TCGAbiolinks (version 1.2.5)

TCGAanalyze_DEA: Differentially expression analysis (DEA) using edgeR package.

Description

TCGAanalyze_DEA allows user to perform Differentially expression analysis (DEA), using edgeR package to identify differentially expressed genes (DEGs). It is possible to do a two-class analysis.

TCGAanalyze_DEA performs DEA using following functions from edgeR:

1. edgeR::DGEList converts the count matrix into an edgeR object.
2. edgeR::estimateCommonDisp each gene gets assigned the same dispersion estimate.
3. edgeR::exactTest performs pair-wise tests for differential expression between two groups.
4. edgeR::topTags takes the output from exactTest(), adjusts the raw p-values using the False Discovery Rate (FDR) correction, and returns the top differentially expressed genes.

Usage

TCGAanalyze_DEA(mat1, mat2, Cond1type, Cond2type, method = "exactTest", fdr.cut = 1, logFC.cut = 0, elementsRatio = 30000)

Arguments

mat1

numeric matrix, each row represents a gene, each column represents a sample with Cond1type

mat2

numeric matrix, each row represents a gene, each column represents a sample with Cond2type

Cond1type

a string containing the class label of the samples in mat1 (e.g., control group)

Cond2type

a string containing the class label of the samples in mat2 (e.g., case group)

method

is 'glmLRT' (1) or 'exactTest' (2). (1) Fit a negative binomial generalized log-linear model to the read counts for each gene (2) Compute genewise exact tests for differences in the means between two groups of negative-binomially distributed counts.

fdr.cut

is a threshold to filter DEGs according their p-value corrected

logFC.cut

is a threshold to filter DEGs according their logFC

elementsRatio

is number of elements processed for second for time consumation estimation

Value

table with DEGs containing for each gene logFC, logCPM, pValue,and FDR

Examples

dataNorm <- TCGAbiolinks::TCGAanalyze_Normalization(dataBRCA, geneInfo)
dataFilt <- TCGAanalyze_Filtering(tabDF = dataBRCA, method = "quantile", qnt.cut =  0.25)
samplesNT <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("NT"))
samplesTP <- TCGAquery_SampleTypes(colnames(dataFilt), typesample = c("TP"))
dataDEGs <- TCGAanalyze_DEA(dataFilt[,samplesNT],
                      dataFilt[,samplesTP],"Normal", "Tumor")