diagnosticMethods: Diagnostic functions for topGOdata and topGOresult objects.
In ycl6/topGO-Bioc: Enrichment Analysis for Gene Ontology

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

The GenTable function generates a summary of the results of the enrichment analysis.

The showGroupDensity function plots the distributions of the gene' scores/ranks inside a GO term.

The printGenes function shows a short summary of the top genes annotated to the specified GO terms.

GenTable(object, ...)

showGroupDensity(object, whichGO, ranks = FALSE, rm.one = TRUE) 

printGenes(object, whichTerms, file, ...)

`object`	an object of class `topGOdata`.
`whichGO`	the GO terms for which the plot should be generated.
`ranks`	if ranks should be used instead of scores.
`rm.one`	the p-values which are 1 are removed.
`whichTerms`	character vector listing the GO terms for which the summary should be printed.
`file`	character string specifying the file in which the results should be printed.
`...`	Extra arguments for `GenTable` can be: ... one or more objects of class `topGOresult`. `orderBy` if more than one `topGOresult` object is given then `orderBy` gives the index of which scores will be used to order the resulting table. Can be an integer index or a character vector given the name of the `topGOresult` object. `ranksOf` same as `orderBy` argument except that this parameter shows the relative ranks of the specified result. `topNodes` the number of top GO terms to be included in the table. `numChar` the GO term definition will be truncated such that only the first `numChar` characters are shown. Extra arguments for `printGenes` can be: `chip` character string containing the name of the Bioconductor annotation package for a microarray chip. `numChar` the gene description is trimmed such that it has `numChar` characters. `simplify` logical variable affecting how the results are returned. `geneCutOff` the maximal number of genes shown for each term. `pvalCutOff` only the genes with a p-value less than `pvalCutOff` are shown. `oneFile` if `TRUE` then a file for each GO term is generated.

GenTable is an easy to use function for summarising the most significant GO terms and the corresponding p-values. The function dispatches for topGOdata and topGOresult objects, and it can take an arbitrary number of the later, making comparison between various results easier.

Note: One needs to type the complete attribute names (the exact name) of this function, like: topNodes = 5, rankOf = "resultFis", etc. This being the price paid for flexibility of specifying different number of topGOdata objects.

The showGroupDensity function analyse the distribution of the gene-wise scores for a specified GO term. The function will show the distribution of the genes in a GO term compared with the complementary set, using a lattice plot.

printGenes The function will generate a table with all the probes annotated to the specified GO term. Various type of identifiers, the gene name and the gene-wise statistics are provided in the table.

One or more GO identifiers can be given to the function using the whichTerms argument. When more than one GO is specified, the function returns a list of data.frames, otherwise only one data.frame is returned.

The function has a argument file which, when specified, will save the results into a file using the CSV format.

For the moment the function will work only when the chip used has an annotation package available in Bioconductor. It will not work with other type of custom annotations.

A data.frame or a list of data.fames.

Adrian Alexa

groupStats-class, getSigGroups-methods

data(GOdata)


########################################
## GenTable
########################################

## load two topGOresult sample objects: resultFisher and resultKS
data(results.tGO)

## generate the result of Fisher's exact test
sig.tab <- GenTable(GOdata, Fis = resultFisher, topNodes = 20)

## results of both test
sig.tab <- GenTable(GOdata, resultFisher, resultKS, topNodes = 20)

## results of both test with specified names
sig.tab <- GenTable(GOdata, Fis = resultFisher, KS = resultKS, topNodes = 20)

## results of both test with specified names and specified ordering
sig.tab <- GenTable(GOdata, Fis = resultFisher, KS = resultKS, orderBy = "KS", ranksOf = "Fis", topNodes = 20)


########################################
## showGroupDensity
########################################

goID <- "GO:0006091"
print(showGroupDensity(GOdata, goID, ranks = TRUE))
print(showGroupDensity(GOdata, goID, ranks = FALSE, rm.one = FALSE))



########################################
## printGenes
########################################

## Not run: 
library(hgu95av2.db)
goID <- "GO:0006629"

gt <- printGenes(GOdata, whichTerms = goID, chip = "hgu95av2.db", numChar = 40)

goIDs <- c("GO:0006629", "GO:0007076")
gt <- printGenes(GOdata, whichTerms = goIDs, chip = "hgu95av2.db", pvalCutOff = 0.01)

gt[goIDs[1]]

## End(Not run)