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24 changes: 24 additions & 0 deletions .Rbuildignore
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^pkgdown$
build_pkg_site.R
^\.github$
vignettes/ATC.R
vignettes/ATC_methods.Rmd
vignettes/PAC.R
vignettes/cola_general.Rmd
vignettes/cola_quick.Rmd
vignettes/compare_partitions.Rmd
vignettes/consensus_partition_workflow.png
vignettes/functional_enrichment.Rmd
vignettes/hierarchical.Rmd
vignettes/hierarchical_partitioning_workflow.png
vignettes/large_k_problem_consensus_heatmap-min.png
vignettes/large_k_problem_select_partition_number-min.png
vignettes/lt_functional_enrichment_TCGA_GBM.rds
vignettes/membership_heatmap.png
vignettes/predict.Rmd
vignettes/rand_jaccard.R
vignettes/rand_jaccard.png
vignettes/silhouette.R
vignettes/skmeans.png
vignettes/test_NMF.R
vignettes/vignette_example.R
vignettes/work_with_big_datasets.Rmd
vignettes/AML.Rmd
vignettes/examples.Rmd
19 changes: 18 additions & 1 deletion LICENSE
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YEAR: 2021
YEAR: 2024
COPYRIGHT HOLDER: Zuguang Gu

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
91 changes: 58 additions & 33 deletions README.md
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[ ![bioc](http://bioconductor.org//shields/lastcommit/devel/bioc/cola.svg) ](http://bioconductor.org/checkResults/devel/bioc-LATEST/cola/)


## Features

1. It modularizes the consensus clustering processes that various methods can
be easily integrated in different steps of the analysis.
2. It provides rich visualizations for intepreting the results.
3. It allows running multiple methods at the same time and provides
functionalities to compare results in a straightforward way.
4. It provides a new method to extract features which are more efficient to
separate subgroups.
5. It generates detailed HTML reports for the complete analysis.

## Citation

Zuguang Gu, et al., cola: an R/Bioconductor package for consensus partitioning through a general framework, Nucleic Acids Research, 2021. https://doi.org/10.1093/nar/gkaa1146

Zuguang Gu, et al., Improve consensus partitioning via a hierarchical procedure. Briefings in bioinformatics 2022. https://doi.org/10.1093/bib/bbac048



## Install

*cola* is available on [Bioconductor](http://bioconductor.org/packages/devel/bioc/html/cola.html), you can install it by:
Expand All @@ -38,30 +32,26 @@ library(devtools)
install_github("jokergoo/cola")
```

## Links
## Methods

Examples for *cola* analysis can be found at https://jokergoo.github.io/cola_examples/ and https://jokergoo.github.io/cola_collection/.
The **cola** supports two types of consensus partitioning.

Online documentation is at https://jokergoo.github.io/cola.
### Standard consensus partitioning

Supplementary for the *cola* manuscript is at https://github.com/jokergoo/cola_supplementary and the scripts are at https://github.com/jokergoo/cola_manuscript.
#### Features

## Vignettes
1. It modularizes the consensus clustering processes that various methods can
be easily integrated in different steps of the analysis.
2. It provides rich visualizations for intepreting the results.
3. It allows running multiple methods at the same time and provides
functionalities to compare results in a straightforward way.
4. It provides a new method to extract features which are more efficient to
separate subgroups.
5. It generates detailed HTML reports for the complete analysis.

There are the following vignettes:

<ol style="list-style-type: decimal">
<li><a href="https://jokergoo.github.io/cola_vignettes/cola_quick.html">A Quick Start of Using cola Package</a></li>
<li><a href="https://jokergoo.github.io/cola_vignettes/cola.html">A Framework for Consensus Partitioning</a></li>
<li><a href="https://jokergoo.github.io/cola_vignettes/functional_enrichment.html">Automatic Functional Enrichment on Signature genes</a></li>
<li><a href="https://jokergoo.github.io/cola_vignettes/predict.html">Predict Classes for New Samples</a></li>
<li><a href="https://jokergoo.github.io/cola_vignettes/work_with_big_datasets.html">Work with Big Datasets</a></li>
<li><a href="https://jokergoo.github.io/cola_vignettes/compare_partitions.html">Compare Partitions</a></li>
<li><a href="https://jokergoo.github.io/cola_vignettes/ATC_methods.html">ATC - More Forms</a></li>
<li><a href="https://jokergoo.github.io/cola_vignettes/hierarchical.html">Hierarchical consensus partitioning</a></li>
</ol>

## Consensus Partitioning
#### Workflow

<img width="700" src="https://user-images.githubusercontent.com/449218/52628723-86af3400-2eb8-11e9-968d-b7f47a408818.png" />

Expand Down Expand Up @@ -89,7 +79,7 @@ The steps of consensus partitioning is:
8. If rows in the matrix can be associated to genes, downstream analysis such
as function enrichment analysis can be performed.

### Usage
#### Usage

Three lines of code to perfrom *cola* analysis:

Expand All @@ -103,18 +93,53 @@ rl = run_all_consensus_partition_methods(
cola_report(rl, output_dir = ...)
```

### Plots
#### Plots

Following plots compare consensus heatmaps with k = 4 under all combinations of methods.

<img src="https://user-images.githubusercontent.com/449218/52631118-3a66f280-2ebe-11e9-8dea-0172d9beab91.png" />


## License
### Hierarchical consensus partitioning

MIT @ Zuguang Gu

#### Acknowledgement
#### Features

1. It can detect subgroups which show major differences and also moderate differences.
2. It can detect subgroups with large sizes as well as with tiny sizes.
3. It generates detailed HTML reports for the complete analysis.

The cola icon in logo is made by <a href="https://www.flaticon.com/authors/photo3idea-studio" title="photo3idea_studio">photo3idea_studio</a> from <a href="https://www.flaticon.com/" title="Flaticon">www.flaticon.com</a>.

#### Hierarchical Consensus Partitioning


<img src="https://user-images.githubusercontent.com/449218/126491482-31a9496f-cc4d-4c4f-80b7-7b752d8d8d06.png" width="400" />



#### Usage

Three lines of code to perfrom hierarchical consensus partitioning analysis:

```r
mat = adjust_matrix(mat) # optional
rh = hierarchical_partition(mat, mc.cores = ...)
cola_report(rh, output_dir = ...)
```

#### Plots

Following figure shows the hierarchy of the subgroups.

<img src="https://user-images.githubusercontent.com/449218/100014572-d7b2c280-2dd6-11eb-9265-a84d324122f2.png" width="300" />

Following figure shows the signature genes.

<img src="https://user-images.githubusercontent.com/449218/100014657-f913ae80-2dd6-11eb-9bf7-53f733e9f8f0.png" width="600" />



## License

MIT @ Zuguang Gu

51 changes: 51 additions & 0 deletions _pkgdown.yml
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destination: docs

navbar:
title: "cola"
left:
- text: "Articles"
menu:
- text: Standard consensus partitioning
- text: "A Quick Start of Using cola Package"
href: articles/cola_quick.html
- text: "A Framework for Consensus Partitioning"
href: articles/cola_general.html
- text: "Automatic Functional Enrichment on Signature genes"
href: articles/functional_enrichment.html
- text: "Predict Classes for New Samples"
href: articles/predict.html
- text: "Work with Big Datasets"
href: articles/work_with_big_datasets.html
- text: "Compare Partitions"
href: articles/compare_partitions.html
- text: "ATC - More Forms"
href: articles/ATC_methods.html

- text: -------
- text: Hierarchical partitioning
- text: "Hierarchical consensus partitioning"
href: articles/hierarchical.html

- text: -------
- text: Other examples
- text: "Re-analyze an AML proteomics dataset"
href: articles/AML.html

- text: -------
- text: On public datasets
- text: "Applications on public datasets"
href: articles/examples.html

- text: -------
- text: Supplementary files
- text: "Supplementary files for cola NAR paper"
href: https://jokergoo.github.io/cola_supplementary/
- text: "Supplementary files for cola BIB paper"
href: https://cola-rh.github.io/supplementary/

- text: "Reference"
href: reference/index.html


template:
includes:
after_body: "<style>nav[data-toggle='toc'] .nav .nav {display: block;}</style>"
19 changes: 17 additions & 2 deletions build_pkg_site.R
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writeLines("destination: docs", "_pkgdown.yml")
if(file.exists(".Rbuildignore")) {
ln = readLines(".Rbuildignore")
if(!any(ln == "^_pkgdown\\.yml$")) {
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^pkgdown$", ".Rbuildignore")
}

pkgdown::build_site(run_dont_run = FALSE)
pkgname = read.dcf("DESCRIPTION")[1, "Package"]

vig_files = list.files(path = "vignettes", full.names = TRUE)
vig_files = vig_files[basename(vig_files) != paste0(pkgname, ".Rmd")]

ln = readLines(".Rbuildignore")

for(v in vig_files) {
if(!any(ln == v)) {
ln = c(ln, v)
}
}
writeLines(ln, ".Rbuildignore")


options(rmarkdown.html_vignette.check_title = FALSE)
pkgdown::build_site(run_dont_run = TRUE)
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