TCGAanalyze_DEA

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Differentially expression analysis (DEA) using edgeR package.

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

Aliases
  • TCGAanalyze_DEA
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")
Documentation reproduced from package TCGAbiolinks, version 1.2.5, License: GPL (>= 3)

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