diffExpR: Function for RNASeq Differential Expression

Description Usage Arguments Details Value Examples

View source: R/diffExpR.R

Description

Identified differentially expressed genes from two different condition

Usage

1
diffExpR(object, comparison = comparison, ...)

Arguments

object

Quantified data obtained from rnaseqProcess function

comparison

name of comparing conditions, currently working is "Control-Treated"

...

Details

The function based on the following flow

1. First extrcat the quantified data from PreProcessData S4 class objec t. 2. Extract the corresponding phenodata information. 3. Use the Limma functionality for creating design and contrast matrix 4. calculate differential expression

Value

Return an object of S4 class PreProcessData.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (object, comparison = comparison, ...) 
{
    state <- object@phenoData$condition
    f <- factor(state)
    design <- model.matrix(~0 + f)
    colnames(design) <- levels(f)
    pdf("Mean-varianceTrend.pdf")
    linmod <- limma::voom(object@qData, design, plot = TRUE)
    dev.off()
    fit <- limma::lmFit(linmod, design)
    contrast.matrix <- limma::makeContrasts(comparison, levels = design)
    fit <- limma::contrasts.fit(fit, contrast.matrix)
    ebayes <- limma::eBayes(fit)
    object@diffExp <- limma::topTable(ebayes, number = Inf)
    object@DFsummary <- data.frame(summary(limma::decideTests(fit)))
    new("PreProcessData", object)
  }

HTDA documentation built on May 31, 2017, 2:29 a.m.