simple GO is a simple and fast GO enrichment tool with functional clustering of the terms. For any problem either open an issue in GitHub or contact me at gio.iacono.work@gmail.com
To install the package.
devtools::install_github("iaconogi/simpleGO")
To run simpleGO simply pass as input your list of genes, the tool will automatically detect the species (supported mouse or human) and the ID format (official gene symbols or ensembl).
results=simpleGO(gene.list=c('Celf4','Eapp','Fzd8','Larp1','Mlxipl','Nr6a1','Phb2', 'Snai2'))
[1] "Preprocessing your gene IDs..."
[1] "Recognized your gene names as Mouse, Gene Symbols"
[1] "Recognized 8/8 of your gene list (100.00 %)"
[1] "Loading mouse data"
[1] "Found 58 terms enriched"
[1] 58 8
Metric: 'jaccard'; comparing: 58 vectors.
[1] "Automatically cutting the tree at 9 percent, with 4 resulting clusters"
[1] "Divided the 58 enriched GO terms into 4 distinct functional groups"
To facilitate the interpretation of the GO enrichments, simpleGO collapses the enriched terms into functional groups. The output of simpleGOis a list as long as the number of functional groups. In this case we have 4 distinct functional groups. Let's look the first one:
head(results[[1]])
BH p-value Overlap GO Signature
negative regulation of RNA metabolic process 1.137030e-06 8 1262
negative regulation of nucleobase-containing compound metabolic process 1.219722e-06 8 1388
negative regulation of gene expression 5.841940e-06 8 1775
With the option excel.export = file name you can automatically save the resulting enrichment in an excel file.
The help of the function simpleGO (F1 key in Rstudio) further explains how to change the cutoff for the p-values (setting `p`), how to use a custom background (setting `background`) and how to increase the number of the recognized IDs (setting `use.synonims`).