fea_topGO: FEA - topGO
In FGNet: Functional Gene Networks derived from biological enrichment analyses

Description Usage Arguments Value References See Also Examples

Performs the functional enrichment analysis through topGO [1].

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)

`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.

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.

[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

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")

## 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)