goana

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Gene Ontology or KEGG Pathway Analysis

Test for over-representation of gene ontology (GO) terms or KEGG pathways in one or more sets of genes, optionally adjusting for abundance or gene length bias.

Keywords
gene set test
Usage
"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)
Arguments
de
a vector of Entrez Gene IDs, or a list of such vectors, or an MArrayLM fit object.
coef
column number or column name specifying for which coefficient or contrast differential expression should be assessed.
geneid
Entrez Gene identifiers. Either a vector of length nrow(de) or the name of the column of de$genes containing the Entrez Gene IDs.
FDR
false discovery rate cutoff for differentially expressed genes. Numeric value between 0 and 1.
species
character string specifying the species. Possible values include "Hs" (human), "Mm" (mouse), "Rn" (rat), "Dm" (fly) or "Pt" (chimpanzee), but other values are possible if the corresponding organism package is available. See alias2Symbol for other possible values. Ignored if species.KEGG or is not NULL or if gene.pathway and pathway.names are not NULL.
species.KEGG
three-letter KEGG species identifier. See http://www.kegg.jp/kegg/catalog/org_list.html or http://rest.kegg.jp/list/organism for possible values. Ignored if gene.pathway and pathway.names are not NULL.
convert
if TRUE then KEGG gene identifiers will be converted to NCBI Entrez Gene identifiers. Note that KEGG IDs are the same as Entrez Gene IDs for most species anyway.
gene.pathway
data.frame linking genes to pathways. First column gives gene IDs, second column gives pathway IDs. By default this is obtained automatically by getGeneKEGGLinks(species.KEGG).
remove.qualifier
if TRUE, the species qualifier will be removed from the pathway names.
pathway.names
data.frame giving full names of pathways. First column gives pathway IDs, second column gives pathway names. By default this is obtained automatically using getKEGGPathwayNames(species.KEGG, remove=TRUE).
trend
adjust analysis for gene length or abundance? Can be logical, or a numeric vector of covariate values, or the name of the column of de$genes containing the covariate values. If TRUE, then de$Amean is used as the covariate.
universe
vector specifying the set of Entrez Gene identifiers to be the background universe. If NULL then all Entrez Gene IDs associated with any gene ontology term will be used as the universe.
prior.prob
optional numeric vector of the same length as 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.
covariate
optional numeric vector of the same length as universe giving a covariate against which prior.prob should be computed. Ignored if universe is NULL.
plot
logical, should the prior.prob vs covariate trend be plotted?
...
any other arguments in a call to the MArrayLM method are passed to the default method.
Details

These functions performs a over-representation analysis for Gene Ontology terms or KEGG pathways in a list of Entrez Gene IDs. The default method accepts the gene list as a vector of gene IDs, while the MArrayLM method extracts the gene lists automatically from a linear model fit object.

goana uses annotation from the appropriate Bioconductor organism package. The species can be any character string XX for which an organism package org.XX.eg.db exists and is installed. See alias2Symbol for other possible values for species.

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 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.

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.

Value

The goana default method produces a data frame with a row for each GO term and the following columns:
Term
GO term.
Ont
ontology that the GO term belongs to. Possible values are "BP", "CC" and "MF".
N
number of genes in the GO term.
DE
number of genes in the DE set.
P.DE
p-value for over-representation of the GO term in the set.
The last two column names above assume one gene set with the name 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:
Term
GO term.
Ont
ontology that the GO term belongs to. Possible values are "BP", "CC" and "MF".
N
number of genes in the GO term.
Up
number of up-regulated differentially expressed genes.
Down
number of down-regulated differentially expressed genes.
P.Up
p-value for over-representation of GO term in up-regulated genes. Not adjusted for multiple testing.
P.Down
p-value for over-representation of GO term in down-regulated genes. Not adjusted for multiple testing.
The row names of the data frame give the GO term IDs.The output from kegga is the same except that row names become KEGG pathway IDs, Term becomes Pathway and there is no Ont column.

Note

kegga requires an internet connection unless gene.pathway and pathway.names are both supplied.

The default for kegga with species="Dm" changed from convert=TRUE to convert=FALSE in limma 3.27.8. Users wanting to use Entrez Gene IDs for Drosophila should set convert=TRUE, otherwise fly-base IDs are assumed.

References

Young, M. D., Wakefield, M. J., Smyth, G. K., Oshlack, A. (2010). Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology 11, R14. http://genomebiology.com/2010/11/2/R14

See Also

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.

Aliases
  • goana
  • goana.default
  • goana.MArrayLM
  • kegga
  • kegga.default
  • kegga.MArrayLM
  • getGeneKEGGLinks
  • getKEGGPathwayNames
Examples
## 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)
Documentation reproduced from package limma, version 3.28.14, License: GPL (>=2)

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