fea_gtLinker: FEA - Gene-Term Linker

In FGNet: Functional Gene Networks derived from biological enrichment analyses

Description

Performs the functional enrichment analysis and clustering through Gene-Term Linker [1] (requires internet connection).

Since Gene-Term Linker takes a while to analyze the gene list, the process has been splitted in two steps:

1. fea_gtLinker(): Submits the query

2. fea_gtLinker_getResults(): Retrieves the results of the analysis. It might take a few minutes for the results to become available.

Usage

fea_gtLinker(geneList, organism = "Hs", 
    annotations = c("GO_Biological_Process", "GO_Molecular_Function", 
    "GO_Cellular_Component", "InterPro_Motifs"),
    minSupport = 4, serverWS = "http://gtlinker.cnb.csic.es:8182")

fea_gtLinker_getResults(jobID = NULL, organism = NULL, jobName = NULL, 
    alreadyDownloaded = FALSE, keepTrying = FALSE, 
    serverWeb = "http://gtlinker.cnb.csic.es", 
    serverWS = "http://gtlinker.cnb.csic.es:8182")

Arguments

fea_gtLinker():

geneList	character vector. List of genes to analyze.
annotations	character vector. Annotation spaces for the functional analysis. Available values: "GO_Biological_Process", "GO_Molecular_Function", "GO_Cellular_Component", "InterPro_Motifs".
minSupport	numeric. Minimum number of genes per group.

common arguments:

serverWS	character. GeneTerm Linker webservice server. Available mirrors: "http://gtlinker.cnb.csic.es:8182" If you change the webserice server, make sure to use the matching 'serverWeb' in the following step.
organism	character. "Hs" (Homo sapiens) or "Sc" (Saccharomyces cerevisiae).

fea_gtLinker_getResults():

jobID	numeric. ID of the job/analysis in GeneTerm Linker.
jobName	character. Folder name and prefix for the files.
alreadyDownloaded	logical. If the files have already been downloaded, these will be read instead of downloaded again.
keepTrying	logical. If true, if the job has not finished, it will keep trying to get the results every few seconds.
serverWeb	character. GeneTerm Linker web server. It should match the web service or web address in which the analysis was performed. Available mirrors: "http://gtlinker.cnb.csic.es"

Value

fea_gtLinker() returns the jobID of the analysis

fea_gtLinker_getResults() returns an invisible list with the folowing fields:

queryArgs list with the arguments for the query.

metagroups data.frame containing the metagroups and their information:

• Metagroup: Metagroup ID.

• Size: Number of gene-term sets supporting the metagroup.

• Diameter: Maximum Cosine distance within the GeneTerm-sets of each metagroup (ranges from 0 to 1).

• Similarity: 1 - average Cosine distance within the GeneTerm-sets of each metagroup (ranges from 0 to 1). Distance and similarity calculations are done based on the genes present in the metagroups.

• Silhouette Width: Measures the compactness and proximity of multiple groups (ranges from 1 to -1). Metagroups with negative Silhouette Width usually include diverse annotations and genes with low functional coherence.

• Genes: Genes in the metagroup.

• nGenes: Number of genes in the metagroup.

• nref_list: Number of annotated genes in the reference list.

• pValue: Adjusted p-value.

• Terms: Non-generic terms in the metagroup.

geneTermSets data.frame containing the gene-term sets that support each metagroup.

• Metagroup: Id of the metagroup the gene-term set belongs to.

• Genes: Genes in the gene-term set.

• nGenes: Number of annotated genes in the input list. In brackets: Total number of genes in the input list.

• nref_list: Number of annotated genes in the reference list. In brackets: Total number of genes in the reference list.

• pValue: Adjusted p-value.

• Terms: Terms in the gene-term set.

fileName .txt file with the formatted FEA results.

References

[1] Fontanillo C, Nogales-Cadenas R, Pascual-Montano A, De Las Rivas J (2011) Functional Analysis beyond Enrichment: Non-Redundant Reciprocal Linkage of Genes and Biological Terms. PLoS ONE 6(9): e24289. URL: http://gtlinker.cnb.csic.es

See Also

Other FEA tools:

• fea_david() (Requires internet connection)

• fea_gage()

• fea_topGO()

  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

## Not run: 
### Execute FEA:
genesYeast <- c("ADA2", "APC1", "APC11", "APC2", "APC4", "APC5", "APC9", 
     "CDC16", "CDC23", "CDC26", "CDC27", "CFT1", "CFT2", "DCP1", "DOC1", "FIP1", 
     "GCN5", "GLC7", "HFI1", "KEM1", "LSM1", "LSM2", "LSM3", "LSM4", "LSM5", 
     "LSM6", "LSM7", "LSM8", "MPE1", "NGG1", "PAP1", "PAT1", "PFS2", "PTA1", 
     "PTI1", "REF2", "RNA14", "RPN1", "RPN10", "RPN11", "RPN13", "RPN2", "RPN3", 
     "RPN5", "RPN6", "RPN8", "RPT1", "RPT3", "RPT6", "SGF11", "SGF29", "SGF73", 
     "SPT20", "SPT3", "SPT7", "SPT8", "TRA1", "YSH1", "YTH1")
# Optional expression (1=UP, -1=DW):
genesYeastExpr <- setNames(c(rep(1,29), rep(-1,30)), genesYeast)

# Submit query
jobID <- fea_gtLinker(geneList=genesYeast,organism="Sc")
jobID


### Get results from FEA:
jobID <- 3907019 # job ID of the query
results <- fea_gtLinker_getResults(jobID=jobID)

# To continue the workflow... (see help for further details))
incidMat <- fea2incidMat(results)
functionalNetwork(incidMat)

# Or full report
FGNet_report(results, geneExpr=genesYeastExpr)

## End(Not run)

FGNet documentation built on Nov. 8, 2020, 5:43 p.m.