runGOseq: Run a GoSeq pathway analysis
In SMITE: Significance-based Modules Integrating the Transcriptome and Epigenome

Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function allows pathway annotation of identified modules.

1	runGOseq(pvalue_annotation, p_thresh = 0.05, supply_cov=TRUE, coverage=NULL, type = "reactome")

`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
`supply_cov`	A logical specifying whether or not the user wants to supply their own coverage (TRUE), or would like SMITE to calculate the coverage based on methylation data already inputted. DEFAULT is TRUE.
`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). DEFAULT is null
`type`	Either "kegg" to run KEGG analysis or "reactome" to run a REACTOME analysis

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.

A PvalueAnnotation with goseq annotated modules.

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

Matthew D. Young myoung at wehi.edu.au

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.

searchGOseq extractGOseq runSpinglass runBioNet extractModules plotModule

##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)