DEG_GEOlimma: Differentially expressed genes analysis through the limma...

In jtcasemajor/GENEXPRESSO: Comparison of DEG and coexpression tools

Description

Uses the lm.fit(),eBayes() and topTable() from the limma package to automate the steps of the analysis.

Usage

data(GEOlimma_probabilities)

Arguments

dataset	dataframe of expression values with samples in columns and genes in row.
design	vector of 0 and 1 values. 0 for the first experimental condition, 1 for the second one.
contrast.matrix	design matrix like the one produced by the make_designMatrix() function.

Value

A dataframe with 3 columns is returned. It contains the Log Fold change value (logFC), The Pvalue associated with this Pvalue (PValue) the last column corresponds to the gene names given in the dataset (SYMBOL)

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  

#Build the design matrix
#design = make_designMatrix(dataset = Data,ncond1 = 10, ncond2 = 10)
#get the results of limma analysis
#res.diff = DEG_GEOlimma(dataset = Data,design)

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