setTest: setTest
In rSEA: Simultaneous Enrichment Analysis

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

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

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

`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

The adjusted p-value 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

setTDP SEA

## 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 )


## End(Not run)