## S3 method for class 'MArrayLM':
goana(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, trend = FALSE, ...)
## S3 method for class 'default':
goana(de, universe = NULL, species = "Hs", prior.prob = NULL, covariate=NULL,
plot=FALSE, \dots)
## S3 method for class 'MArrayLM':
kegga(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, trend = FALSE, ...)
## S3 method for class 'default':
kegga(de, universe = NULL, species = "Hs", species.KEGG = NULL, convert = FALSE,
gene.pathway = NULL, pathway.names = NULL,
prior.prob = NULL, covariate=NULL, plot=FALSE, \dots)
getGeneKEGGLinks(species.KEGG = "hsa", convert = FALSE)
getKEGGPathwayNames(species.KEGG = NULL, remove.qualifier = FALSE)MArrayLM fit object.nrow(de) or the name of the column of de$genes containing the Entrez Gene IDs."Hs", "Mm", "Rn", "Dm" or "Pt". Ignored if species.KEGG or is not NULL or if gene.pathway and pathway.names are not NULL.gene.pathway and pathway.names are not NULL.TRUE then KEGG gene identifiers will be converted to NCBI Entrez Gene identifiers. Note that KEGG IDs are the same as Entrez Gene IDs for most species anyway.getGeneKEGGLinks(species.KEGG).TRUE, the species qualifier will be removed from the pathway names.getKEGGPathwayNames(species.KEGG, remove=TRUE).de$genes containing the covariate values.
If TRUE, then de$Amean is used as the covariate.NULL then all Entrez Gene IDs associated with any gene ontology term will be used as the universe.universe giving the prior probability that each gene in the universe appears in a gene set.
Will be computed from covariate if the latter is provided.
Ignored if universe is NULL.universe giving a covariate against which prior.prob should be computed.
Ignored if universe is NULL.prior.prob vs covariate trend be plotted?MArrayLM method are passed to the default method.goana default method produces a data frame with a row for each GO term and the following columns:"BP", "CC" and "MF".DE set.DE.
In general, there will be a pair of such columns for each gene set and the name of the set will appear in place of "DE".
The goana method for MArrayLM objects produces a data frame with a row for each GO term and the following columns:"BP", "CC" and "MF".kegga is the same except that row names become KEGG pathway IDs, Term becomes Pathway and there is no Ont column.MArrayLM method extracts the gene lists automatically from a linear model fit object.
goana uses annotation from the appropriate Bioconductor organism package.
goana can be used for any of the five species specified.
kegga reads KEGG pathway annotation from the KEGG website.
Note that the species name can be provided in either Bioconductor or KEGG format.
kegga can be used for any species supported by KEGG, of which there are more than 14,000 possibilities.
By default, kegga obtains the KEGG annotation for the specified species from the kegga in the form of two data.frames.
If this is done, then an internet connection is not required.
The ability to supply data.frame annotation to kegga means that kegga can in principle be used to analyze any user-supplied gene sets.
The default goana and kegga methods accept a vector prior.prob giving the prior probability that each gene in the universe appears in a gene set.
This vector can be used to correct for unwanted trends in the differential expression analysis associated with gene length, gene abundance or any other covariate.
The MArrayLM object computes the prior.prob vector automatically when trend is non-NULL.
If prior.prob=NULL, the function computes one-sided hypergeometric tests equivalent to Fisher's exact test.
If prior probabilities are specified, then a test based on the Wallenius' noncentral hypergeometric distribution is used to adjust for the relative probability that each gene will appear in a gene set, following the approach of Young et al (2010).
The MArrayLM methods performs over-representation analyses for the up and down differentially expressed genes from a linear model analysis.
In this case, the universe is all the genes found in the fit object.
trend=FALSE is equivalent to prior.prob=NULL.
If trend=TRUE or a covariate is supplied, then a trend is fitted to the differential expression results and this is used to set prior.prob.
The statistical approach provided here is the same as that provided by the goseq package, with one methodological difference and a few restrictions.
Unlike the goseq package, the gene identifiers here must be Entrez Gene IDs and the user is assumed to be able to supply gene lengths if necessary.
The goseq package has additional functionality to convert gene identifiers and to provide gene lengths.
The only methodological difference is that goana and kegga computes gene length or abundance bias using tricubeMovingAverage instead of monotonic regression.
While tricubeMovingAverage does not enforce monotonicity, it has the advantage of numerical stability when de contains only a small number of genes.topGO, topKEGG
The goseq package provides an alternative implementation of methods from Young et al (2010).
Unlike the limma functions documented here, goseq will work with a variety of gene identifiers and includes a database of gene length information for various species.
The gostats package also does GO analyses without adjustment for bias but with some other options.
See 10.GeneSetTests for a description of other functions used for gene set testing.## Linear model usage:
fit <- lmFit(y, design)
fit <- eBayes(fit)
# Standard GO analysis
go.fisher <- goana(fit, species="Hs")
topGO(go.fisher, sort = "up")
topGO(go.fisher, sort = "down")
# GO analysis adjusting for gene abundance
go.abund <- goana(fit, geneid = "GeneID", trend = TRUE)
topGO(go.abund, sort = "up")
topGO(go.abund, sort = "down")
# GO analysis adjusting for gene length bias
# (assuming that y$genes$Length contains gene lengths)
go.len <- goana(fit, geneid = "GeneID", trend = "Length")
topGO(go.len, sort = "up")
topGO(go.len, sort = "down")
## Default usage with a gene list:
go.de <- goana(list(DE1 = EG.DE1, DE2 = EG.DE2, DE3 = EG.DE3))
topGO(go.de, sort = "DE1")
topGO(go.de, sort = "DE2")
topGO(go.de, ontology = "BP", sort = "DE3")
topGO(go.de, ontology = "CC", sort = "DE3")
topGO(go.de, ontology = "MF", sort = "DE3")
## Standard KEGG analysis
k <- kegga(fit, species="Hs")
k <- kegga(fit, species.KEGG="hsa") # equivalent to previousRun the code above in your browser using DataLab