopt/getopt_1.20.0.tar.gz") +InstallPackageFromArchive("optparse", "http://cran.r-project.org/src/contrib/Archive/optparse/optparse_1.3.2.tar.gz") +install.packages("data.table") +InstallPackageFromArchive("gsalib", "http://cran.r-project.org/src/contrib/gsalib_2.1.tar.gz") +InstallPackageFromArchive("ggplot2", "http://cran.r-project.org/src/contrib/Archive/ggplot2/ggplot2_2.2.1.tar.gz") +install.packages("dplyr") + +# HMM is only required for testing and not in production: +install.packages("HMM") + +q(save = "no") \ No newline at end of file From 0e214889266e67940ebe8561eafa715ce36cf28f Mon Sep 17 00:00:00 2001 From: UxxUnet Date: Wed, 29 Jun 2022 13:59:27 -0400 Subject: [PATCH 36/63] Update the annotation mapping --- variant-annotation/annovar.ipynb | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/variant-annotation/annovar.ipynb b/variant-annotation/annovar.ipynb index 09da05b..6065601 100644 --- a/variant-annotation/annovar.ipynb +++ b/variant-annotation/annovar.ipynb @@ -642,12 +642,18 @@ " df2[\"Gene\"] = df2.apply(rename_chrom, axis=1)\n", " \n", " # Match annovar annotations with regenie_burden needs \n", - " annotation_mappings = {\"nonsynonymous\":'missense', \"frameshift\":'LoF', \"stopgain\":'LoF', \"stoploss\":'LoF', \"synonymous\":'synonymous'}\n", + " annotation_mappings = {\"nonsynonymous SNV\":'missense', \n", + " \"synonymous SNV\":'synonymous',\n", + " \"frameshift substitution\":'LoF', \n", + " \"stopgain\":'LoF', \n", + " \"stoploss\":'LoF',\n", + " \"startloss\":'LoF',\n", + " \"nonframeshift substitution\":\"inframe\"\n", + " }\n", + "\n", " def annotation(x):\n", - " x = x.strip().split()\n", - " for i in x:\n", - " if i in annotation_mappings.keys():\n", - " return annotation_mappings[i]\n", + " if x in annotation_mappings.keys():\n", + " return annotation_mappings[x]\n", " return 'other'\n", " df2[\"anno_cat\"] = df2[\"ExonicFunc.refGene\"].apply(annotation)\n", " \n", @@ -771,7 +777,7 @@ "sos" ] ], - "version": "0.22.6" + "version": "0.23.3" } }, "nbformat": 4, From 7800069c54fd05170d7562037c9106dcf7265a9e Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Fri, 1 Jul 2022 12:08:48 -0400 Subject: [PATCH 37/63] add the option to keep certain samples when making the GRM --- GWAS/LMM.ipynb | 2 ++ 1 file changed, 2 insertions(+) diff --git a/GWAS/LMM.ipynb b/GWAS/LMM.ipynb index d43b5fe..8ef74af 100644 --- a/GWAS/LMM.ipynb +++ b/GWAS/LMM.ipynb @@ -767,6 +767,7 @@ "source": [ "# Partition the GRM into 100 parts and allocate 8GB memory to each job\n", "[gcta_1]\n", + "parameter: keep_samples = path('.')\n", "# Number of parts the GRM calculation is to be partitioned\n", "parameter: parts = 100\n", "part_number = [f'{parts}_{format(x+1, \"0\" + str(len(str(parts))))}' for x in range(parts)]\n", @@ -779,6 +780,7 @@ " gcta64 \\\n", " --bfile ${_input[0]:n} \\\n", " --make-grm-part ${parts} ${_index+1} \\\n", + " ${(\"--keep \" ) if keep_samples.is_file() else \"\"}\n", " --thread-num ${numThreads} \\\n", " --out ${_output[0]:nnn}" ] From 60b48fcdf3e2031bf81d5e6cb4a88843f5ba0350 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Fri, 1 Jul 2022 13:33:34 -0400 Subject: [PATCH 38/63] fixed bugs in the liftover pipeline --- GWAS/liftover.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 9c8ab05..e633521 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -155,7 +155,7 @@ "# Input file which can be plink format, gvcf/vcf format, sumstat format.\n", "parameter: input_file = path\n", "# The path of yaml file with input file format, only for sumstat file.\n", - "parameter: yml_file = path() \n", + "parameter: yml_file = path('.') \n", "# the name of ouput file which will be saved under cwd path\n", "parameter: output_file = path\n", "# From reference genome, defaut is hg19\n", @@ -167,7 +167,7 @@ "# Rename Variant ID\n", "parameter: rename = True\n", "# Container\n", - "parameter: container = str" + "parameter: container = path('.')" ] }, { @@ -255,11 +255,11 @@ " import os.path\n", " input_path=${_input[0]:r}\n", " output_path=${_output[0]:r}\n", - " fr = ${fr}\n", - " to = ${to}\n", + " fr = \"${fr}\"\n", + " to = \"${to}\"\n", " remove_missing=${remove_missing}\n", " rename = ${rename}\n", - " yml_file = ${yml_file}\n", + " yml_file = \"${yml_file}\"\n", " if not os.path.isfile(yml_file):\n", " yml_file = None\n", " print(fr,to,remove_missing)\n", @@ -298,7 +298,7 @@ "sos" ] ], - "version": "0.22.7" + "version": "0.22.6" } }, "nbformat": 4, From bddc6811898611d56a4b75ae87cbf938321d3dfc Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Tue, 5 Jul 2022 15:27:59 -0400 Subject: [PATCH 39/63] update --- GWAS/liftover.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 9c8ab05..896208b 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -59,7 +59,7 @@ " - if plink format, provide the path of `bim` file \n", " - if gvcf/vcf format, the file must have gvcf and vcf in suffixes\n", " - other format will be considered as sumstat format, whose header should have CHR, POS, A0 and A1 columns\n", - "- `--yml_file`, if the sumstat header doesn't have CHR, POS, A0 and A1 columns, you need to provide a ymal file to describe the format of your file, such as following. the first 5 row is required.\n", + "- `--yml_file`, if the sumstat header doesn't have CHR, POS, A0 and A1 columns, you need to provide a ymal file to describe the format of your file, such as following. the first 5 row is required. **ID is the combindation of key words from the word before `:` in the yml file.**\n", "```\n", "ID: CHR,POS,A0,A1\n", "CHR: CHR\n", @@ -155,7 +155,7 @@ "# Input file which can be plink format, gvcf/vcf format, sumstat format.\n", "parameter: input_file = path\n", "# The path of yaml file with input file format, only for sumstat file.\n", - "parameter: yml_file = path() \n", + "parameter: yml_file = path('.') \n", "# the name of ouput file which will be saved under cwd path\n", "parameter: output_file = path\n", "# From reference genome, defaut is hg19\n", @@ -167,7 +167,7 @@ "# Rename Variant ID\n", "parameter: rename = True\n", "# Container\n", - "parameter: container = str" + "parameter: container = '.'" ] }, { @@ -255,11 +255,11 @@ " import os.path\n", " input_path=${_input[0]:r}\n", " output_path=${_output[0]:r}\n", - " fr = ${fr}\n", - " to = ${to}\n", + " fr = f'${fr}'\n", + " to = f'${to}'\n", " remove_missing=${remove_missing}\n", " rename = ${rename}\n", - " yml_file = ${yml_file}\n", + " yml_file = f'${yml_file}'\n", " if not os.path.isfile(yml_file):\n", " yml_file = None\n", " print(fr,to,remove_missing)\n", From d43ac57bb55b7a4799a833021604c0c9890a38ef Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Wed, 6 Jul 2022 14:25:49 -0400 Subject: [PATCH 40/63] update --- GWAS/liftover.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 896208b..0d50112 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -167,7 +167,7 @@ "# Rename Variant ID\n", "parameter: rename = True\n", "# Container\n", - "parameter: container = '.'" + "parameter: container = ''" ] }, { From 374e4e3972cfdc3bce863f4e55d6595ce64b68b4 Mon Sep 17 00:00:00 2001 From: Yin Huang Date: Thu, 7 Jul 2022 16:24:52 -0400 Subject: [PATCH 41/63] update --- GWAS/liftover.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index e6a85bf..443e38a 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -255,11 +255,11 @@ " import os.path\n", " input_path=${_input[0]:r}\n", " output_path=${_output[0]:r}\n", - " fr = f'${fr}'\n", - " to = f'${to}'\n", + " fr = '${fr}'\n", + " to = '${to}'\n", " remove_missing=${remove_missing}\n", " rename = ${rename}\n", - " yml_file = f'${yml_file}'\n", + " yml_file = '${yml_file}'\n", " if not os.path.isfile(yml_file):\n", " yml_file = None\n", " print(fr,to,remove_missing)\n", @@ -298,7 +298,7 @@ "sos" ] ], - "version": "0.22.6" + "version": "0.22.7" } }, "nbformat": 4, From ba4b304b0540205aaef1c199b0142b75ad1fa88a Mon Sep 17 00:00:00 2001 From: UxxUnet Date: Thu, 7 Jul 2022 16:25:56 -0400 Subject: [PATCH 42/63] Make the annotation mapping more general --- variant-annotation/annovar.ipynb | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/variant-annotation/annovar.ipynb b/variant-annotation/annovar.ipynb index 6065601..4ff64f0 100644 --- a/variant-annotation/annovar.ipynb +++ b/variant-annotation/annovar.ipynb @@ -642,18 +642,19 @@ " df2[\"Gene\"] = df2.apply(rename_chrom, axis=1)\n", " \n", " # Match annovar annotations with regenie_burden needs \n", - " annotation_mappings = {\"nonsynonymous SNV\":'missense', \n", - " \"synonymous SNV\":'synonymous',\n", - " \"frameshift substitution\":'LoF', \n", + " annotation_mappings = {\"nonsynonymous\":'missense', \n", + " \"synonymous\":'synonymous',\n", + " \"frameshift\":'LoF',\n", " \"stopgain\":'LoF', \n", " \"stoploss\":'LoF',\n", " \"startloss\":'LoF',\n", - " \"nonframeshift substitution\":\"inframe\"\n", + " \"nonframeshift\":\"inframe\"\n", " }\n", - "\n", " def annotation(x):\n", - " if x in annotation_mappings.keys():\n", - " return annotation_mappings[x]\n", + " x = x.strip().split()\n", + " for i in x:\n", + " if i in annotation_mappings.keys():\n", + " return annotation_mappings[i]\n", " return 'other'\n", " df2[\"anno_cat\"] = df2[\"ExonicFunc.refGene\"].apply(annotation)\n", " \n", From 905257b71f2b0a927d5d1c5a4b0160c06fbcfff7 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Wed, 13 Jul 2022 13:56:36 -0400 Subject: [PATCH 43/63] fix error handeling indels in avinput forma --- variant-annotation/annovar.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/variant-annotation/annovar.ipynb b/variant-annotation/annovar.ipynb index 4ff64f0..ab9a026 100644 --- a/variant-annotation/annovar.ipynb +++ b/variant-annotation/annovar.ipynb @@ -454,8 +454,8 @@ "task: trunk_workers = 1, walltime = walltime, mem = mem, cores = numThreads, tags = f'{_output:bn}'\n", "bash: expand= \"${ }\", stderr = f'{_output:n}.err', stdout = f'{_output:n}.out' \n", " # $6 ref_allele, $5 alt_allele in the bim files \n", - " # Output as annovar avinput chr, start, end (has to be calculated depending on allele length), reference, alternative\n", - " awk '{if ($6 > $5) {print $1, $4, $4 + (length ($6) - length ($5)), $6, $5, $2} else {print $1, $4, $4, $6, $5, $2}}' ${_input} > ${_output}" + " # Output as annovar avinput chr, start, end (has to be calculated depending on reference allele length), reference, alternative\n", + " awk '{if (length ($6) > 1) {print $1, $4, $4 + (length ($6) - 1), $6, $5, $2} else {print $1, $4, $4, $6, $5, $2}}' ${_input} > ${_output}" ] }, { @@ -524,7 +524,7 @@ " operation = ['g', 'g', 'g', 'g', 'r', 'r', 'f', 'f', 'f', 'f', 'f']\n", " arg = ['\"-splicing 12 -exonicsplicing\"', '\"-splicing 30\"', '\"-splicing 12 -exonicsplicing\"', '\"-splicing 12\"', '', '', '', '', '', '', '']\n", "else:\n", - " protocol = ['refGene', 'refGeneWithVer', 'knownGene', 'ensGene', 'phastConsElements30way', 'encRegTfbsClustered', 'gwasCatalog', 'gnomad30_genome', 'gnomad211_exome', 'gme', 'kaviar_20150923', 'avsnp150', 'dbnsfp41a', 'dbscsnv11', 'clinvar_20200316', 'gene4denovo201907']\n", + " protocol = ['refGene', 'refGeneWithVer', 'knownGene', 'ensGene', 'phastConsElements30way', 'encRegTfbsClustered', 'gwasCatalog', 'gnomad30_genome', 'gnomad211_exome', 'gme', 'kaviar_20150923', 'avsnp150', 'dbnsfp41a', 'dbscsnv11', 'clinvar_20220320', 'gene4denovo201907']\n", " operation = ['g', 'g', 'g', 'gx', 'r', 'r', 'r', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f']\n", " arg = ['\"-splicing 12 -exonicsplicing\"', '\"-splicing 30\"', '\"-splicing 12 -exonicsplicing\"', '\"-splicing 12\"', '', '', '', '', '', '', '', '', '', '', '', '']\n", "\n", @@ -778,7 +778,7 @@ "sos" ] ], - "version": "0.23.3" + "version": "0.22.6" } }, "nbformat": 4, From 3040b1148fb5886e03cf197fb229990c057dd1e1 Mon Sep 17 00:00:00 2001 From: hsun3163 <54919134+hsun3163@users.noreply.github.com> Date: Tue, 18 Oct 2022 18:06:29 -0400 Subject: [PATCH 44/63] Add container to liftover.ipynb --- GWAS/liftover.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/GWAS/liftover.ipynb b/GWAS/liftover.ipynb index 443e38a..9b95f77 100644 --- a/GWAS/liftover.ipynb +++ b/GWAS/liftover.ipynb @@ -249,7 +249,7 @@ "depends: f'{cwd:a}/utils.py'\n", "input: input_file\n", "output: f'{cwd}/{output_file}'\n", - "python: input = f'{cwd:a}/utils.py', expand = '${ }', stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout'\n", + "python: input = f'{cwd:a}/utils.py',container = container, expand = '${ }', stderr = f'{_output[0]:n}.stderr', stdout = f'{_output[0]:n}.stdout'\n", " \n", " \n", " import os.path\n", From 763e83627106f47b18ad8fbfac06aff559f719fc Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Wed, 21 Dec 2022 17:34:00 -0500 Subject: [PATCH 45/63] changes to calling pipeline by Isabelles wishes --- variant-calling/gatk_joint_calling.ipynb | 379 +++++++++++++---------- 1 file changed, 216 insertions(+), 163 deletions(-) diff --git a/variant-calling/gatk_joint_calling.ipynb b/variant-calling/gatk_joint_calling.ipynb index 7ff34fb..2ca9e38 100644 --- a/variant-calling/gatk_joint_calling.ipynb +++ b/variant-calling/gatk_joint_calling.ipynb @@ -78,11 +78,12 @@ "This SoS workflow notebook contains four workflows:\n", "\n", "- `gatk_call`\n", - "- `gatk_filter`\n", - "- `annovar`\n", + "- `gatk_filter_strict`\n", + "- `gatk_filter_basic`\n", + "- `vcf_qc`\n", "- `submit_csg`\n", "\n", - "The first three workflows are for the analysis and the last one is for submitting jobs on the cluster.\n", + "The first four workflows are for the analysis and the last one is for submitting jobs on the cluster.\n", "\n", "All workflow steps are numerically ordered to reflect the execution logic. This is the most straightforward SoS workflow style, the \"process-oriented\" style. " ] @@ -190,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": { "kernel": "Bash" }, @@ -208,17 +209,22 @@ "\n", "Workflows:\n", " call\n", - " filter\n", - " annovar\n", - " submit_csg\n", + " strict_filter\n", + " basic_filter\n", + " vcf_qc\n", "\n", "Global Workflow Options:\n", " --vcf-prefix joint_call_output (as path)\n", " Combined VCF file prefix, including path to the output\n", " but without vcf.gz extension, eg\n", " \"/path/to/output_filename\".\n", + " --cwd VAL (as path, required)\n", + " Working directory\n", " --build hg19\n", " Human genome build\n", + " --vcf-filter strict\n", + " VCF filtering strategy e.x: strict or basic (default is\n", + " strict)\n", " --mem 12 (as int)\n", " Memory allocated to a job, in terms of Gigabyte\n", " --container-option 'gaow/gatk4-annovar'\n", @@ -238,46 +244,27 @@ " Workflow Options:\n", " --ref-genome VAL (as path, required)\n", " Path to reference genome file\n", - " filter_1: Split into SNP and INDEL for separate PASS filters\n", - " filter_2: PASS or filter for indels and SNPs (Note | not\n", + " strict_filter_1: Split into SNP and INDEL for separate PASS filters\n", + " strict_filter_2: PASS or filter for indels and SNPs (Note | not\n", " recommended for filters) Ignore MQRankSum warnings <-\n", " can only be calculated for het sites (not homs)\n", " Workflow Options:\n", " --snp-filters QD < 2.0, QD2 QUAL < 30.0, QUAL30 SOR > 3.0, SOR3 FS > 60.0, FS60 MQ < 40.0, MQ40 MQRankSum < -12.5, MQRankSum-12.5 ReadPosRankSum < -8.0, ReadPosRankSum-8 (as list)\n", " --indel-filters QD < 2.0, QD2 QUAL < 30.0, QUAL30 FS > 200.0, FS200 ReadPosRankSum < -20.0, ReadPosRankSum-20 (as list)\n", - " filter_3: Merge back SNP and INDEL\n", - " filter_4: remove non-PASS variants if wanted\n", - " annovar_1: Annotate\n", + " strict_filter_3: Merge back SNP and INDEL\n", + " strict_filter_4: remove non-PASS variants if wanted\n", + " basic_filter_1: remove all coverage < 4x, strand bias and end of read\n", + " bias\n", " Workflow Options:\n", - " --humandb VAL (as path, required)\n", - " humandb path for ANNOVAR\n", - " --x-ref path(f\"{humandb}/mart_export_2019_LOFtools3.txt\")\n", - "\n", - " add xreffile to option without -exonicsplicing\n", - " mart_export_2019_LOFtools3.txt #xreffile latest option\n", - " -> Phenotype description,HGNC symbol,MIM morbid descript\n", - " ion,CGD_CONDITION,CGD_inh,CGD_man,CGD_comm,LOF_tools\n", - " --protocol refGene refGeneWithVer knownGene ensGene wgEncodeBroadHmmGm12878HMM wgEncodeBroadHmmHmecHMM wgEncodeBroadHmmHepg2HMM wgEncodeBroadHmmH1hescHMM wgEncodeRegDnaseClusteredV3 wgEncodeRegTfbsClusteredV3 genomicSuperDups wgRna targetScanS phastConsElements46way tfbsConsSites gwasCatalog gnomad211_genome gnomad211_exome popfreq_max_20150413 gme kaviar_20150923 abraom avsnp150 dbnsfp35a dbscsnv11 regsnpintron cadd13gt20 clinvar_20200316 mcap13 gene4denovo201907 (as list)\n", - " Annovar protocol\n", - " --operation g g g gx r r r r r r r r r r r r f f f f f f f f f f f f f f (as list)\n", - " Annovar operation\n", - " --arg \"-splicing 12 -exonicsplicing\" \"-splicing 30\" \"-splicing 12 -exonicsplicing\" \"-splicing 12\" (as list)\n", - " Annovar args\n", - " annovar_2: Filter out common variants (from 3 databases) with\n", - " annovar\n", - " Workflow Options:\n", - " --humandb humandb (as path)\n", - " humandb path for ANNOVAR\n", - " --keep 'splic|exonic'\n", - " keep pathogenic: use 'pathogenic|Pathogenic', keep\n", - " splice_exonic: use 'splic|exonic'\n", - " submit_csg: Job submission on CSG cluster\n", - " Workflow Options:\n", - " --cmd-file VAL (as path, required)\n", - " Path to job file\n", - " --time '24:00:00'\n", - " Total run time allocated to the script\n", - " --[no-]dryrun (default to False)\n" + " --ref-genome refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa (as path)\n", + " Path to reference genome file\n", + " --variant-filter QUAL < 30.0 , QUAL30 FS > 200.0, FS200 ReadPosRankSum < -20.0, ReadPosRankSum-20 DP < 4, DP4 (as list)\n", + " basic_filter_2: Remove non-PASS variants\n", + " vcf_qc_1: QC VCF for relatedness\n", + " vcf_qc_2: QC VCF for sex check\n", + " vcf_qc_3: QC VCF for IBD\n", + " vcf_qc_4: QC VCF for relatedness\n", + " vcf_qc_5: QC VCF for homozygosity mapping\n" ] } ], @@ -312,26 +299,41 @@ " --vcf-prefix output/minimal_example \\\n", " --samples /mnt/mfs/statgen/data_private/gatk_joint_call_example/20200820_sample_manifest.txt \\\n", " --samples-dir /mnt/mfs/statgen/data_private/gatk_joint_call_example/ \\\n", - " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa\n", + " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa \\\n", + " --cwd output \\\n", + " --vcf_filter strict\n", "```\n", "\n", - "Filtering:\n", + "Filtering with strict filters:\n", "\n", "```\n", - "sos run gatk_joint_calling.ipynb filter \\\n", + "sos run gatk_joint_calling.ipynb strict_filter \\\n", " --container-option /mnt/mfs/statgen/containers/gatk4-annovar.sif \\\n", - " --vcf-prefix output/minimal_example\n", + " --vcf-prefix output/minimal_example \\\n", + " --cwd output \\\n", + " --vcf_filter strict\n", "```\n", "\n", - "Annotating:\n", + "Filtering with basic filters:\n", "\n", "```\n", - "sos run gatk_joint_calling.ipynb annovar \\\n", + "sos run gatk_joint_calling.ipynb basic_filter \\\n", + " --container-option /mnt/mfs/statgen/containers/gatk4-annovar.sif \\\n", + " --vcf-prefix output/minimal_example \\\n", + " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa\\\n", + " --cwd output \\\n", + " --vcf_filter strict\n", + "```\n", + "\n", + "VCF quality control (sex checks, IBD, heterozygosity, etc):\n", + "\n", + "```\n", + "sos run gatk_joint_calling.ipynb vcf_qc \\\n", " --container-option /mnt/mfs/statgen/containers/gatk4-annovar.sif \\\n", " --vcf-prefix output/minimal_example.snp_indel.filter.PASS \\\n", - " --keep \"splic|exonic\" \\\n", - " --humandb /mnt/mfs/statgen/isabelle/REF/humandb \\\n", - " --x-ref /mnt/mfs/statgen/isabelle/REF/humandb/mart_export_2019_LOFtools3.txt\n", + " --cwd output \\\n", + " --vcf_filter strict\n", + " \n", "```" ] }, @@ -385,8 +387,12 @@ "# Combined VCF file prefix, including path to the output but without vcf.gz extension, \n", "# eg \"/path/to/output_filename\".\n", "parameter: vcf_prefix = path('joint_call_output')\n", + "# Working directory\n", + "parameter: cwd = path\n", "# Human genome build\n", "parameter: build = 'hg19'\n", + "# VCF filtering strategy e.x: strict or basic (default is strict)\n", + "parameter: vcf_filter = 'strict'\n", "# Memory allocated to a job, in terms of Gigabyte\n", "parameter: mem=12\n", "# Software container option\n", @@ -476,6 +482,15 @@ "Since we have two types of variants SNP and Indels, the first two steps of the filter workflow pipeline process the two variant types in parallel, then merge them and do additional filtering wiht steps 3 and 4." ] }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "### Strict filter" + ] + }, { "cell_type": "code", "execution_count": 10, @@ -485,7 +500,7 @@ "outputs": [], "source": [ "# Split into SNP and INDEL for separate PASS filters\n", - "[filter_1]\n", + "[strict_filter_1]\n", "variant_type = ['SNP', 'INDEL']\n", "input: f'{vcf_prefix:a}.vcf.gz', for_each='variant_type', concurrent = True\n", "output: f'{vcf_prefix:a}.{_variant_type.lower()}.vcf.gz'\n", @@ -508,7 +523,7 @@ "source": [ "# PASS or filter for indels and SNPs (Note | not recommended for filters)\n", "# Ignore MQRankSum warnings <- can only be calculated for het sites (not homs)\n", - "[filter_2]\n", + "[strict_filter_2]\n", "parameter: snp_filters = ['QD < 2.0, QD2', 'QUAL < 30.0, QUAL30', 'SOR > 3.0, SOR3', 'FS > 60.0, FS60', 'MQ < 40.0, MQ40', 'MQRankSum < -12.5, MQRankSum-12.5', 'ReadPosRankSum < -8.0, ReadPosRankSum-8']\n", "parameter: indel_filters = [\"QD < 2.0, QD2\", \"QUAL < 30.0, QUAL30\", \"FS > 200.0, FS200\", \"ReadPosRankSum < -20.0, ReadPosRankSum-20\"]\n", "input: paired_with = dict(filter_option=[snp_filters, indel_filters])\n", @@ -531,7 +546,7 @@ "outputs": [], "source": [ "# Merge back SNP and INDEL\n", - "[filter_3]\n", + "[strict_filter_3]\n", "input: group_by = 'all'\n", "output: f'{vcf_prefix:a}.snp_indel.filter.vcf.gz'\n", "\n", @@ -550,8 +565,8 @@ "outputs": [], "source": [ "# remove non-PASS variants if wanted\n", - "[filter_4]\n", - "output: f'{vcf_prefix:a}.snp_indel.filter.PASS.vcf.gz'\n", + "[strict_filter_4]\n", + "output: strict_out= f'{vcf_prefix:a}.snp_indel.filter.strict_QC.PASS.vcf.gz'\n", "\n", "\n", "bash: container=container_option, expand=\"${ }\", stderr=f'{_output:nn}.err', stdout=f'{_output:nn}.out'\n", @@ -566,7 +581,7 @@ "kernel": "SoS" }, "source": [ - "## Annotation" + "### Basic filter" ] }, { @@ -577,61 +592,38 @@ }, "outputs": [], "source": [ - "# convert vcf to annovar input format\n", - "[annovar_1]\n", + "# remove all coverage < 4x, strand bias and end of read bias\n", + "[basic_filter_1]\n", + "# Path to reference genome file\n", + "parameter: ref_genome = path('refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa')\n", + "parameter: variant_filter = ['QUAL < 30.0 , QUAL30', 'FS > 200.0, FS200', 'ReadPosRankSum < -20.0, ReadPosRankSum-20', 'DP < 4, DP4']\n", "input: f'{vcf_prefix:a}.vcf.gz'\n", - "output: f'{_input:nn}.avinput'\n", - "\n", - "bash: container=container_option,expand=\"${ }\", stderr=f'{_output[0]:n}.err', stdout=f'{_output[0]:n}.out'\n", - "\n", - " convert2annovar.pl \\\n", - " -includeinfo \\\n", - " -allsample \\\n", - " -withfreq \\\n", - " -format vcf4 ${_input} > ${_output[0]} " + "output: f'{_input:nn}.snp_indel.filter.basic_QC.vcf.gz'\n", + "bash: container=container_option, expand=\"${ }\", stderr=f'{_output:nn}.err', stdout=f'{_output:nn}.out'\n", + " gatk --java-options '-Xmx${mem}g' VariantFiltration \\\n", + " -R ${ref_genome} \\\n", + " -V ${_input} \\\n", + " ${\" \".join(['-filter \"%s\" --filter-name \"%s\"' % tuple([y.strip() for y in x.split(',')]) for x in variant_filter])} \\\n", + " -O ${_output}\n", + " " ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ - "# Annotate \n", - "[annovar_2]\n", - "# humandb path for ANNOVAR\n", - "parameter: humandb = path\n", - "#add xreffile to option without -exonicsplicing\n", - "#mart_export_2019_LOFtools3.txt #xreffile latest option -> Phenotype description,HGNC symbol,MIM morbid description,CGD_CONDITION,CGD_inh,CGD_man,CGD_comm,LOF_tools\n", - "parameter: x_ref = path(f\"{humandb}/mart_export_2019_LOFtools3.txt\")\n", - "# Annovar protocol\n", - "parameter: protocol = ['refGene', 'refGeneWithVer', 'knownGene', 'ensGene', 'wgEncodeBroadHmmGm12878HMM', 'wgEncodeBroadHmmHmecHMM', 'wgEncodeBroadHmmHepg2HMM', 'wgEncodeBroadHmmH1hescHMM', 'wgEncodeRegDnaseClusteredV3', 'wgEncodeRegTfbsClusteredV3', 'genomicSuperDups', 'wgRna', 'targetScanS', 'phastConsElements46way', 'tfbsConsSites', 'gwasCatalog', 'gnomad211_genome', 'gnomad211_exome', 'popfreq_max_20150413', 'gme', 'kaviar_20150923', 'abraom', 'avsnp150', 'dbnsfp35a', 'dbscsnv11', 'regsnpintron', 'cadd13gt20', 'clinvar_20200316', 'mcap13', 'gene4denovo201907']\n", - "# Annovar operation\n", - "parameter: operation = ['g', 'g', 'g', 'gx', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f', 'f']\n", - "# Annovar args\n", - "parameter: arg = ['\"-splicing 12 -exonicsplicing\"', '\"-splicing 30\"', '\"-splicing 12 -exonicsplicing\"', '\"-splicing 12\"', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']\n", - "input: f'{vcf_prefix:a}.avinput'\n", - "output: f'{vcf_prefix:a}.{build}_multianno.csv'\n", - "\n", - "bash: container=container_option, volumes=[f'{humandb:a}:{humandb:a}', f'{x_ref:ad}:{x_ref:ad}'], expand=\"${ }\", stderr=f'{vcf_prefix:a}.err', stdout=f'{vcf_prefix:a}.out'\n", - " #do not add -intronhgvs as option -> writes cDNA variants as HGVS but creates issues (+2 splice site reported only)\n", - " #-nastring . can only be . for VCF files\n", - " #regsnpintron might cause shifted lines (be carefull using)\n", - " table_annovar.pl \\\n", - " ${_input} \\\n", - " ${humandb} \\\n", - " -buildver ${build} \\\n", - " -out ${_output:nn}\\\n", - " -remove \\\n", - " -polish \\\n", - " -nastring . \\\n", - " -protocol ${\",\".join(protocol)} \\\n", - " -operation ${\",\".join(operation)} \\\n", - " -arg ${\",\".join(arg)} \\\n", - " -csvout \\\n", - " -xreffile ${x_ref}" + "# Remove non-PASS variants\n", + "[basic_filter_2]\n", + "output: basic_out=f'{_input:nn}.PASS.vcf.gz'\n", + "\n", + "bash: container=container_option, expand=\"${ }\", stderr=f'{_output:nn}.err', stdout=f'{_output:nn}.out'\n", + " gatk --java-options '-Xmx${mem}g' SelectVariants \\\n", + " -V ${_input} -O ${_output} \\\n", + " --exclude-filtered" ] }, { @@ -640,64 +632,120 @@ "kernel": "SoS" }, "source": [ - "The step below provides some annotation filtered results. If you want to run your own annotation you can do it by running `ANNOVAR` from the singularity image directly, for example:\n", - "\n", - "```\n", - "singularity exec /mnt/mfs/statgen/containers/gatk4-annovar.sif annotate_variation.pl \\\n", - " -filter -dbtype gnomad211_exome \\\n", - " -build hg19 \\\n", - " -score_threshold 0.005 \\\n", - " minimal_example.snp_indel.filter.PASS.hg19_multianno.exonic_splic.txt \\\n", - " humandb \\\n", - " -out minimal_example.snp_indel.filter.PASS.hg19_multianno.exonic_splic\n", - "```" + "## Extra VCF QC filters" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "kernel": "SoS" }, "outputs": [], "source": [ - "# Filter out common variants (from 3 databases) with annovar \n", - "[annovar_3]\n", - "# humandb path for ANNOVAR\n", - "parameter: humandb = path(\"humandb/\")\n", - "# keep pathogenic: use 'pathogenic|Pathogenic',\n", - "# keep splice_exonic: use 'splic|exonic'\n", - "parameter: keep=\"splic|exonic\"\n", - "tag = '_'.join(sorted(set(keep.lower().split('|'))))\n", - "input: f'{vcf_prefix:a}.vcf.gz'\n", - "output: f'{_input[0]:n}.{tag}.txt', \n", - " f'{_input[0]:n}.{tag}.exome_genome.{build}_popfreq_max_20150413_filtered'\n", - "\n", - "bash: container=container_option, volumes=[f'{humandb:a}:{humandb:a}'], expand=\"${ }\", stderr=f'{_output[0]:n}.err', stdout=f'{_output[0]:n}.out'\n", - " set -e\n", - " awk 'FNR == 1 {print} /${keep}/{print}' ${_input[0]} > ${_output[0]}\n", - " \n", - " annotate_variation.pl -filter -dbtype gnomad211_exome \\\n", - " -build ${build} \\\n", - " -score_threshold 0.005 \\\n", - " ${_output[0]} \\\n", - " ${humandb} \\\n", - " -out ${_output[0]:n}\n", - " \n", - " annotate_variation.pl -filter -dbtype gnomad211_genome \\\n", - " -build ${build} \\\n", - " -score_threshold 0.005 \\\n", - " ${_output[0]:n}.${build}_gnomad211_exome_filtered \\\n", - " ${humandb} \\\n", - " -out ${_output[0]:n}.exome\n", - "\n", - " annotate_variation.pl -filter -dbtype popfreq_max_20150413 \\\n", - " -build ${build} \\\n", - " -score_threshold 0.005 \\\n", - " ${_output[0]:n}.exome.${build}_gnomad211_genome_filtered \\\n", - " ${humandb} \\\n", - " -out ${_output[0]:n}.exome_genome\n", - " rm ${_output[0]:nn}*_dropped" + "# QC VCF for relatedness\n", + "[vcf_qc_1 (check relatedness)]\n", + "input: f\"{vcf_prefix:a}.snp_indel.filter.{vcf_filter}_QC.PASS.vcf.gz\" \n", + "output: f'{cwd}/vcf_qc/{_input:bnn}.relatedness', f'{cwd}/vcf_qc/{_input:bnn}.relatedness2'\n", + "bash: expand=\"${ }\", stderr=f'{cwd}/vcf_qc/{_output[0]:b}.err', stdout=f'{cwd}/vcf_qc/{_output[0]:b}.log'\n", + "\n", + " vcftools --relatedness --gzvcf ${_input} --out ${_output[0]:n}\n", + " vcftools --relatedness2 --gzvcf ${_input} --out ${_output[1]:n}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# QC VCF for sex check\n", + "[vcf_qc_2 (check sex)]\n", + "input: f\"{vcf_prefix:a}.snp_indel.filter.{vcf_filter}_QC.PASS.vcf.gz\"\n", + "output: f\"{cwd}/vcf_qc/{_input:bnn}.bed\", f\"{cwd}/vcf_qc/{_input:bnn}.sex.sexcheck\", f\"{cwd}/vcf_qc/{_input:bnn}.sex2.sexcheck\"\n", + "bash: expand=\"${ }\", stderr=f\"{_output[1]:n}.err\",stdout=f\"{_output[1]:n}.log\"\n", + " plink --vcf ${_input} --double-id --make-bed --out ${_output[0]:n} --allow-extra-chr\n", + " plink --bfile ${_output[0]:n} --check-sex --out ${_output[1]:n} --allow-extra-chr\n", + " plink --bfile ${_output[0]:n} --check-sex 0.35 0.65 --out ${_output[2]:n} --allow-extra-chr\n", + " rm ${_output[1]:n}.nosex && rm ${_output[2]:n}.nosex && rm ${_output[1]:n}.log && rm ${_output[2]:n}.log && rm ${_output[0]:n}.nosex" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# QC VCF for IBD\n", + "[vcf_qc_3 (IBD)]\n", + "input: f\"{cwd}/vcf_qc/{vcf_prefix:b}.snp_indel.filter.{vcf_filter}_QC.PASS.bed\" \n", + "output: f'{_input:n}.IBD.genome',\n", + " f'{_input:n}.HET.het',\n", + " f'{_input:n}.IBC.ibc',\n", + " f'{_input:n}.SEX.2.C.sexcheck',\n", + " vcf=f'{_input:n}.C.VCF.vcf'\n", + "bash: expand=\"${ }\", stderr=f'{_output[0]}.err', stdout=f'{_output[0]}.log'\n", + " #add plink IBD\n", + " #missing rate per SNP MAF and HWE cut-off\n", + " plink --bfile ${_input:n} --geno 0.1 --hwe 0.00001 --maf 0.05 --make-bed --out ${_input:n}.C --allow-extra-chr\n", + " #LD pruning with window size 100 step size 10 and r^2 threshold 0.5 (MAF <0.05)\n", + " plink --bfile ${_input:n}.C --indep-pairwise 50 5 0.5 --make-bed --out ${_input:n}.CP --allow-extra-chr\n", + " #IBD sharing\n", + " plink --bfile ${_input:n}.CP --genome --make-bed --out ${_output[0]:n} --allow-extra-chr\n", + " #het (Inbreeding and absence of heterozygosity)\n", + " plink --bfile ${_input:n}.CP --het --make-bed --out ${_output[1]:n} --allow-extra-chr\n", + " #IBCs (Inbreeding coeff)\n", + " plink --bfile ${_input:n}.CP --ibc --make-bed --out ${_output[2]:n} --allow-extra-chr\n", + " ###cleaned sex\n", + " plink --bfile ${_input:n}.C --check-sex 0.35 0.65 --out ${_output[3]:n} --allow-extra-chr\n", + " ####cleaned relatedness\n", + " plink --bfile ${_input:n}.C --recode vcf --out ${_output[4]:n} --allow-extra-chr\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# QC VCF for relatedness\n", + "[vcf_qc_4 (vcftools)]\n", + "input: named_output(\"vcf\")\n", + "output: f'{_input:n}.C.relatedness', f'{_input:n}.C.2.relatedness2'\n", + "bash: expand=\"${ }\", stderr=f'{_output[0]}.err', stdout=f'{_output[0]}.log'\n", + "\n", + " bgzip ${_input} && tabix -p vcf ${_input}.gz\n", + " vcftools --relatedness --gzvcf ${_input}.gz --out ${_output[0]:n}\n", + " vcftools --relatedness2 --gzvcf ${_input}.gz --out ${_output[1]:n}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# QC VCF for homozygosity mapping \n", + "[vcf_qc_5 (homozygosity mapping)]\n", + "parameter: vcf_filter = 'strict'\n", + "input: f\"{cwd}/vcf_qc/{vcf_prefix:b}.snp_indel.filter.{vcf_filter}_QC.PASS.bed\"\n", + "output: f'{_input:n}.HOM.hom'\n", + "bash: expand=\"${ }\", stderr=f'{_output:nn}.err', stdout=f'{_output:nn}.log'\n", + " ##hom_mapping per sample (at least 100 SNPs, and of total length ≥ 1000 (1Mb) - 0.01 MAF\n", + " plink --bfile ${_input:n} --geno 0.1 --hwe 0.00001 --maf 0.01 --make-bed --out ${_input:n}.CH --allow-extra-chr\n", + " plink --bfile ${_input:n}.CH --homozyg --make-bed --out ${_output:n} --allow-extra-chr\n", + " #remove all the unwanted files at the end\n", + " ##FIXME\n", + " mkdir ${cwd}/vcf_qc/cache\n", + " mv ${cwd}/vcf_qc/*.{bed,bim,fam,log,nosex,in,out,gz,tbi} ${cwd}/vcf_qc/cache\n" ] }, { @@ -718,16 +766,23 @@ " --samples-dir /mnt/mfs/statgen/data_private/gatk_joint_call_example/ \\\n", " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa\n", "\n", - "sos run gatk_joint_calling.ipynb filter \\\n", - " --container-option /mnt/mfs/statgen/containers/gatk4-annovar.sif \\\n", - " --vcf-prefix output/minimal_example\n", + "sos run gatk_joint_calling.ipynb strict_filter \\\n", + " --vcf-prefix output/minimal_example \\\n", + " --cwd output/\\\n", + " --variant_filter 'strict'\n", + " \n", + "sos run gatk_joint_calling.ipynb basic_filter \\\n", + " --vcf-prefix output/minimal_example \\\n", + " --cwd output/\\\n", + " --variant_filter 'basic'\n", "\n", - "sos run gatk_joint_calling.ipynb annovar \\\n", - " --container-option /mnt/mfs/statgen/containers/gatk4-annovar.sif \\\n", - " --vcf-prefix output/minimal_example.snp_indel.filter.PASS \\\n", - " --keep \"splic|exonic\" \\\n", - " --humandb /mnt/mfs/statgen/isabelle/REF/humandb \\\n", - " --x-ref /mnt/mfs/statgen/isabelle/REF/humandb/mart_export_2019_LOFtools3.txt\n", + "module load Singularity\n", + "module load VCFTOOLS/0.1.17\n", + "module load PLINK/1.9.10\n", + "sos run gatk_joint_calling.ipynb vcf_qc \\\n", + " --vcf-prefix output/minimal_example \\\n", + " --cwd output/\\\n", + " --variant_filter 'basic'\n", " \n", " \n", "```\n", @@ -753,12 +808,10 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "raw", "metadata": { "kernel": "SoS" }, - "outputs": [], "source": [ "# Job submission on CSG cluster\n", "[submit_csg]\n", @@ -830,7 +883,7 @@ "height": 0, "style": "side" }, - "version": "0.22.4" + "version": "0.22.6" } }, "nbformat": 4, From 315ae21b4b405adbbc96180f33703bc70ac23e6e Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Tue, 27 Dec 2022 10:56:02 -0500 Subject: [PATCH 46/63] fixed variant calling --- variant-calling/gatk_joint_calling.ipynb | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/variant-calling/gatk_joint_calling.ipynb b/variant-calling/gatk_joint_calling.ipynb index 2ca9e38..ef2fb26 100644 --- a/variant-calling/gatk_joint_calling.ipynb +++ b/variant-calling/gatk_joint_calling.ipynb @@ -764,7 +764,9 @@ " --vcf-prefix output/minimal_example \\\n", " --samples /mnt/mfs/statgen/data_private/gatk_joint_call_example/20200820_sample_manifest.txt \\\n", " --samples-dir /mnt/mfs/statgen/data_private/gatk_joint_call_example/ \\\n", - " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa\n", + " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa\\\n", + " --cwd output\\ \\\n", + " --variant_filter 'strict'\n", "\n", "sos run gatk_joint_calling.ipynb strict_filter \\\n", " --vcf-prefix output/minimal_example \\\n", @@ -793,9 +795,12 @@ "\n", "```\n", "sos run gatk_joint_calling.ipynb submit_csg \\\n", - " --cmd_file command_1027.txt \n", + " --cmd_file command_1027.txt \\\n", + " --cwd output\n", " \n", - "sos run ~/gatk_joint_calling_test.ipynb submit_csg --cmd_file ~/gatk_joint_calling/command_1027.txt \n", + "sos run ~/gatk_joint_calling_test.ipynb submit_csg \\\n", + " --cmd_file ~/gatk_joint_calling/command_1027.txt \\\n", + " --cwd output\n", "```\n", "\n", "\n", @@ -803,15 +808,18 @@ "```\n", "sos run gatk_joint_calling.ipynb submit_csg \\\n", " --cmd_file analysis_commands_20200825.txt \\\n", + " --cwd output \\\n", " --dryrun True\n", "```" ] }, { - "cell_type": "raw", + "cell_type": "code", + "execution_count": null, "metadata": { "kernel": "SoS" }, + "outputs": [], "source": [ "# Job submission on CSG cluster\n", "[submit_csg]\n", From 6c1ebcd749f32141b38b39f3ad8eb282c5346895 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Tue, 27 Dec 2022 11:09:01 -0500 Subject: [PATCH 47/63] fixed variant calling --- variant-calling/gatk_joint_calling.ipynb | 55 +++--------------------- 1 file changed, 5 insertions(+), 50 deletions(-) diff --git a/variant-calling/gatk_joint_calling.ipynb b/variant-calling/gatk_joint_calling.ipynb index ef2fb26..74c5247 100644 --- a/variant-calling/gatk_joint_calling.ipynb +++ b/variant-calling/gatk_joint_calling.ipynb @@ -6,63 +6,18 @@ "kernel": "SoS" }, "source": [ - "# WGS GVCF samples joint calling, filtering and annotation\n", + "# WGS GVCF samples joint calling, filtering and quality control \n", "\n", - "Implementing a GATK + ANNOVAR workflow in [SoS](https://github.com/vatlab/SOS), written by Isabelle Schrauwen with software containers built by Gao Wang. " + "Implementing a GATK + VCF_QC workflow in [SoS](https://github.com/vatlab/SOS), written by Isabelle Schrauwen with software containers built by Gao Wang. " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "kernel": "SoS" }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
RevisionAuthorDateMessage
4762450haoyueshuai2020-08-25update submit_csg
b7958f9Gao Wang2020-08-25Fix a bash variable bug
2986c3cGao Wang2020-08-25Update documentation
c1da803Gao Wang2020-08-25Add job submission template for CSG cluster
69e450aGao Wang2020-08-24Add documentation
3b5a1e8Gao Wang2020-08-24Fix ANNOVAR step
43b3150Gao Wang2020-08-23Update joint variant calling pipeline with minimal working example
2cacdc2Gao Wang2020-08-20Remove the need to mount workdir due to recent changes in SoS
afe343cGao Wang2020-08-20Add variant calling pipeline
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%revisions -s -n 10" ] @@ -134,7 +89,7 @@ "*.dict\n", "```\n", "\n", - "- `ANNOVAR` reference files ship with `ANNOVAR` software, under a folder called `humandb`.\n", + "- `VCF_QC` provides quality control measurementes on the VCF such as sex checks, heterozygosity, and relatedness. \n", "\n", "This workflow assumes that the required files already exit. This pipeline does not provide steps to download or to generate them automatically, which you could find in the tutorial slides. The pipeline will indeed check the availability of the reference files and quit on error if they are missing." ] From d4f00c4d93bfb6ec1814c27c08e5cfff437fdbb8 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Tue, 27 Dec 2022 11:15:20 -0500 Subject: [PATCH 48/63] fixed variant calling --- variant-calling/gatk_joint_calling.ipynb | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/variant-calling/gatk_joint_calling.ipynb b/variant-calling/gatk_joint_calling.ipynb index 74c5247..4c267df 100644 --- a/variant-calling/gatk_joint_calling.ipynb +++ b/variant-calling/gatk_joint_calling.ipynb @@ -117,20 +117,32 @@ " ...\n", "```\n", "\n", - "to run variant filtering, \n", + "to run variant filtering both strict or basic, \n", "\n", "\n", "```\n", - "sos run gatk_joint_calling.ipynb filter \\\n", + "sos run gatk_joint_calling.ipynb filter_strict \\\n", " --vcf-prefix /path/to/some_vcf_file_prefix \\\n", + " --cwd output \\\n", + " --variant_filter strict\n", " ...\n", "```\n", "\n", - "to run annotation,\n", + "```\n", + "sos run gatk_joint_calling.ipynb filter_basic \\\n", + " --vcf-prefix /path/to/some_vcf_file_prefix \\\n", + " --cwd output \\\n", + " --variant_filter basic\n", + " ...\n", + "```\n", + "\n", + "to run vcf_qc,\n", "\n", "```\n", - "sos run gatk_joint_calling.ipynb annovar \\\n", + "sos run gatk_joint_calling.ipynb vcf_qc \\\n", " --vcf-prefix /path/to/some_vcf_file_prefix \\\n", + " --cwd output \\\n", + " --variant_filter basic\n", " ...\n", "```\n", "\n", From 17bbb67960d3f70f5701087a793b00998974d017 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Wed, 28 Dec 2022 10:48:23 -0500 Subject: [PATCH 49/63] add plink and vcftools to the submit script --- variant-calling/gatk_joint_calling.ipynb | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/variant-calling/gatk_joint_calling.ipynb b/variant-calling/gatk_joint_calling.ipynb index 4c267df..a7d2626 100644 --- a/variant-calling/gatk_joint_calling.ipynb +++ b/variant-calling/gatk_joint_calling.ipynb @@ -732,25 +732,22 @@ " --samples /mnt/mfs/statgen/data_private/gatk_joint_call_example/20200820_sample_manifest.txt \\\n", " --samples-dir /mnt/mfs/statgen/data_private/gatk_joint_call_example/ \\\n", " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa\\\n", - " --cwd output\\ \\\n", + " --cwd output/ \\ \n", " --variant_filter 'strict'\n", "\n", "sos run gatk_joint_calling.ipynb strict_filter \\\n", " --vcf-prefix output/minimal_example \\\n", - " --cwd output/\\\n", + " --cwd output/ \\\n", " --variant_filter 'strict'\n", " \n", "sos run gatk_joint_calling.ipynb basic_filter \\\n", " --vcf-prefix output/minimal_example \\\n", - " --cwd output/\\\n", + " --cwd output/ \\\n", " --variant_filter 'basic'\n", "\n", - "module load Singularity\n", - "module load VCFTOOLS/0.1.17\n", - "module load PLINK/1.9.10\n", "sos run gatk_joint_calling.ipynb vcf_qc \\\n", " --vcf-prefix output/minimal_example \\\n", - " --cwd output/\\\n", + " --cwd output/ \\\n", " --variant_filter 'basic'\n", " \n", " \n", @@ -806,6 +803,8 @@ " #$ -j y\n", " #$ -S /bin/bash\n", " module load Singularity\n", + " module load VCFTOOLS/0.1.17\n", + " module load PLINK/1.9.10 \n", " export PATH=$HOME/miniconda3/bin:$PATH\n", " set -e\n", " '''\n", From 65d96c5ce69075dbee6499d1bd774a1a5d0e1db1 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Fri, 30 Dec 2022 10:31:55 -0500 Subject: [PATCH 50/63] fixed vcf-qc_3 --- variant-calling/gatk_joint_calling.ipynb | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/variant-calling/gatk_joint_calling.ipynb b/variant-calling/gatk_joint_calling.ipynb index a7d2626..0f50861 100644 --- a/variant-calling/gatk_joint_calling.ipynb +++ b/variant-calling/gatk_joint_calling.ipynb @@ -631,12 +631,11 @@ "# QC VCF for sex check\n", "[vcf_qc_2 (check sex)]\n", "input: f\"{vcf_prefix:a}.snp_indel.filter.{vcf_filter}_QC.PASS.vcf.gz\"\n", - "output: f\"{cwd}/vcf_qc/{_input:bnn}.bed\", f\"{cwd}/vcf_qc/{_input:bnn}.sex.sexcheck\", f\"{cwd}/vcf_qc/{_input:bnn}.sex2.sexcheck\"\n", - "bash: expand=\"${ }\", stderr=f\"{_output[1]:n}.err\",stdout=f\"{_output[1]:n}.log\"\n", - " plink --vcf ${_input} --double-id --make-bed --out ${_output[0]:n} --allow-extra-chr\n", - " plink --bfile ${_output[0]:n} --check-sex --out ${_output[1]:n} --allow-extra-chr\n", - " plink --bfile ${_output[0]:n} --check-sex 0.35 0.65 --out ${_output[2]:n} --allow-extra-chr\n", - " rm ${_output[1]:n}.nosex && rm ${_output[2]:n}.nosex && rm ${_output[1]:n}.log && rm ${_output[2]:n}.log && rm ${_output[0]:n}.nosex" + "output: bed=f'{cwd}/vcf_qc/{_input:bnn}.bed'\n", + "bash: expand=\"${ }\", stderr=f\"{_output:n}.sex.err\",stdout=f\"{_output:n}.sex.log\"\n", + " plink --vcf ${_input} --double-id --make-bed --out ${_output:n} --allow-extra-chr\n", + " plink --bfile ${_output:n} --check-sex --out ${_output:n}.sex --allow-extra-chr\n", + " plink --bfile ${_output:n} --check-sex 0.35 0.65 --out ${_output:n}.sex2 --allow-extra-chr" ] }, { @@ -649,7 +648,7 @@ "source": [ "# QC VCF for IBD\n", "[vcf_qc_3 (IBD)]\n", - "input: f\"{cwd}/vcf_qc/{vcf_prefix:b}.snp_indel.filter.{vcf_filter}_QC.PASS.bed\" \n", + "input: output_from('vcf_qc_2')['bed']\n", "output: f'{_input:n}.IBD.genome',\n", " f'{_input:n}.HET.het',\n", " f'{_input:n}.IBC.ibc',\n", From 3d3392e97b7cfd957a1e32b0d245d093ad59e7b2 Mon Sep 17 00:00:00 2001 From: dmc2245 Date: Thu, 19 Jan 2023 15:08:21 -0500 Subject: [PATCH 51/63] added csg2 to the notebook --- variant-calling/gatk_joint_calling.ipynb | 50 ++++++++++++++++++++++++ 1 file changed, 50 insertions(+) diff --git a/variant-calling/gatk_joint_calling.ipynb b/variant-calling/gatk_joint_calling.ipynb index 0f50861..990be9f 100644 --- a/variant-calling/gatk_joint_calling.ipynb +++ b/variant-calling/gatk_joint_calling.ipynb @@ -773,6 +773,13 @@ " --cmd_file analysis_commands_20200825.txt \\\n", " --cwd output \\\n", " --dryrun True\n", + "```\n", + "\n", + "```\n", + "sos run gatk_joint_calling.ipynb submit_csg2 \\\n", + " --cmd_file analysis_commands_20200825.txt \\\n", + " --cwd output \\\n", + " --dryrun True\n", "```" ] }, @@ -817,6 +824,49 @@ " if output:\n", " print(output)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# Job submission on CSG cluster\n", + "[submit_csg2]\n", + "# Path to job file\n", + "parameter: cmd_file=path\n", + "# Total run time allocated to the script\n", + "parameter: time='36:00:00'\n", + "parameter: dryrun = False\n", + "input: cmd_file\n", + "python3: expand = '$[ ]'\n", + " tpl = '''\n", + " #!/bin/sh\n", + " #$ -l h_rt=$[time]\n", + " #$ -l h_vmem=$[mem+6]G\n", + " #$ -N gatk_joint_call\n", + " #$ -cwd\n", + " #$ -j y\n", + " #$ -q csg2.q -l t_pri\n", + " #$ -S /bin/bash\n", + " module load Singularity\n", + " module load VCFTOOLS/0.1.17\n", + " module load PLINK/1.9.10 \n", + " export PATH=$HOME/miniconda3/bin:$PATH\n", + " set -e\n", + " '''\n", + " script = tpl.lstrip() + ''.join(open($[_input:r]).readlines())\n", + " exe = 'cat' if $[dryrun] else 'qsub'\n", + " from subprocess import Popen, PIPE\n", + " import sys\n", + " p = Popen(exe, shell = False, stdin = PIPE, stdout = PIPE, stderr = PIPE, close_fds = True)\n", + " for item in p.communicate(script.encode(sys.getdefaultencoding())):\n", + " output = item.decode(sys.getdefaultencoding()).rstrip()\n", + " if output:\n", + " print(output)" + ] } ], "metadata": { From 023e6cff7f5a6b15076db09696b599191c527d1c Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 19 Jan 2023 15:31:21 -0500 Subject: [PATCH 52/63] split submit CSG from the variant calling pipeline --- admin/submit_csg.ipynb | 210 +++++++++++++++++++++++ variant-calling/gatk_joint_calling.ipynb | 159 +---------------- 2 files changed, 211 insertions(+), 158 deletions(-) create mode 100644 admin/submit_csg.ipynb diff --git a/admin/submit_csg.ipynb b/admin/submit_csg.ipynb new file mode 100644 index 0000000..fbf4940 --- /dev/null +++ b/admin/submit_csg.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "# A utility workflow to submit jobs to CSG nodes\n", + "\n", + "This notebook provides a short-cut to submit bash scripts to CSG computing nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "\n", + "Suppose we would like to submit these lines of commands to the cluster:\n", + "\n", + "```\n", + "sos run gatk_joint_calling.ipynb call \\\n", + " --container-option /mnt/mfs/statgen/containers/gatk4-annovar.sif \\\n", + " --vcf-prefix output/minimal_example \\\n", + " --samples /mnt/mfs/statgen/data_private/gatk_joint_call_example/20200820_sample_manifest.txt \\\n", + " --samples-dir /mnt/mfs/statgen/data_private/gatk_joint_call_example/ \\\n", + " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa\\\n", + " --cwd output/ \\ \n", + " --variant_filter 'strict'\n", + "\n", + "sos run gatk_joint_calling.ipynb strict_filter \\\n", + " --vcf-prefix output/minimal_example \\\n", + " --cwd output/ \\\n", + " --variant_filter 'strict'\n", + " \n", + "sos run gatk_joint_calling.ipynb basic_filter \\\n", + " --vcf-prefix output/minimal_example \\\n", + " --cwd output/ \\\n", + " --variant_filter 'basic'\n", + "\n", + "sos run gatk_joint_calling.ipynb vcf_qc \\\n", + " --vcf-prefix output/minimal_example \\\n", + " --cwd output/ \\\n", + " --variant_filter 'basic'\n", + " \n", + " \n", + "```\n", + "\n", + "First, we save the above lines to a text file, e.g. call it `analysis_commands_20200825.txt`, then use the following workflow steps to allocate resources and submit the jobs.\n", + "\n", + "Example to submit a job:\n", + "\n", + "```\n", + "sos run gatk_joint_calling.ipynb submit_csg \\\n", + " --cmd_file command_1027.txt \\\n", + " --cwd output\n", + " \n", + "sos run ~/gatk_joint_calling_test.ipynb submit_csg \\\n", + " --cmd_file ~/gatk_joint_calling/command_1027.txt \\\n", + " --cwd output\n", + "```\n", + "\n", + "\n", + "If you want to run in a dryrun mode, meaning just simply test the process but do not genrate results\n", + "```\n", + "sos run gatk_joint_calling.ipynb submit_csg \\\n", + " --cmd_file analysis_commands_20200825.txt \\\n", + " --cwd output \\\n", + " --dryrun True\n", + "```\n", + "\n", + "```\n", + "sos run gatk_joint_calling.ipynb submit_csg2 \\\n", + " --cmd_file analysis_commands_20200825.txt \\\n", + " --cwd output \\\n", + " --dryrun True\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# Job submission on CSG cluster\n", + "[submit_csg]\n", + "# Path to job file\n", + "parameter: cmd_file=path\n", + "# Total run time allocated to the script\n", + "parameter: time='36:00:00'\n", + "parameter: dryrun = False\n", + "input: cmd_file\n", + "python3: expand = '$[ ]'\n", + " tpl = '''\n", + " #!/bin/sh\n", + " #$ -l h_rt=$[time]\n", + " #$ -l h_vmem=$[mem+6]G\n", + " #$ -N gatk_joint_call\n", + " #$ -cwd\n", + " #$ -j y\n", + " #$ -S /bin/bash\n", + " module load Singularity\n", + " module load VCFTOOLS/0.1.17\n", + " module load PLINK/1.9.10 \n", + " export PATH=$HOME/miniconda3/bin:$PATH\n", + " set -e\n", + " '''\n", + " script = tpl.lstrip() + ''.join(open($[_input:r]).readlines())\n", + " exe = 'cat' if $[dryrun] else 'qsub'\n", + " from subprocess import Popen, PIPE\n", + " import sys\n", + " p = Popen(exe, shell = False, stdin = PIPE, stdout = PIPE, stderr = PIPE, close_fds = True)\n", + " for item in p.communicate(script.encode(sys.getdefaultencoding())):\n", + " output = item.decode(sys.getdefaultencoding()).rstrip()\n", + " if output:\n", + " print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# Job submission on CSG cluster\n", + "[submit_csg2]\n", + "# Path to job file\n", + "parameter: cmd_file=path\n", + "# Total run time allocated to the script\n", + "parameter: time='36:00:00'\n", + "parameter: dryrun = False\n", + "input: cmd_file\n", + "python3: expand = '$[ ]'\n", + " tpl = '''\n", + " #!/bin/sh\n", + " #$ -l h_rt=$[time]\n", + " #$ -l h_vmem=$[mem+6]G\n", + " #$ -N gatk_joint_call\n", + " #$ -cwd\n", + " #$ -j y\n", + " #$ -q csg2.q -l t_pri\n", + " #$ -S /bin/bash\n", + " module load Singularity\n", + " module load VCFTOOLS/0.1.17\n", + " module load PLINK/1.9.10 \n", + " export PATH=$HOME/miniconda3/bin:$PATH\n", + " set -e\n", + " '''\n", + " script = tpl.lstrip() + ''.join(open($[_input:r]).readlines())\n", + " exe = 'cat' if $[dryrun] else 'qsub'\n", + " from subprocess import Popen, PIPE\n", + " import sys\n", + " p = Popen(exe, shell = False, stdin = PIPE, stdout = PIPE, stderr = PIPE, close_fds = True)\n", + " for item in p.communicate(script.encode(sys.getdefaultencoding())):\n", + " output = item.decode(sys.getdefaultencoding()).rstrip()\n", + " if output:\n", + " print(output)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SoS", + "language": "sos", + "name": "sos" + }, + "language_info": { + "codemirror_mode": "sos", + "file_extension": ".sos", + "mimetype": "text/x-sos", + "name": "sos", + "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", + "pygments_lexer": "sos" + }, + "sos": { + "default_kernel": "SoS", + "kernels": [ + [ + "Bash", + "bash", + "Bash", + "#E6EEFF", + "" + ], + [ + "SoS", + "sos", + "", + "", + "sos" + ] + ], + "panel": { + "displayed": true, + "height": 0, + "style": "side" + }, + "version": "0.22.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/variant-calling/gatk_joint_calling.ipynb b/variant-calling/gatk_joint_calling.ipynb index 990be9f..f8a9081 100644 --- a/variant-calling/gatk_joint_calling.ipynb +++ b/variant-calling/gatk_joint_calling.ipynb @@ -36,9 +36,6 @@ "- `gatk_filter_strict`\n", "- `gatk_filter_basic`\n", "- `vcf_qc`\n", - "- `submit_csg`\n", - "\n", - "The first four workflows are for the analysis and the last one is for submitting jobs on the cluster.\n", "\n", "All workflow steps are numerically ordered to reflect the execution logic. This is the most straightforward SoS workflow style, the \"process-oriented\" style. " ] @@ -713,160 +710,6 @@ " mkdir ${cwd}/vcf_qc/cache\n", " mv ${cwd}/vcf_qc/*.{bed,bim,fam,log,nosex,in,out,gz,tbi} ${cwd}/vcf_qc/cache\n" ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Submit jobs to the cluster\n", - "\n", - "Suppose we would like to submit these lines of commands to the cluster:\n", - "\n", - "```\n", - "sos run gatk_joint_calling.ipynb call \\\n", - " --container-option /mnt/mfs/statgen/containers/gatk4-annovar.sif \\\n", - " --vcf-prefix output/minimal_example \\\n", - " --samples /mnt/mfs/statgen/data_private/gatk_joint_call_example/20200820_sample_manifest.txt \\\n", - " --samples-dir /mnt/mfs/statgen/data_private/gatk_joint_call_example/ \\\n", - " --ref-genome /mnt/mfs/statgen/isabelle/REF/refs/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa\\\n", - " --cwd output/ \\ \n", - " --variant_filter 'strict'\n", - "\n", - "sos run gatk_joint_calling.ipynb strict_filter \\\n", - " --vcf-prefix output/minimal_example \\\n", - " --cwd output/ \\\n", - " --variant_filter 'strict'\n", - " \n", - "sos run gatk_joint_calling.ipynb basic_filter \\\n", - " --vcf-prefix output/minimal_example \\\n", - " --cwd output/ \\\n", - " --variant_filter 'basic'\n", - "\n", - "sos run gatk_joint_calling.ipynb vcf_qc \\\n", - " --vcf-prefix output/minimal_example \\\n", - " --cwd output/ \\\n", - " --variant_filter 'basic'\n", - " \n", - " \n", - "```\n", - "\n", - "First, we save the above lines to a text file, e.g. call it `analysis_commands_20200825.txt`, then use the following workflow steps to allocate resources and submit the jobs.\n", - "\n", - "Example to submit a job:\n", - "\n", - "```\n", - "sos run gatk_joint_calling.ipynb submit_csg \\\n", - " --cmd_file command_1027.txt \\\n", - " --cwd output\n", - " \n", - "sos run ~/gatk_joint_calling_test.ipynb submit_csg \\\n", - " --cmd_file ~/gatk_joint_calling/command_1027.txt \\\n", - " --cwd output\n", - "```\n", - "\n", - "\n", - "If you want to run in a dryrun mode, meaning just simply test the process but do not genrate results\n", - "```\n", - "sos run gatk_joint_calling.ipynb submit_csg \\\n", - " --cmd_file analysis_commands_20200825.txt \\\n", - " --cwd output \\\n", - " --dryrun True\n", - "```\n", - "\n", - "```\n", - "sos run gatk_joint_calling.ipynb submit_csg2 \\\n", - " --cmd_file analysis_commands_20200825.txt \\\n", - " --cwd output \\\n", - " --dryrun True\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "# Job submission on CSG cluster\n", - "[submit_csg]\n", - "# Path to job file\n", - "parameter: cmd_file=path\n", - "# Total run time allocated to the script\n", - "parameter: time='36:00:00'\n", - "parameter: dryrun = False\n", - "input: cmd_file\n", - "python3: expand = '$[ ]'\n", - " tpl = '''\n", - " #!/bin/sh\n", - " #$ -l h_rt=$[time]\n", - " #$ -l h_vmem=$[mem+6]G\n", - " #$ -N gatk_joint_call\n", - " #$ -cwd\n", - " #$ -j y\n", - " #$ -S /bin/bash\n", - " module load Singularity\n", - " module load VCFTOOLS/0.1.17\n", - " module load PLINK/1.9.10 \n", - " export PATH=$HOME/miniconda3/bin:$PATH\n", - " set -e\n", - " '''\n", - " script = tpl.lstrip() + ''.join(open($[_input:r]).readlines())\n", - " exe = 'cat' if $[dryrun] else 'qsub'\n", - " from subprocess import Popen, PIPE\n", - " import sys\n", - " p = Popen(exe, shell = False, stdin = PIPE, stdout = PIPE, stderr = PIPE, close_fds = True)\n", - " for item in p.communicate(script.encode(sys.getdefaultencoding())):\n", - " output = item.decode(sys.getdefaultencoding()).rstrip()\n", - " if output:\n", - " print(output)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "# Job submission on CSG cluster\n", - "[submit_csg2]\n", - "# Path to job file\n", - "parameter: cmd_file=path\n", - "# Total run time allocated to the script\n", - "parameter: time='36:00:00'\n", - "parameter: dryrun = False\n", - "input: cmd_file\n", - "python3: expand = '$[ ]'\n", - " tpl = '''\n", - " #!/bin/sh\n", - " #$ -l h_rt=$[time]\n", - " #$ -l h_vmem=$[mem+6]G\n", - " #$ -N gatk_joint_call\n", - " #$ -cwd\n", - " #$ -j y\n", - " #$ -q csg2.q -l t_pri\n", - " #$ -S /bin/bash\n", - " module load Singularity\n", - " module load VCFTOOLS/0.1.17\n", - " module load PLINK/1.9.10 \n", - " export PATH=$HOME/miniconda3/bin:$PATH\n", - " set -e\n", - " '''\n", - " script = tpl.lstrip() + ''.join(open($[_input:r]).readlines())\n", - " exe = 'cat' if $[dryrun] else 'qsub'\n", - " from subprocess import Popen, PIPE\n", - " import sys\n", - " p = Popen(exe, shell = False, stdin = PIPE, stdout = PIPE, stderr = PIPE, close_fds = True)\n", - " for item in p.communicate(script.encode(sys.getdefaultencoding())):\n", - " output = item.decode(sys.getdefaultencoding()).rstrip()\n", - " if output:\n", - " print(output)" - ] } ], "metadata": { @@ -906,7 +749,7 @@ "height": 0, "style": "side" }, - "version": "0.22.6" + "version": "0.22.9" } }, "nbformat": 4, From 8a6a1728e8c150410e12aff2bcc6232e2a2cde44 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 19 Jan 2023 15:33:47 -0500 Subject: [PATCH 53/63] split submit CSG from the variant calling pipeline --- admin/submit_csg.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/admin/submit_csg.ipynb b/admin/submit_csg.ipynb index fbf4940..cdb8fcd 100644 --- a/admin/submit_csg.ipynb +++ b/admin/submit_csg.ipynb @@ -53,11 +53,11 @@ "Example to submit a job:\n", "\n", "```\n", - "sos run gatk_joint_calling.ipynb submit_csg \\\n", + "sos run submit_csg.ipynb submit_csg \\\n", " --cmd_file command_1027.txt \\\n", " --cwd output\n", " \n", - "sos run ~/gatk_joint_calling_test.ipynb submit_csg \\\n", + "sos run submit_csg.ipynb submit_csg \\\n", " --cmd_file ~/gatk_joint_calling/command_1027.txt \\\n", " --cwd output\n", "```\n", @@ -65,14 +65,14 @@ "\n", "If you want to run in a dryrun mode, meaning just simply test the process but do not genrate results\n", "```\n", - "sos run gatk_joint_calling.ipynb submit_csg \\\n", + "sos run submit_csg.ipynb submit_csg \\\n", " --cmd_file analysis_commands_20200825.txt \\\n", " --cwd output \\\n", " --dryrun True\n", "```\n", "\n", "```\n", - "sos run gatk_joint_calling.ipynb submit_csg2 \\\n", + "sos run submit_csg.ipynb submit_csg2 \\\n", " --cmd_file analysis_commands_20200825.txt \\\n", " --cwd output \\\n", " --dryrun True\n", From 04a4bb96f4a3062ae8050a89ea99f9d1c5cc1451 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 19 Jan 2023 16:19:30 -0500 Subject: [PATCH 54/63] Fix typo --- admin/submit_csg.ipynb | 19 ++++++------------- 1 file changed, 6 insertions(+), 13 deletions(-) diff --git a/admin/submit_csg.ipynb b/admin/submit_csg.ipynb index cdb8fcd..a41edf4 100644 --- a/admin/submit_csg.ipynb +++ b/admin/submit_csg.ipynb @@ -54,28 +54,21 @@ "\n", "```\n", "sos run submit_csg.ipynb submit_csg \\\n", - " --cmd_file command_1027.txt \\\n", - " --cwd output\n", - " \n", + " --cmd_file command_1027.txt \n", "sos run submit_csg.ipynb submit_csg \\\n", - " --cmd_file ~/gatk_joint_calling/command_1027.txt \\\n", - " --cwd output\n", + " --cmd_file ~/gatk_joint_calling/command_1027.txt \n", "```\n", "\n", "\n", "If you want to run in a dryrun mode, meaning just simply test the process but do not genrate results\n", "```\n", - "sos run submit_csg.ipynb submit_csg \\\n", - " --cmd_file analysis_commands_20200825.txt \\\n", - " --cwd output \\\n", - " --dryrun True\n", + "sos dryrun submit_csg.ipynb submit_csg \\\n", + " --cmd_file analysis_commands_20200825.txt\n", "```\n", "\n", "```\n", - "sos run submit_csg.ipynb submit_csg2 \\\n", - " --cmd_file analysis_commands_20200825.txt \\\n", - " --cwd output \\\n", - " --dryrun True\n", + "sos dryrun submit_csg.ipynb submit_csg2 \\\n", + " --cmd_file analysis_commands_20200825.txt\n", "```" ] }, From 971d4d700cd1394266abc74ddcf575e0a29004e0 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 19 Jan 2023 16:20:36 -0500 Subject: [PATCH 55/63] Fix typo --- admin/submit_csg.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/admin/submit_csg.ipynb b/admin/submit_csg.ipynb index a41edf4..320a202 100644 --- a/admin/submit_csg.ipynb +++ b/admin/submit_csg.ipynb @@ -128,6 +128,7 @@ "parameter: cmd_file=path\n", "# Total run time allocated to the script\n", "parameter: time='36:00:00'\n", + "parameter: mem=12\n", "parameter: dryrun = False\n", "input: cmd_file\n", "python3: expand = '$[ ]'\n", From aefcb23abb81930ce1887f062918cb0829915c6e Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 19 Jan 2023 16:21:31 -0500 Subject: [PATCH 56/63] Fix typo --- admin/submit_csg.ipynb | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/admin/submit_csg.ipynb b/admin/submit_csg.ipynb index 320a202..b22d3f0 100644 --- a/admin/submit_csg.ipynb +++ b/admin/submit_csg.ipynb @@ -62,13 +62,15 @@ "\n", "If you want to run in a dryrun mode, meaning just simply test the process but do not genrate results\n", "```\n", - "sos dryrun submit_csg.ipynb submit_csg \\\n", - " --cmd_file analysis_commands_20200825.txt\n", + "sos run submit_csg.ipynb submit_csg \\\n", + " --cmd_file analysis_commands_20200825.txt \\\n", + " --dryrun\n", "```\n", "\n", "```\n", - "sos dryrun submit_csg.ipynb submit_csg2 \\\n", - " --cmd_file analysis_commands_20200825.txt\n", + "sos run submit_csg.ipynb submit_csg2 \\\n", + " --cmd_file analysis_commands_20200825.txt \\\n", + " --dryrun\n", "```" ] }, @@ -86,6 +88,7 @@ "parameter: cmd_file=path\n", "# Total run time allocated to the script\n", "parameter: time='36:00:00'\n", + "parameter: mem=12\n", "parameter: dryrun = False\n", "input: cmd_file\n", "python3: expand = '$[ ]'\n", From 8eac9c2f855c19b587b166b7a4491075d63aaee8 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 19 Jan 2023 16:22:32 -0500 Subject: [PATCH 57/63] Fix typo --- admin/submit_csg.ipynb | 2 ++ 1 file changed, 2 insertions(+) diff --git a/admin/submit_csg.ipynb b/admin/submit_csg.ipynb index b22d3f0..d590dc6 100644 --- a/admin/submit_csg.ipynb +++ b/admin/submit_csg.ipynb @@ -88,6 +88,7 @@ "parameter: cmd_file=path\n", "# Total run time allocated to the script\n", "parameter: time='36:00:00'\n", + "# Memory allocated to a job, in terms of Gigabyte\n", "parameter: mem=12\n", "parameter: dryrun = False\n", "input: cmd_file\n", @@ -131,6 +132,7 @@ "parameter: cmd_file=path\n", "# Total run time allocated to the script\n", "parameter: time='36:00:00'\n", + "# Memory allocated to a job, in terms of Gigabyte\n", "parameter: mem=12\n", "parameter: dryrun = False\n", "input: cmd_file\n", From 07256c0d4c9834fd995a57c05a02fc718a68daa2 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 16 Mar 2023 15:39:32 -0400 Subject: [PATCH 58/63] minor changes to toy examples --- admin/Job_Example.ipynb | 2 +- admin/csg.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/admin/Job_Example.ipynb b/admin/Job_Example.ipynb index f9fdcf7..c5f3946 100644 --- a/admin/Job_Example.ipynb +++ b/admin/Job_Example.ipynb @@ -65,7 +65,7 @@ "input: for_each = 'n'\n", "output: f'File_{_n}.out'\n", "task: trunk_workers = 1, trunk_size = job_size, walltime = walltime, mem = mem, cores = ncore, tags = f'{step_name}_{_output:bn}'\n", - "bash: expand = True\n", + "bash: expand = True, stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout'\n", " echo {_n} > {_output}" ] }, diff --git a/admin/csg.yml b/admin/csg.yml index 63c5a30..abbf9b7 100644 --- a/admin/csg.yml +++ b/admin/csg.yml @@ -25,7 +25,7 @@ hosts: #$ -S /bin/bash #{partition} module load Singularity - module load R + module load R/4.2 export PATH=$HOME/miniconda3/bin:$PATH export SINGULARITY_BIND="/mnt/mfs/:/mnt/mfs/" set -e From 715cdf4e483aa25305c0f11c26f21aaa802c6f53 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Thu, 16 Mar 2023 23:52:18 -0400 Subject: [PATCH 59/63] Update job template --- admin/Job_Example.ipynb | 26 +++++++++++++++++--------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/admin/Job_Example.ipynb b/admin/Job_Example.ipynb index c5f3946..d0a6a81 100644 --- a/admin/Job_Example.ipynb +++ b/admin/Job_Example.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "reserved-hampshire", + "id": "serial-reproduction", "metadata": { "kernel": "SoS" }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "occupational-thanksgiving", + "id": "beginning-breach", "metadata": { "kernel": "SoS" }, @@ -24,7 +24,7 @@ }, { "cell_type": "markdown", - "id": "religious-processing", + "id": "tracked-arthritis", "metadata": { "kernel": "SoS" }, @@ -37,7 +37,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dried-spyware", + "id": "broadband-replica", "metadata": { "kernel": "SoS" }, @@ -53,7 +53,7 @@ { "cell_type": "code", "execution_count": null, - "id": "norwegian-vocabulary", + "id": "sudden-cursor", "metadata": { "kernel": "SoS" }, @@ -71,7 +71,7 @@ }, { "cell_type": "markdown", - "id": "characteristic-estate", + "id": "representative-composite", "metadata": { "kernel": "SoS" }, @@ -81,7 +81,7 @@ }, { "cell_type": "markdown", - "id": "integral-storm", + "id": "phantom-coverage", "metadata": { "kernel": "SoS" }, @@ -105,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "equal-accreditation", + "id": "developmental-nashville", "metadata": { "kernel": "SoS" }, @@ -139,7 +139,15 @@ "cat toy_example.log\n", "```\n", "\n", - "At the end of the job you should see exactly the same content as you have seen earlier on the screen when you submit jobs from login node." + "At the end of the job you should see exactly the same content as you have seen earlier on the screen when you submit jobs from login node.\n", + "\n", + "## Highlights on the job configuration template\n", + "\n", + "You can modify the job template file (`*.yml` file) as you see fit. A few places you might want to edit:\n", + "\n", + "1. `max_mem: 128G` is default to 128G. That means even if you specified more than this amount of memory, it is not going to request that much. Typically this default should reflect the maximum available memory from a specific queue of computing nodes. You can increase this value. For example all the nodes in `csg.q` at Columbia Neurology have at least 258G of memory.\n", + "2. `max_running_jobs: 50` is to ensure the job you actually submit to the cluster at the same time, shown in `qstat`, is less than this value. We generally do not want to overflood the queue. But you can adjust this to be a bit higher especially when you notice the cluster is mostly idle and you want to exploit it some more.\n", + "3. Add a new host, either based on existing host or from scratch. For example you can put in a big memory queue with a much larger `max_mem` and also changes to the job template as necessary." ] } ], From 3257b336afe1ec056c1b6a0582300362ed2594b7 Mon Sep 17 00:00:00 2001 From: gaow Date: Fri, 17 Mar 2023 00:28:49 -0400 Subject: [PATCH 60/63] Update csg.yml --- admin/csg.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/admin/csg.yml b/admin/csg.yml index abbf9b7..4c464b9 100644 --- a/admin/csg.yml +++ b/admin/csg.yml @@ -29,6 +29,7 @@ hosts: export PATH=$HOME/miniconda3/bin:$PATH export SINGULARITY_BIND="/mnt/mfs/:/mnt/mfs/" set -e + echo $HOSTNAME >& 2 # to write the compute node name to *.err file sos execute {task} -v {verbosity} -s {sig_mode} kill_cmd: qdel {job_id} max_cores: 40 From fb1a6df1d1583ffbe77dae29aedeb69a6fd2bb93 Mon Sep 17 00:00:00 2001 From: gaow Date: Fri, 17 Mar 2023 08:51:28 -0400 Subject: [PATCH 61/63] Update csg.yml --- admin/csg.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/admin/csg.yml b/admin/csg.yml index 4c464b9..e8e3a17 100644 --- a/admin/csg.yml +++ b/admin/csg.yml @@ -19,8 +19,8 @@ hosts: #$ -l h_vmem={mem//10**9}G #$ -pe {PE} {cores} #$ -N job_{job_name} - #$ -o {cur_dir}/{job_name}.out - #$ -e {cur_dir}/{job_name}.err + #$ -o /home/{user_name}/.sos/{job_name}.out + #$ -e /home/{user_name}/.sos/{job_name}.err #$ -cwd #$ -S /bin/bash #{partition} @@ -29,6 +29,7 @@ hosts: export PATH=$HOME/miniconda3/bin:$PATH export SINGULARITY_BIND="/mnt/mfs/:/mnt/mfs/" set -e + cd {cur_dir} echo $HOSTNAME >& 2 # to write the compute node name to *.err file sos execute {task} -v {verbosity} -s {sig_mode} kill_cmd: qdel {job_id} From cf3c4f66fd4f2d19e93eb88201dd21be73c8bab6 Mon Sep 17 00:00:00 2001 From: gaow Date: Fri, 17 Mar 2023 08:52:49 -0400 Subject: [PATCH 62/63] Update csg.yml --- admin/csg.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/admin/csg.yml b/admin/csg.yml index e8e3a17..e55e196 100644 --- a/admin/csg.yml +++ b/admin/csg.yml @@ -27,7 +27,7 @@ hosts: module load Singularity module load R/4.2 export PATH=$HOME/miniconda3/bin:$PATH - export SINGULARITY_BIND="/mnt/mfs/:/mnt/mfs/" + export SINGULARITY_BIND="/mnt/mfs/:/mnt/mfs/,/mnt/vast/:/mnt/vast/" set -e cd {cur_dir} echo $HOSTNAME >& 2 # to write the compute node name to *.err file From 12af9c8b1addaa53949eaf68918d2f4790b89384 Mon Sep 17 00:00:00 2001 From: Gao Wang Date: Wed, 11 Oct 2023 11:49:16 -0400 Subject: [PATCH 63/63] Purge obsolete mixture prior codes --- .../mash_data_preprocessing.ipynb | 335 +++++++++ multivariate-fine-mapping/mixture_prior.ipynb | 655 ------------------ 2 files changed, 335 insertions(+), 655 deletions(-) create mode 100644 multivariate-fine-mapping/mash_data_preprocessing.ipynb delete mode 100644 multivariate-fine-mapping/mixture_prior.ipynb diff --git a/multivariate-fine-mapping/mash_data_preprocessing.ipynb b/multivariate-fine-mapping/mash_data_preprocessing.ipynb new file mode 100644 index 0000000..377eed1 --- /dev/null +++ b/multivariate-fine-mapping/mash_data_preprocessing.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "# Data munggling for multi-variant summary stats" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Minimal working example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "To see the input requirements and output data formats, please [download a minimal working example here](https://drive.google.com/file/d/1838xUOQuWTszQ0WJGXNiJMszY05cw3RS/view?usp=sharing), and run the following:\n", + "\n", + "### Merge univariate results\n", + "\n", + "```\n", + "sos run mixture_prior.ipynb merge \\\n", + " --analysis-units \\\n", + " --plink-sumstats \\\n", + " --name gtex_mixture\n", + "```\n", + "\n", + "### Select and merge univariate effects\n", + "\n", + "```\n", + "m=/path/to/data\n", + "cd $m && ls *.rds | sed 's/\\.rds//g' > analysis_units.txt && cd -\n", + "sos run mixture_prior.ipynb extract_effects \\\n", + " --analysis-units $m/analysis_units.txt \\\n", + " --datadir $m --name `basename $m`\n", + "```\n", + "\n", + "Notice that for production use, each `sos run` command should be submitted to the cluster as a job." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Global parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "[global]\n", + "import os\n", + "# Work directory & output directory\n", + "parameter: cwd = path('./output')\n", + "# The filename prefix for output data\n", + "parameter: name = str\n", + "parameter: mixture_components = ['flash', 'flash_nonneg', 'pca', 'canonical']\n", + "parameter: job_size = 1# Residual correlatoin file\n", + "parameter: resid_cor = path(\".\")\n", + "fail_if(not (resid_cor.is_file() or resid_cor == path('.')), msg = f'Cannot find ``{resid_cor}``')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Merge PLINK univariate association summary statistic to RDS format" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "[merge]\n", + "parameter: molecular_pheno = path\n", + "# Analysis units file. For RDS files it can be generated by `ls *.rds | sed 's/\\.rds//g' > analysis_units.txt`\n", + "parameter: analysis_units = path\n", + "regions = [x.strip().split() for x in open(analysis_units).readlines() if x.strip() and not x.strip().startswith('#')]\n", + "input: molecular_pheno, for_each = \"regions\"\n", + "output: f'{cwd:a}/RDS/{_regions[0]}'\n", + "\n", + "task: trunk_workers = 1, trunk_size = job_size, walltime = '4h', mem = '6G', tags = f'{step_name}_{_output:bn}' \n", + "\n", + "R: expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n", + " library(\"dplyr\")\n", + " library(\"tibble\")\n", + " library(\"purrr\")\n", + " library(\"readr\")\n", + " molecular_pheno = read_delim($[molecular_pheno:r], delim = \"\\t\")\n", + " molecular_pheno = molecular_pheno%>%mutate(dir = map_chr(`#molc_pheno`,~paste(c(`.x`,\"$[_regions[0]]\"),collapse = \"\")))\n", + " n = nrow(molecular_pheno)\n", + " # For every condition read rds and extract the bhat and sbhat.\n", + " genos = tibble( i = 1:n)\n", + " genos = genos%>%mutate(bhat = map(i, ~readRDS(molecular_pheno[[.x,2]])$bhat%>%as.data.frame%>%rownames_to_column),\n", + " sbhat = map(i, ~readRDS(molecular_pheno[[.x,2]])$sbhat%>%as.data.frame%>%rownames_to_column))\n", + " \n", + " # Join first two conditions\n", + " genos_join_bhat = full_join((genos%>%pull(bhat))[[1]],(genos%>%pull(bhat))[[2]],by = \"rowname\")\n", + " genos_join_sbhat = full_join((genos%>%pull(sbhat))[[1]],(genos%>%pull(sbhat))[[2]],by = \"rowname\")\n", + " \n", + " # If there are more conditions, join the rest\n", + " if(n > 2){\n", + " for(j in 3:n){\n", + " genos_join_bhat = full_join(genos_join_bhat,(genos%>%pull(bhat))[[j]],by = \"rowname\")%>%select(-rowname)%>%as.matrix\n", + " genos_join_sbhat = full_join(genos_join_sbhat,(genos%>%pull(sbhat))[[j]],by = \"rowname\")%>%select(-rowname)%>%as.matrix\n", + " }\n", + " }\n", + " \n", + " name = molecular_pheno%>%mutate(name = map(`#molc_pheno`, ~read.table(text = .x,sep = \"/\")),\n", + " name = map_chr(name, ~.x[,ncol(.x)-2]%>%as.character) )%>%pull(name)\n", + " colnames(genos_join_bhat) = name\n", + " colnames(genos_join_sbhat) = name\n", + " \n", + " \n", + " # save the rds file\n", + " saveRDS(file = \"$[_output]\", list(bhat=genos_join_bhat, sbhat=genos_join_sbhat))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "kernel": "SoS" + }, + "source": [ + "## Get top, random and null effects per analysis unit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "# extract data for MASH from summary stats\n", + "[extract_effects_1]\n", + "parameter: table_name = \"\"\n", + "parameter: bhat = \"bhat\"\n", + "parameter: sbhat = \"sbhat\"\n", + "parameter: expected_ncondition = 0\n", + "parameter: datadir = path\n", + "parameter: seed = 999\n", + "parameter: n_random = 4\n", + "parameter: n_null = 4\n", + "parameter: z_only = True\n", + "# Analysis units file. For RDS files it can be generated by `ls *.rds | sed 's/\\.rds//g' > analysis_units.txt`\n", + "parameter: analysis_units = path\n", + "# handle N = per_chunk data-set in one job\n", + "parameter: per_chunk = 1000\n", + "regions = [x.strip().split() for x in open(analysis_units).readlines() if x.strip() and not x.strip().startswith('#')]\n", + "input: [f'{datadir}/{x[0]}.rds' for x in regions], group_by = per_chunk\n", + "output: f\"{cwd}/{name}/cache/{name}_{_index+1}.rds\"\n", + "task: trunk_workers = 1, walltime = '1h', trunk_size = 1, mem = '4G', cores = 1, tags = f'{_output:bn}'\n", + "R: expand = \"${ }\"\n", + " set.seed(${seed})\n", + " matxMax <- function(mtx) {\n", + " return(arrayInd(which.max(mtx), dim(mtx)))\n", + " }\n", + " remove_rownames = function(x) {\n", + " for (name in names(x)) rownames(x[[name]]) = NULL\n", + " return(x)\n", + " }\n", + " handle_nan_etc = function(x) {\n", + " x$bhat[which(is.nan(x$bhat))] = 0\n", + " x$sbhat[which(is.nan(x$sbhat) | is.infinite(x$sbhat))] = 1E3\n", + " return(x)\n", + " }\n", + " extract_one_data = function(dat, n_random, n_null, infile) {\n", + " if (is.null(dat)) return(NULL)\n", + " z = abs(dat$${bhat}/dat$${sbhat})\n", + " max_idx = matxMax(z)\n", + " # strong effect samples\n", + " strong = list(bhat = dat$${bhat}[max_idx[1],,drop=F], sbhat = dat$${sbhat}[max_idx[1],,drop=F])\n", + " # random samples excluding the top one\n", + " if (max_idx[1] == 1) {\n", + " sample_idx = 2:nrow(z)\n", + " } else if (max_idx[1] == nrow(z)) {\n", + " sample_idx = 1:(max_idx[1]-1)\n", + " } else {\n", + " sample_idx = c(1:(max_idx[1]-1), (max_idx[1]+1):nrow(z))\n", + " }\n", + " random_idx = sample(sample_idx, min(n_random, length(sample_idx)), replace = F)\n", + " random = list(bhat = dat$${bhat}[random_idx,,drop=F], sbhat = dat$${sbhat}[random_idx,,drop=F])\n", + " # null samples defined as |z| < 2\n", + " null.id = which(apply(abs(z), 1, max) < 2)\n", + " if (length(null.id) == 0) {\n", + " warning(paste(\"Null data is empty for input file\", infile))\n", + " null = list()\n", + " } else {\n", + " null_idx = sample(null.id, min(n_null, length(null.id)), replace = F)\n", + " null = list(bhat = dat$${bhat}[null_idx,,drop=F], sbhat = dat$${sbhat}[null_idx,,drop=F])\n", + " }\n", + " dat = (list(random = remove_rownames(random), null = remove_rownames(null), strong = remove_rownames(strong)))\n", + " dat$random = handle_nan_etc(dat$random)\n", + " dat$null = handle_nan_etc(dat$null)\n", + " dat$strong = handle_nan_etc(dat$strong)\n", + " return(dat)\n", + " }\n", + " reformat_data = function(dat, z_only = TRUE) {\n", + " # make output consistent in format with \n", + " # https://github.com/stephenslab/gtexresults/blob/master/workflows/mashr_flashr_workflow.ipynb \n", + " res = list(random.z = dat$random$bhat/dat$random$sbhat, \n", + " strong.z = dat$strong$bhat/dat$strong$sbhat, \n", + " null.z = dat$null$bhat/dat$null$sbhat)\n", + " if (!z_only) {\n", + " res = c(res, list(random.b = dat$random$bhat,\n", + " strong.b = dat$strong$bhat,\n", + " null.b = dat$null$bhat,\n", + " null.s = dat$null$sbhat,\n", + " random.s = dat$random$sbhat,\n", + " strong.s = dat$strong$sbhat))\n", + " }\n", + " return(res)\n", + " }\n", + " merge_data = function(res, one_data) {\n", + " if (length(res) == 0) {\n", + " return(one_data)\n", + " } else if (is.null(one_data)) {\n", + " return(res)\n", + " } else {\n", + " for (d in names(one_data)) {\n", + " if (is.null(one_data[[d]])) {\n", + " next\n", + " } else {\n", + " res[[d]] = rbind(res[[d]], one_data[[d]])\n", + " }\n", + " }\n", + " return(res)\n", + " }\n", + " }\n", + " res = list()\n", + " for (f in c(${_input:r,})) {\n", + " # If cannot read the input for some reason then we just skip it, assuming we have other enough data-sets to use.\n", + " dat = tryCatch(readRDS(f), error = function(e) return(NULL))${(\"$\"+table_name) if table_name != \"\" else \"\"}\n", + " if (is.null(dat)) {\n", + " message(paste(\"Skip loading file\", f, \"due to load failure.\"))\n", + " next\n", + " }\n", + " if (${expected_ncondition} > 0 && (ncol(dat$${bhat}) != ${expected_ncondition} || ncol(dat$${sbhat}) != ${expected_ncondition})) {\n", + " message(paste(\"Skip loading file\", f, \"because it has\", ncol(dat$${bhat}), \"columns different from required\", ${expected_ncondition}))\n", + " next\n", + " }\n", + " res = merge_data(res, reformat_data(extract_one_data(dat, ${n_random}, ${n_null}, f), ${\"TRUE\" if z_only else \"FALSE\"}))\n", + " }\n", + " saveRDS(res, ${_output:r})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "kernel": "SoS" + }, + "outputs": [], + "source": [ + "[extract_effects_2]\n", + "input: group_by = \"all\"\n", + "output: f\"{cwd}/{name}.rds\"\n", + "task: trunk_workers = 1, walltime = '1h', trunk_size = 1, mem = '16G', cores = 1, tags = f'{_output:bn}'\n", + "R: expand = \"${ }\"\n", + " merge_data = function(res, one_data) {\n", + " if (length(res) == 0) {\n", + " return(one_data)\n", + " } else {\n", + " for (d in names(one_data)) {\n", + " res[[d]] = rbind(res[[d]], one_data[[d]])\n", + " }\n", + " return(res)\n", + " }\n", + " }\n", + " dat = list()\n", + " for (f in c(${_input:r,})) {\n", + " dat = merge_data(dat, readRDS(f))\n", + " }\n", + " # compute empirical covariance XtX\n", + " X = dat$strong.z\n", + " X[is.na(X)] = 0\n", + " dat$XtX = t(as.matrix(X)) %*% as.matrix(X) / nrow(X)\n", + " saveRDS(dat, ${_output:r})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SoS", + "language": "sos", + "name": "sos" + }, + "language_info": { + "codemirror_mode": "sos", + "file_extension": ".sos", + "mimetype": "text/x-sos", + "name": "sos", + "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", + "pygments_lexer": "sos" + }, + "sos": { + "kernels": [ + [ + "R" + ], + [ + "SoS" + ] + ], + "version": "0.22.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/multivariate-fine-mapping/mixture_prior.ipynb b/multivariate-fine-mapping/mixture_prior.ipynb deleted file mode 100644 index a242ad4..0000000 --- a/multivariate-fine-mapping/mixture_prior.ipynb +++ /dev/null @@ -1,655 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "# A multivariate EBNM approach for mixture multivariate distribution estimate\n", - "\n", - "An earlier version of the approach is outlined in Urbut et al 2019. This workflow implements a few improvements including using additional EBMF methods as well as the new `udr` package to fit the mixture model." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Overview of approach\n", - "\n", - "1. A workflow step is provided to merge PLINK univariate association analysis results to RDS files for extracting effect estimate samples\n", - "2. Estimated effects are analyzed by FLASH and PCA to extract patterns of sharing\n", - "3. Estimate the weights for patterns extracted from previous step" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Minimal working example" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "To see the input requirements and output data formats, please [download a minimal working example here](https://drive.google.com/file/d/1838xUOQuWTszQ0WJGXNiJMszY05cw3RS/view?usp=sharing), and run the following:\n", - "\n", - "### Merge univariate results\n", - "\n", - "```\n", - "sos run mixture_prior.ipynb merge \\\n", - " --analysis-units \\\n", - " --plink-sumstats \\\n", - " --name gtex_mixture\n", - "```\n", - "\n", - "### Select and merge univariate effects\n", - "\n", - "```\n", - "m=/path/to/data\n", - "cd $m && ls *.rds | sed 's/\\.rds//g' > analysis_units.txt && cd -\n", - "sos run mixture_prior.ipynb extract_effects \\\n", - " --analysis-units $m/analysis_units.txt \\\n", - " --datadir $m --name `basename $m`\n", - "```\n", - "\n", - "### Perform mixture model fitting\n", - "\n", - "```\n", - "sos run mixture_prior.ipynb ud \\\n", - " --datadir $m --name `basename $m` &> ed_$m.log\n", - "sos run mixture_prior.ipynb ud --ud-method ted \\\n", - " --datadir $m --name `basename $m` &> ted_$m.log\n", - "sos run mixture_prior.ipynb ed \\\n", - " --datadir $m --name `basename $m` &> bovy_$m.log\n", - "```\n", - "\n", - "### Plot results\n", - "\n", - "```\n", - "sos run mixture_prior.ipynb plot_U --model-data /path/to/mixture_model.rds --cwd output\n", - "```\n", - "\n", - "Notice that for production use, each `sos run` command should be submitted to the cluster as a job." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Global parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[global]\n", - "import os\n", - "# Work directory & output directory\n", - "parameter: cwd = path('./output')\n", - "# The filename prefix for output data\n", - "parameter: name = str\n", - "parameter: mixture_components = ['flash', 'flash_nonneg', 'pca', 'canonical']\n", - "parameter: job_size = 1# Residual correlatoin file\n", - "parameter: resid_cor = path(\".\")\n", - "fail_if(not (resid_cor.is_file() or resid_cor == path('.')), msg = f'Cannot find ``{resid_cor}``')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Merge PLINK univariate association summary statistic to RDS format" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[merge]\n", - "parameter: molecular_pheno = path\n", - "# Analysis units file. For RDS files it can be generated by `ls *.rds | sed 's/\\.rds//g' > analysis_units.txt`\n", - "parameter: analysis_units = path\n", - "regions = [x.strip().split() for x in open(analysis_units).readlines() if x.strip() and not x.strip().startswith('#')]\n", - "input: molecular_pheno, for_each = \"regions\"\n", - "output: f'{cwd:a}/RDS/{_regions[0]}'\n", - "\n", - "task: trunk_workers = 1, trunk_size = job_size, walltime = '4h', mem = '6G', tags = f'{step_name}_{_output:bn}' \n", - "\n", - "R: expand = \"$[ ]\", stderr = f'{_output}.stderr', stdout = f'{_output}.stdout'\n", - " library(\"dplyr\")\n", - " library(\"tibble\")\n", - " library(\"purrr\")\n", - " library(\"readr\")\n", - " molecular_pheno = read_delim($[molecular_pheno:r], delim = \"\\t\")\n", - " molecular_pheno = molecular_pheno%>%mutate(dir = map_chr(`#molc_pheno`,~paste(c(`.x`,\"$[_regions[0]]\"),collapse = \"\")))\n", - " n = nrow(molecular_pheno)\n", - " # For every condition read rds and extract the bhat and sbhat.\n", - " genos = tibble( i = 1:n)\n", - " genos = genos%>%mutate(bhat = map(i, ~readRDS(molecular_pheno[[.x,2]])$bhat%>%as.data.frame%>%rownames_to_column),\n", - " sbhat = map(i, ~readRDS(molecular_pheno[[.x,2]])$sbhat%>%as.data.frame%>%rownames_to_column))\n", - " \n", - " # Join first two conditions\n", - " genos_join_bhat = full_join((genos%>%pull(bhat))[[1]],(genos%>%pull(bhat))[[2]],by = \"rowname\")\n", - " genos_join_sbhat = full_join((genos%>%pull(sbhat))[[1]],(genos%>%pull(sbhat))[[2]],by = \"rowname\")\n", - " \n", - " # If there are more conditions, join the rest\n", - " if(n > 2){\n", - " for(j in 3:n){\n", - " genos_join_bhat = full_join(genos_join_bhat,(genos%>%pull(bhat))[[j]],by = \"rowname\")%>%select(-rowname)%>%as.matrix\n", - " genos_join_sbhat = full_join(genos_join_sbhat,(genos%>%pull(sbhat))[[j]],by = \"rowname\")%>%select(-rowname)%>%as.matrix\n", - " }\n", - " }\n", - " \n", - " name = molecular_pheno%>%mutate(name = map(`#molc_pheno`, ~read.table(text = .x,sep = \"/\")),\n", - " name = map_chr(name, ~.x[,ncol(.x)-2]%>%as.character) )%>%pull(name)\n", - " colnames(genos_join_bhat) = name\n", - " colnames(genos_join_sbhat) = name\n", - " \n", - " \n", - " # save the rds file\n", - " saveRDS(file = \"$[_output]\", list(bhat=genos_join_bhat, sbhat=genos_join_sbhat))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Get top, random and null effects per analysis unit" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "# extract data for MASH from summary stats\n", - "[extract_effects_1]\n", - "parameter: table_name = \"\"\n", - "parameter: bhat = \"bhat\"\n", - "parameter: sbhat = \"sbhat\"\n", - "parameter: expected_ncondition = 0\n", - "parameter: datadir = path\n", - "parameter: seed = 999\n", - "parameter: n_random = 4\n", - "parameter: n_null = 4\n", - "parameter: z_only = True\n", - "# Analysis units file. For RDS files it can be generated by `ls *.rds | sed 's/\\.rds//g' > analysis_units.txt`\n", - "parameter: analysis_units = path\n", - "# handle N = per_chunk data-set in one job\n", - "parameter: per_chunk = 1000\n", - "regions = [x.strip().split() for x in open(analysis_units).readlines() if x.strip() and not x.strip().startswith('#')]\n", - "input: [f'{datadir}/{x[0]}.rds' for x in regions], group_by = per_chunk\n", - "output: f\"{cwd}/{name}/cache/{name}_{_index+1}.rds\"\n", - "task: trunk_workers = 1, walltime = '1h', trunk_size = 1, mem = '4G', cores = 1, tags = f'{_output:bn}'\n", - "R: expand = \"${ }\"\n", - " set.seed(${seed})\n", - " matxMax <- function(mtx) {\n", - " return(arrayInd(which.max(mtx), dim(mtx)))\n", - " }\n", - " remove_rownames = function(x) {\n", - " for (name in names(x)) rownames(x[[name]]) = NULL\n", - " return(x)\n", - " }\n", - " handle_nan_etc = function(x) {\n", - " x$bhat[which(is.nan(x$bhat))] = 0\n", - " x$sbhat[which(is.nan(x$sbhat) | is.infinite(x$sbhat))] = 1E3\n", - " return(x)\n", - " }\n", - " extract_one_data = function(dat, n_random, n_null, infile) {\n", - " if (is.null(dat)) return(NULL)\n", - " z = abs(dat$${bhat}/dat$${sbhat})\n", - " max_idx = matxMax(z)\n", - " # strong effect samples\n", - " strong = list(bhat = dat$${bhat}[max_idx[1],,drop=F], sbhat = dat$${sbhat}[max_idx[1],,drop=F])\n", - " # random samples excluding the top one\n", - " if (max_idx[1] == 1) {\n", - " sample_idx = 2:nrow(z)\n", - " } else if (max_idx[1] == nrow(z)) {\n", - " sample_idx = 1:(max_idx[1]-1)\n", - " } else {\n", - " sample_idx = c(1:(max_idx[1]-1), (max_idx[1]+1):nrow(z))\n", - " }\n", - " random_idx = sample(sample_idx, min(n_random, length(sample_idx)), replace = F)\n", - " random = list(bhat = dat$${bhat}[random_idx,,drop=F], sbhat = dat$${sbhat}[random_idx,,drop=F])\n", - " # null samples defined as |z| < 2\n", - " null.id = which(apply(abs(z), 1, max) < 2)\n", - " if (length(null.id) == 0) {\n", - " warning(paste(\"Null data is empty for input file\", infile))\n", - " null = list()\n", - " } else {\n", - " null_idx = sample(null.id, min(n_null, length(null.id)), replace = F)\n", - " null = list(bhat = dat$${bhat}[null_idx,,drop=F], sbhat = dat$${sbhat}[null_idx,,drop=F])\n", - " }\n", - " dat = (list(random = remove_rownames(random), null = remove_rownames(null), strong = remove_rownames(strong)))\n", - " dat$random = handle_nan_etc(dat$random)\n", - " dat$null = handle_nan_etc(dat$null)\n", - " dat$strong = handle_nan_etc(dat$strong)\n", - " return(dat)\n", - " }\n", - " reformat_data = function(dat, z_only = TRUE) {\n", - " # make output consistent in format with \n", - " # https://github.com/stephenslab/gtexresults/blob/master/workflows/mashr_flashr_workflow.ipynb \n", - " res = list(random.z = dat$random$bhat/dat$random$sbhat, \n", - " strong.z = dat$strong$bhat/dat$strong$sbhat, \n", - " null.z = dat$null$bhat/dat$null$sbhat)\n", - " if (!z_only) {\n", - " res = c(res, list(random.b = dat$random$bhat,\n", - " strong.b = dat$strong$bhat,\n", - " null.b = dat$null$bhat,\n", - " null.s = dat$null$sbhat,\n", - " random.s = dat$random$sbhat,\n", - " strong.s = dat$strong$sbhat))\n", - " }\n", - " return(res)\n", - " }\n", - " merge_data = function(res, one_data) {\n", - " if (length(res) == 0) {\n", - " return(one_data)\n", - " } else if (is.null(one_data)) {\n", - " return(res)\n", - " } else {\n", - " for (d in names(one_data)) {\n", - " if (is.null(one_data[[d]])) {\n", - " next\n", - " } else {\n", - " res[[d]] = rbind(res[[d]], one_data[[d]])\n", - " }\n", - " }\n", - " return(res)\n", - " }\n", - " }\n", - " res = list()\n", - " for (f in c(${_input:r,})) {\n", - " # If cannot read the input for some reason then we just skip it, assuming we have other enough data-sets to use.\n", - " dat = tryCatch(readRDS(f), error = function(e) return(NULL))${(\"$\"+table_name) if table_name != \"\" else \"\"}\n", - " if (is.null(dat)) {\n", - " message(paste(\"Skip loading file\", f, \"due to load failure.\"))\n", - " next\n", - " }\n", - " if (${expected_ncondition} > 0 && (ncol(dat$${bhat}) != ${expected_ncondition} || ncol(dat$${sbhat}) != ${expected_ncondition})) {\n", - " message(paste(\"Skip loading file\", f, \"because it has\", ncol(dat$${bhat}), \"columns different from required\", ${expected_ncondition}))\n", - " next\n", - " }\n", - " res = merge_data(res, reformat_data(extract_one_data(dat, ${n_random}, ${n_null}, f), ${\"TRUE\" if z_only else \"FALSE\"}))\n", - " }\n", - " saveRDS(res, ${_output:r})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[extract_effects_2]\n", - "input: group_by = \"all\"\n", - "output: f\"{cwd}/{name}.rds\"\n", - "task: trunk_workers = 1, walltime = '1h', trunk_size = 1, mem = '16G', cores = 1, tags = f'{_output:bn}'\n", - "R: expand = \"${ }\"\n", - " merge_data = function(res, one_data) {\n", - " if (length(res) == 0) {\n", - " return(one_data)\n", - " } else {\n", - " for (d in names(one_data)) {\n", - " res[[d]] = rbind(res[[d]], one_data[[d]])\n", - " }\n", - " return(res)\n", - " }\n", - " }\n", - " dat = list()\n", - " for (f in c(${_input:r,})) {\n", - " dat = merge_data(dat, readRDS(f))\n", - " }\n", - " # compute empirical covariance XtX\n", - " X = dat$strong.z\n", - " X[is.na(X)] = 0\n", - " dat$XtX = t(as.matrix(X)) %*% as.matrix(X) / nrow(X)\n", - " saveRDS(dat, ${_output:r})" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Factor analyses" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[flash]\n", - "input: f\"{cwd}/{name}.rds\"\n", - "output: f\"{cwd}/{name}.flash.rds\"\n", - "task: trunk_workers = 1, walltime = '6h', trunk_size = 1, mem = '8G', cores = 2, tags = f'{_output:bn}'\n", - "R: expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout'\n", - " library(\"mashr\")\n", - " bhat = readRDS(${_input:r})$strong.z\n", - " sbhat = bhat\n", - " sbhat[!is.na(sbhat)] = 1\n", - " dat = mashr::mash_set_data(bhat,sbhat)\n", - " res = mashr::cov_flash(dat, factors=\"default\", remove_singleton=${\"TRUE\" if \"canonical\" in mixture_components else \"FALSE\"}, output_model=\"${_output:n}.model.rds\")\n", - " saveRDS(res, ${_output:r})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[flash_nonneg]\n", - "input: f\"{cwd}/{name}.rds\"\n", - "output: f\"{cwd}/{name}.flash_nonneg.rds\"\n", - "task: trunk_workers = 1, walltime = '6h', trunk_size = 1, mem = '8G', cores = 2, tags = f'{_output:bn}'\n", - "R: expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout'\n", - " library(\"mashr\")\n", - " bhat = readRDS(${_input:r})$strong.z\n", - " sbhat = bhat\n", - " sbhat[!is.na(sbhat)] = 1\n", - " dat = mashr::mash_set_data(bhat,sbhat)\n", - " res = mashr::cov_flash(dat, factors=\"nonneg\", remove_singleton=${\"TRUE\" if \"canonical\" in mixture_components else \"FALSE\"}, output_model=\"${_output:n}.model.rds\")\n", - " saveRDS(res, ${_output:r})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[pca]\n", - "parameter: npc = 3\n", - "input: f\"{cwd}/{name}.rds\"\n", - "output: f\"{cwd}/{name}.pca.rds\"\n", - "task: trunk_workers = 1, walltime = '2h', trunk_size = 1, mem = '8G', cores = 2, tags = f'{_output:bn}'\n", - "R: expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout'\n", - " library(\"mashr\")\n", - " bhat = readRDS(${_input:r})$strong.z\n", - " sbhat = bhat\n", - " sbhat[!is.na(sbhat)] = 1\n", - " dat = mashr::mash_set_data(bhat,sbhat)\n", - " res = mashr::cov_pca(dat, ${npc})\n", - " saveRDS(res, ${_output:r})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[canonical]\n", - "input: f\"{cwd}/{name}.rds\"\n", - "output: f\"{cwd}/{name}.canonical.rds\"\n", - "task: trunk_workers = 1, walltime = '1h', trunk_size = 1, mem = '8G', cores = 1, tags = f'{_output:bn}'\n", - "R: expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout'\n", - " library(\"mashr\")\n", - " bhat = readRDS(${_input:r})$strong.z\n", - " sbhat = bhat\n", - " sbhat[!is.na(sbhat)] = 1\n", - " dat = mashr::mash_set_data(bhat,sbhat)\n", - " res = mashr::cov_canonical(dat)\n", - " saveRDS(res, ${_output:r})" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Fit mixture model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "# Installed commit d6d4c0e\n", - "[ud]\n", - "# Method is `ed` or `ted`\n", - "parameter: ud_method = \"ed\"\n", - "# A list of models where we only update the scales and not the matrices\n", - "# A typical choice is to estimate scales only for canonical components\n", - "parameter: scale_only = []\n", - "# Tolerance for change in likelihood\n", - "parameter: ud_tol_lik = 1e-3\n", - "input: [f\"{cwd}/{name}.rds\"] + [f\"{cwd}/{name}.{m}.rds\" for m in mixture_components]\n", - "output: f'{cwd}/{name}.{ud_method}{\"_unconstrained\" if len(scale_only) == 0 else \"\"}{(\".\" + os.path.basename(resid_cor)[:-4]) if resid_cor.is_file() else \"\"}.rds'\n", - "task: trunk_workers = 1, walltime = '36h', trunk_size = 1, mem = '10G', cores = 4, tags = f'{_output:bn}'\n", - "R: expand = \"${ }\", stderr = f\"{_output:n}.stderr\", stdout = f\"{_output:n}.stdout\"\n", - " library(stringr)\n", - " rds_files = c(${_input:r,})\n", - " dat = readRDS(rds_files[1])\n", - " U = list(XtX = dat$XtX)\n", - " U_scaled = list()\n", - " mixture_components = c(${paths(mixture_components):r,})\n", - " scale_only = c(${paths(scale_only):r,})\n", - " scale_idx = which(mixture_components %in% scale_only )\n", - " for (f in 2:length(rds_files) ) {\n", - " if ((f - 1) %in% scale_idx ) {\n", - " U_scaled = c(U_scaled, readRDS(rds_files[f]))\n", - " } else {\n", - " U = c(U, readRDS(rds_files[f]))\n", - " }\n", - " }\n", - " #\n", - " if (${\"TRUE\" if resid_cor.is_file() else \"FALSE\"}) {\n", - " V = readRDS(${resid_cor:r})\n", - " } else {\n", - " V = cor(dat$null.z)\n", - " }\n", - " # Fit mixture model using udr package\n", - " library(udr)\n", - " message(paste(\"Running ${ud_method.upper()} via udr package for\", length(U), \"mixture components\"))\n", - " f0 = ud_init(X = as.matrix(dat$strong.z), V = V, U_scaled = U_scaled, U_unconstrained = U, n_rank1=0)\n", - " res = ud_fit(f0, X = na.omit(f0$X), control = list(unconstrained.update = \"${ud_method}\", resid.update = 'none', scaled.update = \"fa\", maxiter=5000, tol.lik = ${ud_tol_lik}), verbose=TRUE)\n", - " saveRDS(list(U=res$U, w=res$w, loglik=res$loglik), ${_output:r})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[ed]\n", - "parameter: ed_tol = 1e-6\n", - "input: [f\"{cwd}/{name}.rds\"] + [f\"{cwd}/{name}.{m}.rds\" for m in mixture_components]\n", - "output: f'{cwd}/{name}.ed_bovy{(\".\" + os.path.basename(resid_cor)[:-4]) if resid_cor.is_file() else \"\"}.rds'\n", - "task: trunk_workers = 1, walltime = '36h', trunk_size = 1, mem = '10G', cores = 4, tags = f'{_output:bn}'\n", - "R: expand = \"${ }\", stderr = f\"{_output:n}.stderr\", stdout = f\"{_output:n}.stdout\"\n", - " rds_files = c(${_input:r,})\n", - " dat = readRDS(rds_files[1])\n", - " U = list(XtX = dat$XtX)\n", - " for (f in rds_files[2:length(rds_files)]) U = c(U, readRDS(f))\n", - " if (${\"TRUE\" if resid_cor.is_file() else \"FALSE\"}) {\n", - " V = readRDS(${resid_cor:r})\n", - " } else {\n", - " V = cor(dat$null.z)\n", - " }\n", - " # Fit mixture model using ED code by J. Bovy\n", - " mash_data = mashr::mash_set_data(dat$strong.z, V=V)\n", - " message(paste(\"Running ED via J. Bovy's code for\", length(U), \"mixture components\"))\n", - " res = mashr:::bovy_wrapper(mash_data, U, logfile=${_output:nr}, tol = ${ed_tol})\n", - " saveRDS(list(U=res$Ulist, w=res$pi, loglik=scan(\"${_output:n}_loglike.log\")), ${_output:r})" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "kernel": "SoS" - }, - "source": [ - "## Plot patterns of sharing\n", - "\n", - "This is a simple utility function that takes the output from the pipeline above and make some heatmap to show major patterns of multivariate effects. The plots will be ordered by their mixture weights." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "kernel": "SoS" - }, - "outputs": [], - "source": [ - "[plot_U]\n", - "parameter: model_data = path\n", - "# number of components to show\n", - "parameter: max_comp = -1\n", - "# whether or not to convert to correlation\n", - "parameter: to_cor = False\n", - "parameter: tol = \"1E-6\"\n", - "parameter: remove_label = False\n", - "parameter: name = \"\"\n", - "input: model_data\n", - "output: f'{cwd:a}/{_input:bn}{(\"_\" + name.replace(\"$\", \"_\")) if name != \"\" else \"\"}.pdf'\n", - "R: expand = \"${ }\", stderr = f'{_output:n}.stderr', stdout = f'{_output:n}.stdout'\n", - " library(reshape2)\n", - " library(ggplot2)\n", - " plot_sharing = function(X, col = 'black', to_cor=FALSE, title=\"\", remove_names=F) {\n", - " clrs <- colorRampPalette(rev(c(\"#D73027\",\"#FC8D59\",\"#FEE090\",\"#FFFFBF\",\n", - " \"#E0F3F8\",\"#91BFDB\",\"#4575B4\")))(128)\n", - " if (to_cor) lat <- cov2cor(X)\n", - " else lat = X/max(diag(X))\n", - " lat[lower.tri(lat)] <- NA\n", - " n <- nrow(lat)\n", - " if (remove_names) {\n", - " colnames(lat) = paste('t',1:n, sep = '')\n", - " rownames(lat) = paste('t',1:n, sep = '')\n", - " }\n", - " melted_cormat <- melt(lat[n:1,], na.rm = TRUE)\n", - " p = ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+\n", - " geom_tile(color = \"white\")+ggtitle(title) + \n", - " scale_fill_gradientn(colors = clrs, limit = c(-1,1), space = \"Lab\") +\n", - " theme_minimal()+ \n", - " coord_fixed() +\n", - " theme(axis.title.x = element_blank(),\n", - " axis.title.y = element_blank(),\n", - " axis.text.x = element_text(color=col, size=8,angle=45,hjust=1),\n", - " axis.text.y = element_text(color=rev(col), size=8),\n", - " title =element_text(size=10),\n", - " # panel.grid.major = element_blank(),\n", - " panel.border = element_blank(),\n", - " panel.background = element_blank(),\n", - " axis.ticks = element_blank(),\n", - " legend.justification = c(1, 0),\n", - " legend.position = c(0.6, 0),\n", - " legend.direction = \"horizontal\")+\n", - " guides(fill = guide_colorbar(title=\"\", barwidth = 7, barheight = 1,\n", - " title.position = \"top\", title.hjust = 0.5))\n", - " if(remove_names){\n", - " p = p + scale_x_discrete(labels= 1:n) + scale_y_discrete(labels= n:1)\n", - " }\n", - " return(p)\n", - " }\n", - " \n", - " dat = readRDS(${_input:r})\n", - " name = \"${name}\"\n", - " if (name != \"\") {\n", - " if (is.null(dat[[name]])) stop(\"Cannot find data ${name} in ${_input}\")\n", - " dat = dat[[name]]\n", - " }\n", - " if (is.null(names(dat$U))) names(dat$U) = paste0(\"Comp_\", 1:length(dat$U))\n", - " meta = data.frame(names(dat$U), dat$w, stringsAsFactors=F)\n", - " colnames(meta) = c(\"U\", \"w\")\n", - " tol = ${tol}\n", - " n_comp = length(meta$U[which(meta$w>tol)])\n", - " meta = head(meta[order(meta[,2], decreasing = T),], ${max_comp if max_comp > 1 else \"nrow(meta)\"})\n", - " message(paste(n_comp, \"components out of\", length(dat$w), \"total components have weight greater than\", tol))\n", - " res = list()\n", - " for (i in 1:n_comp) {\n", - " title = paste(meta$U[i], \"w =\", round(meta$w[i], 6))\n", - " ##Handle updated udr data structure\n", - " if(is.list(dat$U[[meta$U[i]]])){\n", - " res[[i]] = plot_sharing(dat$U[[meta$U[i]]]$mat, to_cor = ${\"T\" if to_cor else \"F\"}, title=title, remove_names = ${\"TRUE\" if remove_label else \"FALSE\"})\n", - " } else if(is.matrix(dat$U[[meta$U[i]]])){\n", - " res[[i]] = plot_sharing(dat$U[[meta$U[i]]], to_cor = ${\"T\" if to_cor else \"F\"}, title=title, remove_names = ${\"TRUE\" if remove_label else \"FALSE\"})\n", - " }\n", - " }\n", - " unit = 4\n", - " n_col = 5\n", - " n_row = ceiling(n_comp / n_col)\n", - " pdf(${_output:r}, width = unit * n_col, height = unit * n_row)\n", - " do.call(gridExtra::grid.arrange, c(res, list(ncol = n_col, nrow = n_row, bottom = \"Data source: readRDS(${_input:br})${('$'+name) if name else ''}\")))\n", - " dev.off()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "SoS", - "language": "sos", - "name": "sos" - }, - "language_info": { - "codemirror_mode": "sos", - "file_extension": ".sos", - "mimetype": "text/x-sos", - "name": "sos", - "nbconvert_exporter": "sos_notebook.converter.SoS_Exporter", - "pygments_lexer": "sos" - }, - "sos": { - "kernels": [ - [ - "R" - ], - [ - "SoS" - ] - ], - "version": "0.22.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}