ora.RdTraditional Overrepresentation Analysis by hypergeometric test: pathways are treated as collections of individual genes and all genes are treated as equally informative. This function is provided for comparison of the results of traditional methods to Sigora.
ora(geneList, GPSrepo, idmap = load_data("idmap"))
| geneList | A vector containing the list of genes of interest (e.g. differentially expressed genes). Following Identifier types are supported: Gene Symbols, ENTREZ-IDs or ENSEMBL-IDs. |
|---|---|
| GPSrepo | A GPS-repository (either one of the provided precomputed GPS
repositories) or one created by |
| idmap | A dataframe for converting between different gene-identifier types (e.g. ENSEMBL, ENTREZ and HGNC-Symbols of genes). Most users do not need to set this argument, as there is a built-in conversion table. |
A dataframe with individual gene ORA results.
The primary purpose of makeGPS is to create a GPS repository. It
does, however, retain the original "single gene"-"pathway" associations for
the purpose of followup analyses, such as comparison of sigora-results to
traditional methods. ora is an implementation of the traditional
(individual gene) Overrepresentation Analysis.
data(kegM) ## select 50 genes from 3 mouse pathways set.seed(seed=345) a1<-genesFromRandomPathways(kegM,3,50) #> ### randomly selected pathways are: #> mmu03010 #> mmu04722 #> mmu05223 ## originally selected pathways: a1[["selectedPathways"]] #> [1] "mmu03010" "mmu04722" "mmu05223" ## compare to traditional methods results: oraRes <- ora(a1[["genes"]],kegM) dim(oraRes) #> [1] 4 6 oraRes #> pathwyid description success pathwaySize pvalues #> 1 mmu03010 Ribosome 30 175 8.131e-37 #> 2 mmu04722 Neurotrophin signaling pathway 18 121 3.392e-20 #> 3 mmu03008 Ribosome biogenesis in eukaryotes 7 116 1.296e-05 #> 4 mmu05223 Non-small cell lung cancer 5 66 8.420e-05 #> Bonfer #> 1 1.032637e-34 #> 2 4.307840e-18 #> 3 1.645920e-03 #> 4 1.069340e-02