Description Usage Arguments Value Author(s) See Also Examples
These functions evaluate gene set rankings obtained from applying enrichment methods to multiple datasets. This allows to assess resulting rankings for granularity (how many gene sets have a unique pvalue?) and statistical significance (how many gene sets have a pvalue below a significance threshold?).
1 2 3  evalNrSigSets(ea.ranks, alpha = 0.05, padj = "none", perc = TRUE)
evalNrSets(ea.ranks, uniq.pval = TRUE, perc = TRUE)

ea.ranks 
Enrichment analysis rankings. A list with an entry for each enrichment method applied. Each entry is a list that stores for each dataset analyzed the resulting gene set ranking as obtained from applying the respective method to the respective dataset. 
alpha 
Statistical significance level. Defaults to 0.05. 
padj 
Character. Method for adjusting pvalues to multiple testing.
For available methods see the man page of the stats function

perc 
Logical. Should the percentage or absolute number of gene sets
be returned? Percentage is typically more useful for comparison between
rankings with a potentially different total number of gene sets. Defaults
to 
uniq.pval 
Logical. Should the number of gene sets with a unique
pvalue or the total number of gene sets per ranking be returned? Defaults
to 
A list of numeric vectors storing for each method the number of (significant) gene sets for each dataset analyzed. If each element of the resulting list is of equal length (corresponds to successful application of each enrichment method to each dataset), the list is automatically simplified to a numeric matrix (rows = datasets, columns = methods).
Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>
runEA
to apply enrichment methods to multiple
datasets; readResults
to read saved rankings as an input for
the evalfunctions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  # simulated setup:
# 2 methods & 2 datasets
methods < paste0("m", 1:2)
data.ids < paste0("d", 1:2)
# simulate gene set rankings
getRankingForDataset < function(d)
{
r < EnrichmentBrowser::makeExampleData("ea.res")
EnrichmentBrowser::gsRanking(r, signif.only=FALSE)
}
getRankingsForMethod < function(m)
{
rs < lapply(data.ids, getRankingForDataset)
names(rs) < data.ids
rs
}
ea.ranks < lapply(methods, getRankingsForMethod)
names(ea.ranks) < methods
# evaluate
evalNrSets(ea.ranks)
evalNrSigSets(ea.ranks)

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