GOEnrich-methods: GO enrichment methods

In sirusb/R3CPET: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process

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 ([e-mail])

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)

sirusb/R3CPET documentation built on Oct. 12, 2020, 6 p.m.