tools.DEG.RNAseq: Compute the pvalues of genes being differentially expressed...

In jtcasemajor/GENEXPRESSO: Comparison of DEG and coexpression tools

Description

Through different functions contained in several packages, this function computes pvalues of differentially expressed genes

Usage

tools.DEG.RNAseq(data, tool)

Arguments

data

dataframe of gene expression levels, with genes in rows and samples in columns.

tool

Method to Normalize datasets and statistically analysing them "edgeR_RLE","edgeR_upperquartile","edgeR_TMMwsp", and "edgeR_TMM" are methods of normalization implemented in the edgeR package with the function calcNormFactors.DGEList() If "tool" is one of these parameters, it will compute two analysis: the exact test from the exactTest() function and a Quasi-likelihood test with the glmQLFTest() function still in the edgeR package. "deseq2.Wald" and "deseq2.LRT' uses the same normalization methods contained in the DESeq2 package with de DESeq() function. The first method uses a Wald test and the second a Likelihood ratio test to determine Pvalues of differentially expressed genes.

Value

Dataframe with genes in row, and methods used in columns. It contains the differentially expressed p-values for each gene.

Examples

# Import the dataset
Data = matrix(runif(5000, 10, 100), ncol=20)
group = paste0(rep(c("control", "case"), each = 10),rep(c(1:10),each = 1))
genes <- paste0(rep(LETTERS[1:25], each=10), rep(c(1:10),each = 1))
colnames(Data) = group
row.names(Data) = genes 

# computing pvalues of DEG with TMM normalization method
res.DEG = tools.DEG.RNAseq(data = Data, tool = "edgeR_TMM")

jtcasemajor/GENEXPRESSO documentation built on Dec. 21, 2021, 4:11 a.m.