setTDP: setTDP
In rSEA: Simultaneous Enrichment Analysis

Description Usage Arguments Value Author(s) References See Also Examples

Estimates the TDP of the specified set of features.

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

`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

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.

Mitra Ebrahimpoor

m.ebrahimpoor@lumc.nl

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

setTest, SEA

## 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)


## End(Not run)