gdcDEAnalysis {GDCRNATools} | R Documentation

## Differential gene expression analysis

### 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')

[Package GDCRNATools version 1.1.1 Index]