panev.stats.enrichment: Data overview and enrichment analysis

In vpalombo/PANEV: PaNeV: an R package for a pathway-based network visualization

Data overview and enrichment analysis

Description

The function takes as input the principal descriptive information on given gene list. In particular: number of gene(s) per single pathway, number of pathway(s) per single gene and enrichment analysis. When biological assumptions are broad or not strictly defined, pathways with more occurrences may be principal candidate pathways (FL) to be investigated with PANEV. The results obtained are based on KEGG Pathway database information.

Usage

panev.stats.enrichment(in.file, out.file = "PANEV_enrich", species = NULL)

Arguments

in.file	Name of input file (with extension). The file must contain three columns labelled 'ensembl_gene_id', 'entrezgene' and 'external_gene_name' respectively. The file must rely in the working directory. The handy function panev.dataPreparation could be used to create a properly formatted input file from a gene list.
out.file	The specific name of generated files (default = 'PANEV_enrich').
species	The species code of interest. You can get the correct code among the list of those available in KEGG with the panev.speciesCode function.

Details

This function is based on keggList and keggLink functions of KEGGREST package (http://bioconductor.org/packages/release/bioc/html/KEGGREST.html.

The enrichment analysis is based on enrichment function of bc3net package (https://cran.r-project.org/web/packages/bc3net/index.html.

Value

A <out.file>_enrichment.txt file containing the results of functional enrichment analysis, based on a one-sided Fisher's exact test (hypergeometric test).

A <out.file>_GxP.txt file containing the numbers of gene(s) detected for single pathway.

A <out.file>_PxG.txt file containing the numbers of pathway(s) detected for single gene.

Author(s)

Valentino Palombo ([e-mail])

References

Tenenbaum D (2017). KEGGREST: Client-side REST access to KEGG. R package version 1.16.1.

Simoes R de M, Emmert-Streib F (2012). Bagging statistical network inference from large-scale gene expression data. PLOS ONE; 7: e33624. doi:10.1371/journal.pone.0033624

Examples

##### EXAMPLES CODE #####
#Copy the example files in the current working directory
panev.example()

#Look for the organism code matching the search string 
list <- panev.biomartSpecies(string = "cow")
biomart.species <- as.character(list[1,1]) # btaurus_gene_ensembl

#Prepare PANEV input file using a gene list containing ensembl gene id.
genelist_converted <- panev.dataPreparation(in.file = "ensembl_genelist.txt", 
                                          gene_id = "ensembl", 
                                          biomart.species = biomart.species)

#Look for the specie code matching the search string 
list <- panev.speciesCode(string = "bos")
species <- as.character(list[1,2]) # bta

#Overview on input data 
panev.stats.enrichment(in.file = "ensembl_genelist_converted.txt", 
                     out.file = "example", 
                     species="bta")

vpalombo/PANEV documentation built on June 13, 2022, 1:11 p.m.