Performs enrichment analysis, given a contongency table of codon counts.
p values are calculated by binomial test, adjustment for multiple testing
can be performed by any of the
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Numeric, discard categories with p value below this
threshold. By default, no threshold is set (
Character, one of the
Numeric, discard categories with adjusted p value below
this threshold. By default, no threshold is set (
AnnotatedDataFrame object, or a list of those; data in
each object has category values in rows, and the following columns:
category, a character vector of annotation categories
all, a numeric vector of integers, coresponding to sequence counts for each annotation category, in the background gene set (universe).
a numeric vector(s) of integers, coresponding to sequence counts for each annotation category, in the set of genes for which enrichment is calculated, i.e. the predefined subset of (usually highly expressed) genes in the universe (named for the corresponding 'crossTab' column).
enrichment, calculated as the ratio: (scaled sample counts - scaled backg. counts) / scaled backg. counts * 100, where scaling means that sample counts are simply increased by 1, and background counts are multiplied by ratio of summed sample counts and summed backgroun counts, and also increased by 1
M, log ratios of scaled counts
A, mean average of scaled counts
pvals, p values for exact binomial test
padj, p values corrected by BH method.
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require(Biobase) # create contingency table s <- getKO(HD59) v <- as.numeric(MELP(HD59, ribosomal = TRUE)) ct <- crossTab(s, v) # enrichment analysis enr <- enrichment(ct) enr # for help, see `?Biobase::AnnotatedDataFrame` head(pData(enr)) enr <- enrichment(ct, pAdjustMethod = "holm") head(pData(enr)) enr <- enrichment(ct, pvalueCutoff = 0.05) head(pData(enr)) enr <- enrichment(ct, padjCutoff = 0.05) head(pData(enr))
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