teEnrichmentCustom: Calculate tissue-specific gene enrichment using the...

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

Calculate tissue-specific gene enrichment using the hypergeometric test for custom datasets

Description

The teEnrichmentCustom function is used to calculate tissue-specific gene enrichment using tissue-specific genes defined using the teGeneRetrieval function.

Usage

teEnrichmentCustom(inputGenes = NULL, tissueSpecificGenes = NULL,
  tissueSpecificGeneType = 1, multiHypoCorrection = TRUE,
  backgroundGenes = NULL)

Arguments

inputGenes	An GeneSet object containing the input genes.
tissueSpecificGenes	A SummarizedExperiment object. Output from 'teGeneRetrieval' function. Default NULL.
tissueSpecificGeneType	An integer describing the type of tissue-specific genes to be used. 1 for 'All' (default), 2 for 'Tissue-Enriched',3 for 'Tissue-Enhanced', and 4 for 'Group-Enriched'. Default 1.
multiHypoCorrection	Flag to correct P-values for multiple hypothesis using BH method. Default TRUE.
backgroundGenes	A GeneSet object containing the background gene list, organism type ('Homo Sapiens' or 'Mus Musculus'), and gene id identifier (Gene Symbol or ENSEMBL identifier). The input genes must be present in the background gene list. If not provided all the genes will be used as background.

Value

The output is a list with three objects. The first object is the SummarizedExperiment object containing the enrichment results, the second object contains the tissue-specificity information of the tissue-specific genes for genes from the input gene set, and the third is a GeneSet object containing genes that were not identified in the tissue-specific gene data.

Author(s)

Ashish Jain, Geetu Tuteja

Examples

library(dplyr)
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(metadata(output)[['TissueSpecificGenes']])
genes<-system.file('extdata', 'inputGenesEnsembl.txt', package =
'TissueEnrich')
inputGenes<-scan(genes,character())
gs<-GeneSet(geneIds=inputGenes)
output2<-teEnrichmentCustom(gs,output)
#Plotting the P-Values
enrichmentOutput<-setNames(data.frame(assay(output2[[1]]),
row.names = rowData(output2[[1]])[,1]),
colData(output2[[1]])[,1])
enrichmentOutput$Tissue<-row.names(enrichmentOutput)
ggplot(enrichmentOutput,aes(x=reorder(Tissue,-Log10PValue),y=Log10PValue,
label = Tissue.Specific.Genes,fill = Tissue))+
geom_bar(stat = 'identity')+
labs(x='', y = '-LOG10(P-Value)')+
theme_bw()+
theme(legend.position='none')+
theme(plot.title = element_text(hjust = 0.5,size = 20),axis.title =
element_text(size=15))+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
panel.grid.major= element_blank(),panel.grid.minor = element_blank())

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