gdcDEAnalysis: Differential gene expression analysis

In GDCRNATools: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC

Description

Performs differential gene expression analysis by limma, edgeR, and DESeq2

Usage

gdcDEAnalysis(counts, group, comparison, method = "limma",
  n.cores = NULL, filter = TRUE)

Arguments

counts	a dataframe or numeric matrix of raw counts data generated from gdcRNAMerge
group	a vector giving the group that each sample belongs to
comparison	a character string specifying the two groups being compared. Example: comparison='PrimaryTumor-SolidTissueNormal'
method	one of 'limma', 'edgeR', and 'DESeq2'. Default is 'limma' Note: It may takes long time for method='DESeq2' with a single core
n.cores	a numeric value of cores to be used for method='DESeq2' to accelate the analysis process. Default is NULL
filter	logical, whether to filter out low expression genes. If TRUE, only genes with cpm > 1 in more than half of the samples will be kept. Default is TRUE

Value

A dataframe containing Ensembl gene ids/miRBase v21 mature miRNA ids, gene symbols, biotypes, fold change on the log2 scale, p value, and FDR etc. of all genes/miRNAs of analysis.

Note

It may takes long time for method='DESeq2' with a single core. Please use multiple cores if possible

Author(s)

Ruidong Li and Han Qu

References

Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010 Jan 1;26(1):139-40.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research. 2015 Jan 20; 43(7):e47-e47.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology. 2014 Dec 5; 15(12):550.

Examples

genes <- c('ENSG00000000938','ENSG00000000971','ENSG00000001036',
        'ENSG00000001084','ENSG00000001167','ENSG00000001460')

samples <- c('TCGA-2F-A9KO-01', 'TCGA-2F-A9KP-01',
            'TCGA-2F-A9KQ-01', 'TCGA-2F-A9KR-11', 
            'TCGA-2F-A9KT-11', 'TCGA-2F-A9KW-11')

metaMatrix <- data.frame(sample_type=rep(c('PrimaryTumor',
                    'SolidTissueNormal'),each=3),
                    sample=samples,
                    days_to_death=seq(100,600,100),
                    days_to_last_follow_up=rep(NA,6))
rnaMatrix <- matrix(c(6092,11652,5426,4383,3334,2656,
                    8436,2547,7943,3741,6302,13976,
                    1506,6467,5324,3651,1566,2780,
                    834,4623,10275,5639,6183,4548,
                    24702,43,1987,269,3322,2410,
                    2815,2089,3804,230,883,5415), 6,6)
rownames(rnaMatrix) <- genes
colnames(rnaMatrix) <- samples
DEGAll <- gdcDEAnalysis(counts     = rnaMatrix, 
                        group      = metaMatrix$sample_type, 
                        comparison = 'PrimaryTumor-SolidTissueNormal', 
                        method     = 'limma')

GDCRNATools documentation built on Nov. 27, 2020, 2 a.m.