CoSIA is a package that provides researchers with an alternative
methodology for comparing across species and tissues using normal wild-type RNA-Seq Gene Expression data from Bgee.
Using RNA-Seq Gene Expression data, CoSIA provides multiple visualization tools to explore the transcriptome diversity and variation across genes,
tissues, and species. CoSIA uses Coefficient of Variation and Shannon Entropy based Diversity and Specificity to calculate transcriptome diversity and variation.
CoSIA also provides additional conversion tools and utilities to provide a streamlined methodology for cross-species comparison
across the tissues and genes of five commonly used biomedical research species
(Mus musculus, Rattus norvegicus, Danio rerio, Drosophila melanogaster, and Caenorhabditis elegans) in addition to Homo sapiens.
In preparation for the Bioconductor 3.17 release, we have developed CoSIA within a Bioconductor docker.
Follow the instructions on Bioconductor's Docker Help Page to install and run CoSIAdata within the bioconductor_docker:devel container.
In R:
## Install BiocManager if necessary; Install CoSIAdata
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("CoSIAdata")
library(CoSIAdata)
CoSIAdata has species-specific helper functions for accessing expression data
c_elegans_vst_counts <- CoSIAdata::Caenorhabditis_elegans()
Check out the vignette for more information on accesssing specific data in CoSIAdata.
- Anisha Haldar
- Vishal H. Oza
- Nathaniel DeVoss
- Amanda D. Clark
- Brittany N. Lasseigne
What is Happening in the Lasseigne Lab?
This work was supported in part by the UAB Lasseigne Lab Start-Up funds (BNL, AH, ND, ADC and VHO), the UAB Pilot Center for Precision Animal Modeling (C-PAM) (1U54OD030167) (BNL and VHO), UAB Pilot Center for Precision Animal Modeling (C-PAM) - Diversity Supplement (3U54OD030167-03S1) (ADC), and Mentored Experiences in Research, Instruction, and Teaching (MERIT) Program (K12 GM088010) (ADC).
The authors would like to thank the members of the Lassigne lab for their support and feedback, in particular, Elizabeth J. Wilk, Jordan Whitlock, and Timothy C. Howton.
This project is licensed under the MIT License - see the LICENSE.md file for details