The correlationAnalyzeR package uses correlation data derived from the ARCHS4 RNA Seq repository to generate biological insights about a gene or genes of interest. The web implementation can be found here.

To get started with correlationAnalyzeR, please view the vignette for this package here.

correlationAnalyzeR using install_github() from the devtools package.

## install.packages("devtools")
devtools::install_github("Bishop-Laboratory/correlationAnalyzeR")

This project is motivated by a recurring issue that arises during exploratory bioinformatics: Sometimes little to no information exists about a gene or gene(s) of interest. One way to address this problem is to compute gene expression correlations. These values indicate how genes vary in relation to eachother.

With gene correlation data, it is possible to implement three levels of analysis:

• Single gene: Analyses such as GSEA (Gene Set Enrichment Analysis) which predict the biological pathways correlated with a gene of interest.
• Paired gene set: Statistical approaches to determine if a gene set is significantly correlated with a gene of interest.
• Gene set toplogy: Methods to uncover the topology of a gene set by using gene correlation data as the input for tools such as UpSet and hierarchical clustering.

Please report issues and feel free to contribute! I hope this project can some day incorporate other sources of data such as ppi networks and continue to develop as a useful tool for anyone who wants to understand more about their HTS data.

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