bandle-EFDR: Compute the expected False Discovery Rate

In ococrook/bandle: An R package for the Bayesian analysis of differential subcellular localisation experiments

Compute the expected False Discovery Rate

Description

The EFDR for a given threshold is equal to the sum over all proteins that exceed that threshold of one minus the posterior probability of differential localisations, divides by the total number of proteins with probabilities of differential localisation greater than that threshold.

Usage

EFDR(prob, threshold = 0.9)

Arguments

prob	A numeric indicating probabilities of differential localisation
threshold	A numeric indicating the probability threshold. The default is 0.90.

Value

The expected false discovery rate for a given threshold

Examples

library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))
d1 <- tansim$lopitrep
control1 <- d1[1:3]
treatment1 <- d1[4:6]
mcmc1 <- bandle(objectCond1 = control1, objectCond2 = treatment1, gpParams = gpParams,
                                     fcol = "markers", numIter = 10L, burnin = 1L, thin = 2L,
                                     numChains = 1, BPPARAM = SerialParam(RNGseed = 1))
mcmc1 <- bandleProcess(mcmc1)
dp <- diffLocalisationProb(mcmc1)
EFDR(dp, threshold = 0.5)

ococrook/bandle documentation built on July 13, 2022, 5:54 a.m.