R/bandle-utils.R
bandle-differentiallocalisation.RdThese functions implement helper functions for the bandle method
diffLocalisationProb(params)
bootstrapdiffLocprob(params, top = 20, Bootsample = 5000, decreasing = TRUE)
binomialDiffLocProb(params, top = 20, nsample = 5000, decreasing = TRUE)An instance of bandleParams
The number of proteins for which to sample from the binomial distribution
Number of Bootstramp samples. Default is 5000
Starting at protein most likely to be differentially localization
how many samples to return from the binomial distribution
returns a named vector of differential localisation probabilities
returns a matrix of size Bootsample * top containing bootstrap
returns a list containing empirical binomial samples
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))
#> You haven't provided a seed, you may wish to provide a seed
#>
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mcmc1 <- bandleProcess(mcmc1)
dp <- diffLocalisationProb(mcmc1)
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))
#> You haven't provided a seed, you may wish to provide a seed
#>
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mcmc1 <- bandleProcess(mcmc1)
bdp <- bootstrapdiffLocprob(mcmc1)
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))
#> You haven't provided a seed, you may wish to provide a seed
#>
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mcmc1 <- bandleProcess(mcmc1)
dp <- binomialDiffLocProb(mcmc1)