fea_gtLinker {FGNet} | R Documentation

## FEA - Gene-Term Linker

### 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)

[Package FGNet version 3.24.0 Index]