setTest: setTest

Description

calculates the adjusted p-value for the local hypothesis as defined by testtype and testvalue.

Usage

setTest(pvalue, featureIDs, data, set, testype, testvalue)

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 selected

testype

Character, type of the test: "selfcontained" or "competitive". Choosing the self-contained option will automatically set the threshold to zero and the testvalue is ignored. Choosing the competitive option without a testvalue will set the threshold to the overall estimated proportion of true hypotheses

testvalue

Optional value to test against. Setting this value to c along with testype=="competitive" will lead to testing the null hypothesis against a threshold c. Note: this value needs to be a proportion

Value

The adjusted p-value 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 {
#Generate a vector of pvalues
set.seed(159)

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")

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

# perform a competitive test for the random pathway
settest(pvalues, featureIDs, set=randset, testype = "competitive")

# perform a unified null hypothesis test against 0.2 for a set of size 50
settest(pvalues, featureIDs, set=randset, testype = "competitive", testvalue = 0.2 )

# }