GOEnrich.folder-methods {R3CPET} | R Documentation

## GO enrichment methods

### Description

This helper methods can be called to do GO enrichment by using the `DAVID` web service.

`GOEnrich.networks` can be used to do a GO enrichment of the chromatin maintainer networks.

`GOEnrich.folder` can be called to do a GO enrichment on the gene-list files generated by the method `outputGenesPerClusterToDir`.

There is a `5 secs` delay between each request to not avoid being rejected by the server.

### Usage

    ## S4 method for signature 'character'
    GOEnrich.folder(folder, fdr=0.05,GOlimit=20)

    ## S4 method for signature 'ChromMaintainers'
    GOEnrich.networks(object, fdr=0.05, GOlimit= 5,path="")

### Arguments

`folder` | name of the folder that contains the gene-list files. The files are supposed to have a `.txt` extension. The first column of each file is supposed to contain the genes EntrezID.
`object` | a `"ChromMaintainers"` objects with the `topNodes` already calculated.
`fdr` | cut-off value GO terms with fdr value <= fdr will be considered. Benjamini-Hochberg FDR is used.
`GOlimit` | the number of top GO terms to return.
`path` | the path where to store the generated plot (pdf file). if not specified the plot will be displayed.

### Value

Returns a list of `data.frame` that contain the GO results for each file (or network).

### Author(s)

Mohamed Nadhir Djekidel (nde12@mails.tsinghua.edu.cn)

### References

http://david.abcc.ncifcrf.gov/ (DAVID website)

Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nature Protoc. 2009;4(1):44-57.

### See Also

`outputGenesPerClusterToDir`

### Examples

        ## get the different datasets path
        petFile <- file.path(system.file("example",package="R3CPET"),"HepG2_interactions.txt")
        tfbsFile <- file.path(system.file("example",package="R3CPET"),"HepG2_TF.txt.gz")

     ## Not run:

        x <- ChiapetExperimentData(pet = petFile, tfbs=  tfbsFile, IsBed = FALSE, ppiType="HPRD", filter= TRUE)
        ## build the different indexes
        x <- createIndexes(x)

        ## build networks connecting each interacting regions
        nets<- buildNetworks(x)

        ## infer the networks
        hlda<- InferNetworks(nets)

        ## Get the list of genes in each cluster by default
        ## a folder ClustersGenes will be created
        outputGenesPerClusterToDir(hlda,x)

        ## GO enrichment
        GOEnrich.folder(folder="ClustersGenes/")

    ## End(Not run)

[Package R3CPET version 1.16.0 Index]