R/function_edgeR.r

In Caleb-Huo/AWFisher: An R package for fast computing for adaptively weighted fisher's method

##' use edgeR function to get pvalue
##'
##' use edgeR function to get pvalue
##' @title use edgeR function to get pvalue
##' @param astudy A list contains a data matrix and a vector of group label
##' @return A list of pvalue and effect size
##' @author Zhiguang Huo
##' @import edgeR 
##' @export
##' @examples
##'
##' N0 = 10
##' G <- 1000
##' GDEp <- 50
##' GDEn <- 50
##'
##' set.seed(15213)
##'
##' astudy <- matrix(rpois(N0*2*G,10),nrow=G,ncol=N0*2)
##' ControlLabel <- 1:N0
##' caseLabel <- (N0 + 1):(2*N0)
##'
##' astudy[1:GDEp,caseLabel] <- astudy[1:GDEp,caseLabel] + 2
##' astudy[1:GDEp + GDEn,caseLabel] <- astudy[1:GDEp,caseLabel] - 2
##'
##' alabel <- c(rep(0,length(ControlLabel)),rep(1,length(caseLabel)))
##' Study <- list(data=astudy, label=alabel)
##'
##' result <- function_edgeR(Study)
##' fdr <- p.adjust(result$pvalue)
##' sum(fdr<=0.05)
##'

function_edgeR <- function(astudy) {
    alabel <- astudy$label
    aData <- astudy$data
    
    group <- factor(alabel)
    d = DGEList(counts = aData, group = group)
    d <- calcNormFactors(d)  # normalization part
    design = model.matrix(~group)
    d = estimateGLMCommonDisp(d)
    d = estimateGLMTrendedDisp(d)
    d = estimateGLMTagwiseDisp(d)
    fit <- glmFit(d, design)
    lrt <- glmLRT(fit, coef = 2)
    apvalue = lrt$table[, 4]
    aeffectsize <- fit$coefficient[, 2]
    
    return(list(pvalue = apvalue, effectSize = aeffectsize))
}