prora

prora an R package for proteomics over representation analysis (ORA) and gene set enrichment analysis (GSEA)

For the prora package documentation visit https://protviz.github.io/prora/.

Introduction

A plethora of R packages exist on CRAN and Bioconductor to perform over-representation analysis (ORA) and gene set enrichment analysis (GSEA). However, consistency in the underlying nomenclature for specific analyses and user friendly implementation is still lacking. prora aims at unifying ID mapping and enrichment analysis in a syntactically coherent and intuitive way, while ensuring reproducibility of results. prora primarily consists of wrapper functions around the r BiocStyle::CRANpkg("sigora"), r BiocStyle::CRANpkg("WebGestaltR") and r BiocStyle::Biocpkg("fgsea") packages from CRAN and Bioconductor and r BiocStyle::CRANpkg("rmarkdown") based reports for visualisation and contextualisation of analysis results.

Installing R package prora

To install the package in R run the following code:

install.packages("remotes")
remotes::install_github("protViz/prora")

Webgestalt ORA

PS >  Rscript <fgczgseaora_path>\run_scripts\lfq_2grp_webgestalt_ora.R --help

WebGestaltR ORA

Usage:
  test.R <grp2file> [--organism=<organism>] [--outdir=<outdir>] [--log2fc=<log2fc>] [--is_greater=<is_greater>] [--nperm=<nperm>] [--ID_col=<ID_col>] [--fc_col=<fc_col>]

Options:
  -o --organism=<organism> organism [default: hsapiens]
  -r --outdir=<outdir> output directory [default: results_ora]
  -t --log2fc=<log2fc> fc threshold [default: 1]
  -g --is_greater=<is_greater> is greater than log2fc [default: TRUE]
  -n --nperm=<nperm> number of permutations to calculate enrichment scores [default: 50]
  -i --ID_col=<ID_col> Column containing the UniprotIDs [default: TopProteinName]
  -f --fc_col=<fc_col> Column containing the estimates [default: log2FC]

Arguments:
  grp2file  input file
PS D:\Dropbox\DataAnalysis\fgczgseaora_Test_SRMService_Integration>

Ora analysis for foldchanges greater and smaller than 1.

Rscript <fgczgseaora_path>\run_scripts\lfq_2grp_webgestalt_ora.R .\data\2Grp_CF_a_vs_CF_b.txt --log2fc=1 --is_greater=TRUE
Rscript <fgczgseaora_path>\run_scripts\lfq_2grp_webgestalt_ora.R .\data\2Grp_CF_a_vs_CF_b.txt --log2fc=1 --is_greater=FALSE
 

WebGestaltR GSEA

PS >  Rscript <fgczgseaora_path>\run_scripts\lfq_2grp_webgestalt_gsea.R --help
WebGestaltR GSEA

Usage:
  test.R <grp2file> [--organism=<organism>] [--outdir=<outdir>] [--nperm=<nperm>] [--ID_col=<ID_col>] [--fc_col=<fc_col>]

Options:
  -o --organism=<organism> organism [default: hsapiens]
  -r --outdir=<outdir> output directory [default: results_gsea]
  -n --nperm=<nperm> number of permutations to calculate enrichment scores [default: 50]
  -i --ID_col=<ID_col> Column containing the UniprotIDs [default: TopProteinName]
  -f --fc_col=<fc_col> Column containing the estimates [default: log2FC]

Arguments:
  grp2file  input file
PS D:\Dropbox\DataAnalysis\fgczgseaora_Test_SRMService_Integration>

PS > Rscript <fgczgseaora_path>\run_scripts\lfq_2grp_webgestalt_gsea.R .\data\2Grp_CF_a_vs_CF_b.txt

To simplify executing the scripts you can add <fgczgseaora_path>\win to the path variable on windows and than execute:

PS > lfq_2grp_webgestalt_gsea.bat --help

or ad <fgczgseaora_path>\run_scripts to the path on linux and execute

lfq_2grp_webgestalt_gsea.R --help

Related topics

(LEGO: a novel method for gene set over-representation analysis by incorporating network-based gene weights)[https://www.nature.com/articles/srep18871]

TeaTasting <-
matrix(c(3, 1, 1, 3),
       nrow = 2,
       dimnames = list(Guess = c("Milk", "Tea"),
                       Truth = c("Milk", "Tea")))
fisher.test(TeaTasting, alternative = "greater")

matrix(c(6, 2, 2, 6),
       nrow = 2,
       dimnames = list(Guess = c("Milk", "Tea"),
                       Truth = c("Milk", "Tea")))
fisher.test(TeaTasting, alternative = "greater")