
"goana"(de, coef = ncol(de), geneid = rownames(de), FDR = 0.05, species = "Hs", trend = FALSE, ...)
MArrayLM
fit object.nrow(de)
or the name of the column of de$genes
containing the Entrez Gene IDs."Hs"
, "Mm"
, "Rn"
or "Dm"
.de$genes
containing the covariate values.
If TRUE
, then de$Amean
is used as the covariate.goana.default
."BP"
, "CC"
and "MF"
.If trend=FALSE
, the function computes one-sided hypergeometric tests equivalent to Fisher's exact test.
If trend=TRUE
or a covariate is supplied, then a trend is fitted to the differential expression results and the method of Young et al (2010) is used to adjust for this trend.
The adjusted test uses Wallenius' noncentral hypergeometric distribution.
goana.default
, topGO
The goseq package implements a similar GO analysis. The goseq version will work with a variety of gene identifiers, not only Entrez Gene as here, and includes a database of gene length information for various species.
The gostats package also does GO analyses with some different options.
## Not run:
#
# fit <- lmFit(y, design)
# fit <- eBayes(fit)
#
# # Standard GO analysis
# go.fisher <- goana(fit)
# topGO(go.fisher, sort = "up")
# topGO(go.fisher, sort = "down")
#
# # GO analysis adjusting for gene abundance
# go.abund <- goana(fit, geneid = "GeneID", trend = TRUE)
# topGO(go.abund, sort = "up")
# topGO(go.abund, sort = "down")
#
# # GO analysis adjusting for gene length bias
# # (assuming that y$genes$Length contains gene lengths)
# go.len <- goana(fit, geneid = "GeneID", trend = "Length")
# topGO(go.len, sort = "up")
# topGO(go.len, sort = "down")
# ## End(Not run)
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