"goana"(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, trend = FALSE, ...) "goana"(de, universe = NULL, species = "Hs", prior.prob = NULL, covariate=NULL, plot=FALSE, ...) "kegga"(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, trend = FALSE, ...) "kegga"(de, universe = NULL, species = "Hs", species.KEGG = NULL, convert = FALSE, gene.pathway = NULL, pathway.names = NULL, prior.prob = NULL, covariate=NULL, plot=FALSE, ...) getGeneKEGGLinks(species.KEGG = "hsa", convert = FALSE) getKEGGPathwayNames(species.KEGG = NULL, remove.qualifier = FALSE)
nrow(de)or the name of the column of
de$genescontaining the Entrez Gene IDs.
"Pt"(chimpanzee), but other values are possible if the corresponding organism package is available. See
alias2Symbolfor other possible values. Ignored if
species.KEGGor is not
TRUEthen KEGG gene identifiers will be converted to NCBI Entrez Gene identifiers. Note that KEGG IDs are the same as Entrez Gene IDs for most species anyway.
TRUE, the species qualifier will be removed from the pathway names.
de$genescontaining the covariate values. If
de$Ameanis used as the covariate.
NULLthen all Entrez Gene IDs associated with any gene ontology term will be used as the universe.
universegiving the prior probability that each gene in the universe appears in a gene set. Will be computed from
covariateif the latter is provided. Ignored if
universegiving a covariate against which
prior.probshould be computed. Ignored if
covariatetrend be plotted?
MArrayLMmethod are passed to the default method.
goanadefault method produces a data frame with a row for each GO term and the following columns:
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
MArrayLMobjects produces a data frame with a row for each GO term and the following columns:
keggais the same except that row names become KEGG pathway IDs,
Pathwayand there is no
MArrayLMmethod extracts the gene lists automatically from a linear model fit object.
goana uses annotation from the appropriate Bioconductor organism package.
species can be any character string XX for which an organism package org.XX.eg.db exists and is installed.
alias2Symbol for other possible values for
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.
kegga obtains the KEGG annotation for the specified species from the http://rest.kegg.jp website.
Alternatively one can supply the required pathway annotation to
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.
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.
MArrayLM object computes the
prior.prob vector automatically when
trend is non-
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).
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
trend=TRUE or a covariate is supplied, then a trend is fitted to the differential expression results and this is used to set
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
kegga computes gene length or abundance bias using
tricubeMovingAverage instead of monotonic regression.
tricubeMovingAverage does not enforce monotonicity, it has the advantage of numerical stability when
de contains only a small number of genes.
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.
## Not run: # ## 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 previous # ## End(Not run)