edge (version 2.4.2)

goana.DGELRT: Gene Ontology or KEGG Analysis of Differentially Expressed Genes

Description

Test for over-representation of gene ontology (GO) terms or KEGG pathways in the up and down differentially expressed genes from a linear model fit.

Usage

## S3 method for class 'DGELRT':
goana(de, geneid = rownames(de), FDR = 0.05, trend = FALSE, ...)
## S3 method for class 'DGELRT':
kegga(de, geneid = rownames(de), FDR = 0.05, trend = FALSE, ...)

Arguments

de

an DGELRT object.

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.

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$genes containing the covariate values. If TRUE, then de$AveLogCPM is used as the covariate.

...

any other arguments are passed to goana.default or kegga.default.

Value

• goana 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.
• P.Down
  p-value for over-representation of GO term in down-regulated genes.
• The row names of the data frame give the GO term IDs. kegga produces a data.frame as above except that the rownames are KEGG pathway IDs, Term become Path and there is no Ont column.

Details

goana performs Gene Ontology enrichment analyses for the up and down differentially expressed genes from a linear model analysis. kegga performs the corresponding analysis for KEGG pathways. The Entrez Gene ID must be supplied for each gene. If trend=FALSE, the function computes one-sided hypergeometric tests equivalent to Fisher's exact test. If trend=TRUE or a covariate is supplied, then a trend is fitted to the differential expression results and the method of Young et al (2010) is used to adjust for this trend. The adjusted test uses Wallenius' noncentral hypergeometric distribution.

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

goana, topGO, kegga, topKEGG

Examples

fit <- glmFit(y, design)
lrt <- glmLRT(fit)
go <- goana(lrt, species="Hs)
topGO(go, ont="BP", sort = "up")
topGO(go, ont="BP", sort = "down")