teGeneRetrieval: Define tissue-specific genes by using the algorithm from the...

In Tuteja-Lab/TissueEnrich: Tissue-specific gene enrichment analysis

Define tissue-specific genes by using the algorithm from the Human Protein Atlas

Description

The teGeneRetrieval function is used to define tissue-specific genes, using the algorithm from the HPA (Uhlén et al. 2015). It takes a gene expression SummarizedExperiment object as input (rows as genes and columns as tissue) and classifies the genes into different gene groups. The users also have the option of changing the default thresholds to vary the degree of tissue specificity of genes. More details about the gene groups and HPA thresholds are provided below. More details about the gene groups are provided in the vignette.

Usage

teGeneRetrieval(expressionData, foldChangeThreshold = 5,
  maxNumberOfTissues = 7, expressedGeneThreshold = 1)

Arguments

expressionData	A SummarizedExperiment object containing gene expression values.
foldChangeThreshold	A numeric Threshold of fold change, default 5.
maxNumberOfTissues	A numeric Maximum number of tissues in a group for group enriched genes, default 7.
expressedGeneThreshold	A numeric Minimum gene expression cutoff for the gene to be called as expressed, default 1.

Value

The output is a SummarizedExperiment object containing the information about the tissue-specific genes with three columns: Gene, Tissue, and Enrichment group of the gene in the given tissue.

Author(s)

Ashish Jain, Geetu Tuteja

Examples

library(SummarizedExperiment)
data<-system.file('extdata', 'test.expressiondata.txt', package =
'TissueEnrich')
expressionData<-read.table(data,header=TRUE,row.names=1,sep='\t')
se<-SummarizedExperiment(assays = SimpleList(as.matrix(expressionData)),
rowData = row.names(expressionData),colData = colnames(expressionData))
output<-teGeneRetrieval(se)
head(assay(output))

Tuteja-Lab/TissueEnrich documentation built on March 26, 2022, 9:29 a.m.