#' The main function to run GSEA-R from Broad Institute
#' @usage
#' Run.GSEA()
#'
#' @return A vector with sampling size
#' @export
#'
#' @examples
#' readRDS(paste(path.package("canceR"),"/extdata/rdata/ucec_tcga_pubGSEA1021.rds", sep=""))
#' \dontrun{
#' Run.GSEA()
#' }
Run.GSEA <- function(){
# if (!require("GSEABase")) {
# if (!requireNamespace("BiocManager", quietly=TRUE))
# install.packages("BioManager")
# BiocManager::install("GSEABase")
# library("GSEABase")
# }
#source("dialogSelectFiles_GSEA.R")
# GSEA 1.0 -- Gene Set Enrichment Analysis / Broad Institute
#source("dialogSelectFiles_GSEA.R")
#workspace <- getwd()
dialogSelectFiles_GSEA()
##stop the Run when cancelling dialogSelectFiles()
#if(!exists("fname.GSEA")){
# stop("The GSEA.1.0.R file is not found.")
#}
GSEA.program.location <- paste(path.package("canceR"),"/extdata/GSEA.1.0.R", sep="")
#GSEA.program.location <- fname.GSEA # R source program (change pathname to the rigth location in local machine)
source(GSEA.program.location, verbose=TRUE, max.deparse.length=9999)
ENV$prefix <- basename(ENV$fname.Output)
GSEA( # Input/Output Files :-------------------------------------------
input.ds = ENV$fname.GCT, # Input gene expression Affy dataset file in RES or GCT format
input.cls = ENV$fname.CLS, # Input class vector (phenotype) file in CLS format
gs.db = ENV$fname.GMT, # Gene set database in GMT format
output.directory = ENV$fname.Output, # Directory where to store output and results (default: "")
## Program parameters :-------------------------------------------------------------------------------------------------------------------------
doc.string = ENV$prefix, # Documentation string used as a prefix to name result files (default: "GSEA.analysis")
non.interactive.run = FALSE, # Run in interactive (i.e. R GUI) or batch (R command line) mode (default: F)
reshuffling.type = "sample.labels", # Type of permutation reshuffling: "sample.labels" or "gene.labels" (default: "sample.labels"
nperm = 1000, # Number of random permutations (default: 1000)
weighted.score.type = 1, # Enrichment correlation-based weighting: 0=no weight (KS), 1= weigthed, 2 = over-weigthed (default: 1)
nom.p.val.threshold = -1, # Significance threshold for nominal p-vals for gene sets (default: -1, no thres)
fwer.p.val.threshold = -1, # Significance threshold for FWER p-vals for gene sets (default: -1, no thres)
fdr.q.val.threshold = 0.25, # Significance threshold for FDR q-vals for gene sets (default: 0.25)
topgs = 20, # Besides those passing test, number of top scoring gene sets used for detailed reports (default: 10)
adjust.FDR.q.val = FALSE, # Adjust the FDR q-vals (default: F)
gs.size.threshold.min = 15, # Minimum size (in genes) for database gene sets to be considered (default: 25)
gs.size.threshold.max = 500, # Maximum size (in genes) for database gene sets to be considered (default: 500)
reverse.sign = FALSE, # Reverse direction of gene list (pos. enrichment becomes negative, etc.) (default: F)
preproc.type = 0, # Preproc.normalization: 0=none, 1=col(z-score)., 2=col(rank) and row(z-score)., 3=col(rank). (def: 0)
random.seed = 3338, # Random number generator seed. (default: 123456)
perm.type = 0, # For experts only. Permutation type: 0 = unbalanced, 1 = balanced (default: 0)
fraction = 1.0, # For experts only. Subsampling fraction. Set to 1.0 (no resampling) (default: 1.0)
replace = FALSE, # For experts only, Resampling mode (replacement or not replacement) (default: F)
save.intermediate.results = FALSE, # For experts only, save intermediate results (e.g. matrix of random perm. scores) (default: F)
OLD.GSEA = FALSE, # Use original (old) version of GSEA (default: F)
use.fast.enrichment.routine = TRUE # Use faster routine to compute enrichment for random permutations (default: T)
)
#-----------------------------------------------------------------------------------------------------------------------------------------------
# Overlap and leading gene subset assignment analysis of the GSEA results
GSEA.Analyze.Sets(
directory = ENV$fname.Output, # Directory where to store output and results (default: "")
topgs = 20, # number of top scoring gene sets used for analysis
non.interactive.run = FALSE,
height = 16,
width = 16
)
}
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