runGOseq {SMITE} | R Documentation

## Run a GoSeq pathway analysis

### Description

This function allows pathway annotation of identified modules.

### Usage

    runGOseq(pvalue_annotation, p_thresh = 0.05, coverage, type = "reactome")

### Arguments

`pvalue_annotation` | An S4 object of class PvalueAnnotation, for which module-finding has already been performed
`p_thresh` | A numeric specifying a threshold for signficance of FDR (q-values). DEFAULT is alpha=0.05
`coverage` | A data.frame that is a bed file (chr start stop) folowed by a gene name and a numeric specifying the bias data (e.g. gene length or number of probes related to gene)
`type` | Either "kegg" to run KEGG analysis or "reactome" to run a REACTOME analysis

### Details

Goseq analysis is useful since it allows you to assess term/pathway enrichment in a collection of genes, while adjusting for bias data. Potential bias can be from aspects like gene length or probe density that influence the likelihood of finding a particular gene. For more information please see the goseq reference.

The function will compare all of the genes within a module to known pathways and terms to find the terms that are most enriched within a module. In this way, this tool allows a reasearch to find a functional importance of a module.

We currently offer KEGG and REACTOME analysis, although additional pathway tools may be added in the near future.

### Value

A PvalueAnnotation with goseq annotated modules.

### Note

This is a wrapper function written by N. Ari Wijetunga for the package SMITE.

### Author(s)

Matthew D. Young myoung at wehi.edu.au

### References

Young MD, Wakefield MJ, Smyth GK and Oshlack A (2010). Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology, 11, pp. R14.

### See Also

searchGOseq extractGOseq runSpinglass runBioNet extractModules plotModule

### Examples

    ##load sample data with only PvalueObject filled in##
    data(test_annotation_score_data)

    ## NOTE commented out in example.  Please see Vignette for better example ##
    #test_annotation<-runGOseq(pvalue_annotation=test_annotation,
    #coverage=read.table(
    #system.file("extdata", "hg19_symbol_hpaii.sites.inbodyand2kbupstream.bed.gz",
    #package="SMITE"),stringsAsFactors=FALSE),type="kegg")

    ## search for a term ##
    searchGOseq(test_annotation,"Cell cycle")

    ## show goseq analysis for module 1 ##
    #extractGOseq(test_annotation,1)

[Package SMITE version 1.8.0 Index]