FEA - topGO

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Description

Performs the functional enrichment analysis through topGO [1].

Usage

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fea_topGO(geneList, geneIdType = "ENSEMBL", geneLabels=NULL, organism = "Hs", 
    annotations = c("GO_BP", "GO_MF", "GO_CC"), evidence=NULL,
    genesUniverse = NULL, refPackage = NULL, 
    geneID2GO = NULL, nodeSize = 5, pValThr = 0.01, testStat = NULL, 
    jobName = NULL)

Arguments

geneList

character vector. List of genes to analyze.

geneIdType

character. Type of gene identifier should be available for the organism package.

geneLabels

named character vector. Gene name or label to use in the report/plots instead of the original gene ID. The vector names should be the gene ID and the content of the vector the gene label. The resulting geneTermSets table will contain the original gene ID column (geneIDs) and the label column (Genes).

organism

two letter code for the organism. See: data(organisms);organisms

annotations

character vector. Annotation spaces for the functional analysis. Accepted values: "GO_BP", "GO_MF", "GO_CC".

evidence

character vector. Required evidence code for GO annotations. If NULL no filtering is done (all annotations are used). For full list, see the organism "EVIDENCE" keys: i.e keys(org.Hs.eg.db, keytype="EVIDENCE"). For non-comprehensive code description: data(GOEvidenceCodes).

genesUniverse

character vector. List of genes used for background (i.e. all genes available in the chip).

refPackage

character. Name of the package to use for calculating the genes universe. A Chip package is recommended. If NULL the genes universe is set as all the genes available in the organism package.

geneID2GO

GO gene sets. If NULL it is calculated automatically.

nodeSize

numeric. Minimum size of GO terms. TopGo authors recommend 5-10 for more stable results, 1 for no prune.

pValThr

numeric. P-value threshold.

testStat

classicCount from toGO. If NULL: GOFisherTest is used.

jobName

character. Folder name and prefix for the files.

Value

Invisible list with the folowing fields:
queryArgs list with the arguments for the query.

clusters Empty list. only for compatibility.

geneTermSets data.frame containing the gene-term sets.

  • Ont: Ontology to wich the term belongs (BP, MF or CC)

  • Terms: Term in the gene-term set.

  • Genes: Genes in the gene-term set.

  • GenesIDs: In case GeneLabels was provided, original gene ID.

  • Other stats provided by topGO: Annotated, Significant, Expected, classic.

fileName .txt file with the formatted FEA results.

References

[1] Adrian Alexa and Jorg Rahnenfuhrer (2010) topGO: Enrichment analysis for Gene Ontology. R package version 2.16.0. URL: http://www.bioconductor.org/packages/release/bioc/html/topGO.html

See Also

Other FEA tools:

  • fea_david() (Requires internet connection)

  • fea_gtLinker() & fea_gtLinker_getResults() (Requires internet connection)

  • fea_gage()

    To import results from a previous/external FEA analysis: format_david(), format_results() and readGeneTermSets().

Overview of the package: FGNet

Package tutorial: vignette("FGNet-vignette")

Examples

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## Not run: 

# Load/format gene list:
geneList <- c("YBL084C", "YDL008W", "YDR118W", "YDR301W", "YDR448W", "YFR036W",
    "YGL240W", "YHR166C", "YKL022C", "YLR102C", "YLR115W", "YLR127C", "YNL172W", 
    "YOL149W", "YOR249C")

library(org.Sc.sgd.db)
geneLabels <- unlist(as.list(org.Sc.sgdGENENAME)[geneList])

geneExpr <- setNames(c(rep(1,10),rep(-1,5)), geneLabels) 

# FEA (using directly the gene names): 
results <- fea_topGO(geneLabels, geneIdType="GENENAME", organism="Sc") 

# FEA (using the gene ID, and replacing it by the label after the FEA): 
results <- fea_topGO(geneList, geneIdType="ENSEMBL", 
    geneLabels=geneLabels, organism="Sc") 

# To continue the workflow... (see help for further details)
FGNet_report(results, geneExpr=geneExpr)

incidMat <- fea2incidMat(results, geneExpr=geneExpr)
functionalNetwork(incidMat)
    
    

## End(Not run)