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WGCNA (version 1.13)

GOenrichmentAnalysis: Calculation of GO enrichment (experimental)

Description

WARNING: This function should be considered experimental. The arguments and resulting values (in particular, the enrichment p-values) are not yet finalized and may change in the future. The function should only be used to get a quick and rough overview of GO enrichment in the modules in a data set; for a publication-quality analysis, please use an established tool.

Using Bioconductor's annotation packages, this function calculates enrichments and returns terms with best enrichment values.

Usage

GOenrichmentAnalysis(labels, 
                     entrezCodes, 
                     yeastORFs = NULL,
                     organism = "human", 
                     ontologies = c("BP", "CC", "MF"), 
                     evidence = c("IMP", "IGI", "IPI", "ISS", "IDA", "IEA", "TAS", "NAS", "ND", "IC"), 
                     includeOffspring = TRUE, 
                     backgroundType = "givenInGO",
                     removeDuplicates = TRUE,
                     leaveOutLabel = NULL,
                     nBestP = 10, pCut = NULL, 
                     nBiggest = 0, 
                     verbose = 2, indent = 0)

Arguments

labels
cluster (module, group) labels of genes to be analyzed. Either a single vector, or a matrix. In the matrix case, each column will be analyzed separately; analyzing a collection of module assignments in one function call will be faster than calling the fun
entrezCodes
Entrez (a.k.a. LocusLink) codes of the genes whose labels are given in labels. A single vector; the i-th entry corresponds to row i of the matrix labels (or to the i-the entry if labels is a vec
yeastORFs
if organism=="yeast" (below), this argument can be used to input yeast open reading frame (ORF) identifiers instead of Entrez codes. Since the GO mappings for yeast are provided in terms of ORF identifiers, this may lead to a more accurate GO
organism
character string specifying the organism for which to perform the analysis. Recognized values are (unique abbreviations of) "human", "mouse", "rat", "malaria", "yeast", "fly", "bovine", "worm", "canine", "zebrafish", "chicken".
ontologies
vector of character strings specifying GO ontologies to be included in the analysis. Can be any subset of "BP", "CC", "MF". The result will contain the terms with highest enrichment in each specified category, plus a separate list of terms w
evidence
vector of character strings specifying admissible evidence for each gene in its specific term. GO uses the following codes: IMP: inferred from mutant phenotype; IGI: inferred from genetic interaction; IPI: inferred from physical interaction; ISS: inferred
includeOffspring
logical: should genes belonging to the offspring of each term be included in the term? As a default, only genes belonging directly to each term are associated with the term. Note that the calculation of enrichments with offspring included can be quite slo
backgroundType
specification of the background to use. Recognized values are (unique abbreviations of) "allGiven", "allInGO", "givenInGO", meaning that the functions will take all genes given in labels as backround ("allGiven"), al
removeDuplicates
logical: should duplicate entries in entrezCodes be removed? If TRUE, only the first occurence of each unique Entrez code will be kept. The cluster labels labels will be adjusted accordingly.
leaveOutLabel
optional specifications of module labels for which enrichment calculation is not desired. Can be a single label or a vector of labels to be ignored. However, if in any of the sets no labels are left to calculate enrichment of, the function will stop with
nBestP
specifies the number of terms with highest enrichment whose detailed information will be returned.
pCut
alternative specification of terms to be returned: all terms whose enrichment p-value is more significant than pCut will be returned. If pCut is given, nBestP is ignored.
nBiggest
in addition to returning terms with highest enrichment, terms that contain most of the genes in each cluster can be returned by specifying the number of biggest terms per cluster to be returned. This may be useful for development and testing purposes.
verbose
integer specifying the verbosity of the function. Zero means silent, positive values will cause the function to print progress reports.
indent
integer specifying indentation of the diagnostic messages. Zero means no indentation, each unit adds two spaces.

Value

  • A list with the following components:
  • keptForAnalysislogical vector with one entry per given gene. TRUE if the entry was used for enrichment analysis. Depending on the setting of removeDuplicates above, only a single entry per gene may be used.
  • inGOlogical vector with one entry per given gene. TRUE if the gene belongs to any GO term, FALSE otherwise. Also FALSE for genes not used for the analysis because of duplication.
  • If input labels contained only one vector of labels, the following components:
  • countsInTermsa matrix whose rows correspond to given cluster, and whose columns correspond to GO terms, contaning number of genes in the intersection of the corresponding module and GO term. Row and column names are set appropriately.
  • enrichmentPa matrix whose rows correspond to given cluster, and whose columns correspond to GO terms, contaning enrichment p-values of each term in each cluster. Row and column names are set appropriately.
  • bestPTermsa list of lists with each inner list corresponding to an ontology given in ontologies in input, plus one component corresponding to all given ontologies combined. The name of each component is set appropriately. Each inner list contains two components: enrichment is a data frame containing the highest enriched terms for each module; and forModule is a list of lists with one inner list per module, appropriately named. Each inner list contains one component per term. This component is yet another list and contains components termName (term name), enrichmentP (enrichment P value), termDefinition (GO term definition), termOntology (GO term ontology), geneCodes (Entrez codes of module genes in this term), genePositions (indices of the genes listed in geneCodes within the given labels). Thus, to obtain information on say the second term of the 5th module in ontology BP, one can look at the appropriate row of bestPTerms$BP$enrichment, or one can reference bestPTerms$BP$forModule[[5]][[2]]. The author of the function apologizes for any confusion this structure of the output may cause.
  • biggestTermsa list of the same format as bestPTerms, containing information about the terms with most genes in the module for each supplied ontology.
  • If input labels contained more than one vector, instead of the above components the return value contains a list named setResults that has one component per given set; each component is a list containing the above components for the corresponding set.

Details

This function is basically a wrapper for the annotation packages available from Bioconductor. It requires the packages GO.db, AnnotationDbi, and org.xx.eg.db, where xx is the code corresponding to the organism that the user wishes to analyze (e.g., Hs for human Homo Sapiens, Mm for mouse Mus Musculus etc). For each cluster specified in the input, the function calculates all enrichments in the specified ontologies, and collects information about the terms with highest enrichment. The enrichment p-value is calculated using Fisher exact test. As background we use all of the supplied genes that are present in at least one term in GO (in any of the ontologies).

For best results, the newest annotation libraries should be used. Because of the way Bioconductor is set up, to get the newest annotation libraries you may have to use the current version of R.

See Also

Bioconductor's annotation packages such as GO.db and organism-specific annotation packages such as org.Hs.eg.db.