runfgsea-methods: perform multilevel preranked gene set enrichment analysis.

In ViSEAGO: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity

Description

This method perform fast gene set enrichment analysis (GSEA) using fgsea package.

Usage

runfgsea(
  geneSel,
  gene2GO,
  ont,
  method = c("fgseaSimple", "fgseaMultilevel"),
  params = list(nperm = 10000, sampleSize = 101, minSize = 1, maxSize = Inf, eps = 0,
    scoreType = c("std", "pos", "neg"), nproc = 0, gseaParam = 1, BPPARAM = NULL, absEps
    = NULL)
)

## S4 method for signature 'ANY,gene2GO,character'
runfgsea(
  geneSel,
  gene2GO,
  ont,
  method = c("fgseaSimple", "fgseaMultilevel"),
  params = list(nperm = 10000, sampleSize = 101, minSize = 1, maxSize = Inf, eps = 0,
    scoreType = c("std", "pos", "neg"), nproc = 0, gseaParam = 1, BPPARAM = NULL, absEps
    = NULL)
)

Arguments

geneSel	a 2 columns data.table with preranked gene identifiants (in first column) based on the statistical values (second column).
gene2GO	a gene2GO-class object created by annotate method.
ont	the ontology used is "MF" (Molecuar Function), "BP" (Biological Process), or "CC" (Cellular Component).
method	fgsea method to use with fgseaSimple or fgseaMultilevel.
params	a list with fgseaSimple or fgseaMultilevel parameters.

Details

This method is a convenient wrapper using a given ontology category (ont argument) in order to perform gene set enrichment analysis using fgseaSimple or fgseaMultilevel algorithm from fgsea package.

The complete GO annotation is required (gene2GO argument), and also a 2 columns data.table with preranked gene identifiants (in first column) based on statistical values (second column).

Defaults fgseaSimple parameters were used for perform test with nperm set to 10,000.
Defaults fgseaMultilevel parameters were used for perform test except the eps arg that was set to 0 for better pvalues estimation.
A gene frequency (%) of leadingEdge/size is added to output data.table.

Value

a fgsea-class object.

References

Korotkevich G, Sukhov V, Sergushichev A (2019). "Fast gene set enrichment analysis." bioRxiv. doi: 10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012.

See Also

Other GO_terms: GOcount(), GOterms_heatmap(), annotate(), create_topGOdata(), gene2GO-class, merge_enrich_terms()

Examples

# gene list
PregnantvsLactate<-data.table::fread(
    system.file(
        "extdata/data/input",
        "pregnantvslactate.complete.txt",
        package = "ViSEAGO"
    ),
    select = c("Id","padj")
)

# rank Id based on statistical value (padj here)
PregnantvsLactate<-data.table::setorder(PregnantvsLactate,padj)

## Not run: 
# connect to Bioconductor
Bioconductor<-ViSEAGO::Bioconductor2GO()

myGENE2GO<-ViSEAGO::annotate(
   "org.Mm.eg.db",
   Bioconductor
)

# run fgseaMultilevel
pregnantvslactate<-ViSEAGO::runfgsea(
    geneSel=PregnantvsLactate,
    gene2GO=myGENE2GO,
    ont="BP",
    method="fgseaMultilevel",
    params=list(
        minSize=5,
        scoreType="pos"
    )
)

## End(Not run)

ViSEAGO documentation built on Nov. 8, 2020, 6:51 p.m.