R/bandle-function.R, R/bandle-sampler.R
bandle.RdThese function implement the bandle model for dynamic mass spectrometry based spatial proteomics datasets using MCMC for inference
These functions implement the bandle model for dynamic mass spectrometry based spatial proteomics datasets using MCMC for inference, this is an internal sampling function
bandle(
objectCond1,
objectCond2,
fcol = "markers",
hyperLearn = "LBFGS",
numIter = 1000,
burnin = 100L,
thin = 5L,
u = 2,
v = 10,
lambda = 1,
gpParams = NULL,
hyperIter = 20,
hyperMean = c(0, 0, 0),
hyperSd = c(1, 1, 1),
seed = NULL,
pg = FALSE,
pgPrior = NULL,
tau = 0.2,
dirPrior = NULL,
maternCov = TRUE,
PC = TRUE,
pcPrior = matrix(c(0.5, 3, 100), nrow = 1),
nu = 2,
propSd = c(0.3, 0.1, 0.05),
numChains = 4L,
BPPARAM = BiocParallel::bpparam()
)
diffLoc(
objectCond1,
objectCond2,
fcol = "markers",
hyperLearn = "MH",
numIter = 1000,
burnin = 100L,
thin = 5L,
u = 2,
v = 10,
lambda = 1,
gpParams = NULL,
hyperIter = 20,
hyperMean = c(0, 0, 0),
hyperSd = c(1, 1, 1),
seed = NULL,
pg = TRUE,
pgPrior = NULL,
tau = 0.2,
dirPrior = NULL,
maternCov = TRUE,
PC = TRUE,
nu = 2,
pcPrior = NULL,
propSd = c(0.3, 0.1, 0.05)
)A list of MSnbase::MSnSets where each is an experimental
replicate for the first condition, usually a control
A list of MSnbase::MSnSets where each is an experimental
replicate for the second condition, usually a treatment
The feature meta-data containing marker definitions. Default is
markers
Algorithm to learn posterior hyperparameters of the Gaussian
processes. Default is LBFGS and MH for metropolis-hastings is also implemented.
The number of iterations of the MCMC algorithm. Default is 1000. Though usually much larger numbers are used
The number of samples to be discarded from the begining of the chain. Default is 100.
The thinning frequency to be applied to the MCMC chain. Default is 5.
The prior shape parameter for Beta(u, v). Default is 2
The prior shape parameter for Beta(u, v). Default is 10.
Controls the variance of the outlier component. Default is 1.
Object of class gpParams. parameters from prior fitting of GPs
to each niche to accelerate inference. Default is NULL.
The frequency of MCMC interation to update the hyper-parameters default is 20
The prior mean of the log normal prior of the GP parameters. Default is 0 for each. Order is length-scale, amplitude and noise variance
The prior standard deviation of the log normal prior fo the GP parameters. Default is 1 for each. Order is length-scale, ampliture and noise variance.
The random number seed.
logical indicating whether to use polya-gamma prior. Default is
FALSE.
A matrix generated by pgPrior function. If param pg is TRUE but pgPrior is NULL then a pgPrior is generated on the fly.
The tau parameter for the polya-Gamma prior (is used). Defaults to 0.2
A matrix generated by dirPrior function. Default is NULL and dirPrior is generated on the fly.
logical indicated whether to use a matern or gaussian
covariance. Default is True and matern covariance is used
logical indicating whether to use a penalised complexity prior.
Default is TRUE.
matrix with 3 columns indicating the lambda paramters for the
penalised complexity prior. Default is null which internally sets
the penalised complexity prior to c(0.5, 3, 100) for each organelle and the order is
length-scale, amplitude and variance. See vignette for more details.
integer indicating the smoothness of the matern prior. Default
is 2.
If MH is used to learn posterior hyperparameters then the proposal standard deviations. A Gaussian random-walk proposal is used.
integer indicating the number of parallel chains to run.
Defaults to 4.
BiocParallel parameter. Defaults to machine default backend using bpparam()
bandle returns an instance of class bandleParams
bandle returns an instance of class bandleParams
The bandle function generate the sample from the posterior distributions
(object or class bandleParams) based on an annotated quantitative spatial
proteomics datasets (object of class MSnbase::MSnSet). Both are then
passed to the bandlePredict function to predict the sub-cellular localisation
and compute the differential localisation probability of proteins. See the
vignette for examples
The diffloc function generate the sample from the posterior distributions
(object or class bandleParam) based on an annotated quantitative spatial
proteomics datasets (object of class MSnbase::MSnSet). Both are then
passed to the bandlePredict function to predict the sub-cellular localisation
and compute the differential localisation probability of proteins. See the
vignette for 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 = 5L, 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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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 <- diffLoc(objectCond1 = control1, objectCond2 = treatment1, gpParams = gpParams,
fcol = "markers", numIter = 5L, burnin = 1L, thin = 2L)
#> You haven't provided a seed, you may wish to provide a seed
#>
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