EnrichmentBrowser (version 2.2.2)

de.ana: Differential expression analysis between two sample groups

Description

The function carries out a differential expression analysis between two sample groups. Resulting fold changes and derived p-values are returned. Raw p-values are corrected for multiple testing.

Usage

de.ana( expr, grp = NULL, blk = NULL,  de.method = c("limma", "edgeR", "DESeq"), padj.method = "BH", stat.only=FALSE )

Arguments

expr

Expression data. A numeric matrix. Rows correspond to genes, columns to samples. Alternatively, this can also be an object of class ExpressionSet (in case of microarray data) or an object of class SeqExpressionSet (in case of RNA-seq data). See the man page of read.eset for prerequisites for the expression data.

grp

*BINARY* group assignment for the samples. Use '0' and '1' for unaffected (controls) and affected (cases) samples, respectively. If NULL, this is assumed to be defined via a column named 'GROUP' in the pData slot if 'expr' is a (Seq)ExpressionSet.

blk

Optional. For paired samples or sample blocks. This can also be defined via a column named 'BLOCK' in the pData slot if 'expr' is a (Seq)ExpressionSet.

de.method

Differential expression method. Use 'limma' for microarray and RNA-seq data. Alternatively, differential expression for RNA-seq data can be also calculated using edgeR ('edgeR') or DESeq2 ('DESeq'). Defaults to 'limma'.

padj.method

Method for adjusting p-values to multiple testing. For available methods see the man of page the of the stats function p.adjust. Defaults to 'BH'.

stat.only

Logical. Should only the test statistic be returned? This is mainly for internal use, in order to carry out permutation tests on the DE statistic for each gene. Defaults to FALSE.

Value

A DE-table with measures of differential expression for each gene/row, i.e. a two-column matrix with log2 fold changes in the 1st column and derived p-values in the 2nd column. If 'expr' is a (Seq)ExpressionSet, the DE-table will be automatically appended to the fData slot.

See Also

read.eset describes prerequisites for the expression data, normalize for normalization of expression data, voom for preprocessing of RNA-seq data, p.adjust for multiple testing correction, eBayes for DE analysis with limma, glmFit for DE analysis with edgeR, and DESeq for DE analysis with DESeq.

Examples

    # (1) microarray data: intensity measurements
    ma.eset <- make.example.data(what="eset", type="ma")
    ma.eset <- de.ana(ma.eset)
    head(fData(ma.eset))
    
    # (2) RNA-seq data: read counts
    rseq.eset <- make.example.data(what="eset", type="rseq")
    rseq.eset <- de.ana(rseq.eset, de.method="DESeq")
    head(fData(rseq.eset))