bandle-differentiallocalisation: Compute differential localisation probabilities from ms-based...

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

Compute differential localisation probabilities from ms-based experiments using the bandle method

Description

These functions implement helper functions for the bandle method

Usage

diffLocalisationProb(params)

bootstrapdiffLocprob(params, top = 20, Bootsample = 5000, decreasing = TRUE)

binomialDiffLocProb(params, top = 20, nsample = 5000, decreasing = TRUE)

Arguments

params	An instance of bandleParams
top	The number of proteins for which to sample from the binomial distribution
Bootsample	Number of Bootstramp samples. Default is 5000
decreasing	Starting at protein most likely to be differentially localization
nsample	how many samples to return from the binomial distribution

Value

returns a named vector of differential localisation probabilities

returns a matrix of size Bootsample * top containing bootstrap

returns a list containing empirical binomial samples

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)

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)
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))
mcmc1 <- bandleProcess(mcmc1)
dp <- binomialDiffLocProb(mcmc1)

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