microarray analysis - gene upregulation/downregulation

Question

[bioinformatics]

Hi guys,

I have performed microarray differential expression analysis using the following R commands/script:

  library("arrayQualityMetrics")
    > library(GEOquery)
    > library(oligo)
    > library(Biobase)
    > library(affy)
    > library("splitstackshape")
    > library("tidyr")
    > library(dplyr)
    > celFiles <- list.celfiles()
    > affyRaw <- read.celfiles(celFiles) Platform design info loaded. Reading in : GSM766537.CEL Reading in : GSM766539.CEL Reading in : GSM766624.CEL Reading in : GSM766640.CEL
    > eset <-oligo::rma(affyRaw) Background correcting Normalizing Calculating Expression
    > library(limma)
    > pData(eset)
                  index GSM766537.CEL     1 GSM766539.CEL     2 GSM766624.CEL     3 GSM766640.CEL     4
    > Groups <- c("DDLPS", "DDLPS", "WDLPS", "WDLPS")
    > design <- model.matrix(~factor(Groups))
    > colnames(design) <- c("DDLPS", "DDLPSvsWDLPS")
    > fit <- lmFit(eset, design)
    > fit <- eBayes(fit)
    > options (digits =2)
    > res <- topTable (fit, number = Inf, adjust.method = "none", coef = 1)
    > write.table(res, "diff_exp.txt", sep= "\t")
    > require(hgu133a.db)
    > probes <- rownames(eset)
    > annotLookup <- select(hgu133a.db, keys = probes,
    +   columns = c('PROBEID', 'ENSEMBL', 'SYMBOL')) 'select()' returned 1:many mapping between keys and columns
write.exprs(eset, file = "exprvalues")

I have 52 WDLPS and 40 DDLPS samples that I performed differential expression between. I'm now trying to find out which/total number of genes were upregulated the most in WDLPS or DDLPS tissues samples. Does anyone know how I might do this with commands in R?

Thanks!

[ATpoint]

Duplicate/related:

microarray differential analysis - P value histogram

microarray differential expression analyses

microarray differential expression analyses

Please stop rapid-firing posts, read received answers and apply them.