setTDP: setTDP

Description

Estimates the TDP of the specified set of features.

Usage

setTDP(pvalue, featureIDs, data, set, alpha = 0.05)

Arguments

pvalue

The vector of p-values. It can be the name of the covariate representing the Vector of raw p-values in the data or a single vector but in the latter case it should match the featureIDs vector

featureIDs

The vector of feature IDs. It can be the name of the covariate representing the IDs in the data or a single vector but in the latter case it should match the pvalue vector

data

Optional data frame or matrix containing the variables in pvalue and featureIDs

set

The selection of features defining the feature-set based on the the featureIDs. If missing, the set of all features is evaluated

alpha

The type I error allowed. The default is 0.05. NOTE: this shouls be consistent across the study

Value

A named vector including the lower bound and point estimate for the true discovery proportion (TDP) of the specified test for the feature-set is returned.

References

Mitra Ebrahimpoor, Pietro Spitali, Kristina Hettne, Roula Tsonaka, Jelle Goeman, Simultaneous Enrichment Analysis of all Possible Gene-sets: Unifying Self-Contained and Competitive Methods, Briefings in Bioinformatics, , bbz074, https://doi.org/10.1093/bib/bbz074

Examples

Run this code

# NOT RUN {
# }
# NOT RUN {
set.seed(159)
#generate random p-values with pseudo IDs
m<- 100
pvalues <- runif(m,0,1)^5
featureIDs <- as.character(1:m)

# perform a self-contained test for all features
settest(pvalues, featureIDs, testype = "selfcontained")

# estimate the proportion of true discoveries among all m features
settdp(pvalues, featureIDs)

# create a random pathway of size 60
randset=as.character(c(sample(1:m, 60)))


# estimate the proportion of true discoveries in a random set of size 50
settdp(pvalues, featureIDs, set=randset)

# }
# NOT RUN {
# }