Package 'bandle' reference manual

Title:	An R package for the Bayesian analysis of differential subcellular localisation experiments
Description:	The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data.
Authors:	Oliver M. Crook [aut, cre] , Lisa Breckels [aut]
Maintainer:	Oliver M. Crook <[email protected]>
License:	Artistic-2.0
Version:	1.11.0
Built:	2024-11-02 03:30:38 UTC
Source:	https://github.com/bioc/bandle

An R package for the Bayesian analysis of differential subcellular localisation experiments

Description

The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data.

Details

The DESCRIPTION file:

Package:	bandle
Type:	Package
Title:	An R package for the Bayesian analysis of differential subcellular localisation experiments
Version:	1.11.0
Authors@R:	c(person(family = "Crook", given = "Oliver M.", email = "[email protected]", comment = c(ORCID = "0000-0001-5669-8506"), role = c("aut", "cre")), person(family = "Breckels", given = "Lisa", email = "[email protected]", comment = c(ORCID = "0000-0001-8918-7171"), role = c("aut")))
Description:	The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data.
License:	Artistic-2.0
Encoding:	UTF-8
Depends:	R (>= 4.1), S4Vectors, Biobase, MSnbase, pRoloc
Imports:	Rcpp (>= 1.0.4.6), pRolocdata, lbfgs, ggplot2, dplyr, plyr, knitr, methods, BiocParallel, robustbase, BiocStyle, ggalluvial, ggrepel, tidyr, circlize, graphics, stats, utils, grDevices, rlang, RColorBrewer, gtools, gridExtra, coda (>= 0.19-4)
Suggests:	testthat, interp, fields, pheatmap, viridis, rmarkdown, spelling
VignetteBuilder:	knitr
LinkingTo:	Rcpp, RcppArmadillo, BH
Roxygen:	list(markdown=TRUE)
RoxygenNote:	7.3.2
biocViews:	Bayesian, Classification, Clustering, ImmunoOncology, QualityControl,DataImport, Proteomics, MassSpectrometry
BugReports:	https://github.com/ococrook/bandle/issues
URL:	http://github.com/ococrook/bandle
Language:	en-US
Repository:	https://bioc.r-universe.dev
RemoteUrl:	https://github.com/bioc/bandle
RemoteRef:	HEAD
RemoteSha:	ca58277144c6c6cd9c47f405abc53aa0097bf17b
Author:	Oliver M. Crook [aut, cre] (<https://orcid.org/0000-0001-5669-8506>), Lisa Breckels [aut] (<https://orcid.org/0000-0001-8918-7171>)
Maintainer:	Oliver M. Crook <[email protected]>

Index of help topics:

EFDR                    Compute the expected False Discovery Rate
StatStratum             inherits StatSratum
bandle                  Differential localisation experiments using the
                        bandle method
bandle-package          An R package for the Bayesian analysis of
                        differential subcellular localisation
                        experiments
bandleChains-class      Infrastructure to to store and process MCMC
                        results
bandlePredict           Make predictions from a bandle analysis
bandleProcess           process bandle results
bandle_get_outliers     Number of outliers at each iteration of MCMC
besselK_boost           bessel function of the second kind from boost
                        library
calculateGelman         Calculate the Gelman and Rubin diagnostic for
                        bandle output
diffLocalisationProb    Compute differential localisation probabilities
                        from ms-based experiments using the bandle
                        method
fitGP                   Fit a Gaussian process to spatial proteomics
                        data
gpParams-class          Container for GP results
gradientGP              Compute GP gradient
kldirpg                 Computes the Kullback-Leibler divergence
                        between Polya-Gamma and Dirichlet priors
mcmc_plot_probs         Generate a violin plot showing the probabilitiy
                        of protein localisation to different organelles
meanOrganelle           Computes Organelle means and variances using
                        markers
plotConvergence         Generates a histogram of ranks (a rank plot)
                        for convergence
plotOutliers            Generate trace and density plots for all chains
plotTable               Generate a table of differential localisations
plotTranslocations      Plot changes in localisation between two
                        conditions/datasets
proteinAllocation       sample allocations, probabilities and compute
                        loglikilihoods
robustMahalanobis       robust Mahalanobis distance
sim_dynamic             Generate a dynamic spatial proteomics
                        experiment
spatial2D               Generate a PCA plot with smoothed probability
                        contours

~~ An overview of how to use the package, including the most important functions ~~

Author(s)

Oliver M. Crook [aut, cre] (<https://orcid.org/0000-0001-5669-8506>), Lisa Breckels [aut] (<https://orcid.org/0000-0001-8918-7171>)

Maintainer: Oliver M. Crook <[email protected]>

References

~~ Literature or other references for background information ~~

Differential localisation experiments using the bandle method

Description

These 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

Usage

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)
)
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)
)

Arguments

`objectCond1`	A list of `MSnbase::MSnSet`s where each is an experimental replicate for the first condition, usually a control
`objectCond2`	A list of `MSnbase::MSnSet`s where each is an experimental replicate for the second condition, usually a treatment
`fcol`	The feature meta-data containing marker definitions. Default is `markers`
`hyperLearn`	Algorithm to learn posterior hyperparameters of the Gaussian processes. Default is `LBFGS` and `MH` for metropolis-hastings is also implemented.
`numIter`	The number of iterations of the MCMC algorithm. Default is 1000. Though usually much larger numbers are used
`burnin`	The number of samples to be discarded from the begining of the chain. Default is 100.
`thin`	The thinning frequency to be applied to the MCMC chain. Default is 5.
`u`	The prior shape parameter for Beta(u, v). Default is 2
`v`	The prior shape parameter for Beta(u, v). Default is 10.
`lambda`	Controls the variance of the outlier component. Default is 1.
`gpParams`	Object of class `gpParams`. parameters from prior fitting of GPs to each niche to accelerate inference. Default is NULL.
`hyperIter`	The frequency of MCMC interation to update the hyper-parameters default is 20
`hyperMean`	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
`hyperSd`	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.
`seed`	The random number seed.
`pg`	`logical` indicating whether to use polya-gamma prior. Default is `FALSE`.
`pgPrior`	A matrix generated by pgPrior function. If param pg is TRUE but pgPrior is NULL then a pgPrior is generated on the fly.
`tau`	The tau parameter for the polya-Gamma prior (is used). Defaults to 0.2
`dirPrior`	A matrix generated by dirPrior function. Default is NULL and dirPrior is generated on the fly.
`maternCov`	`logical` indicated whether to use a matern or gaussian covariance. Default is True and matern covariance is used
`PC`	`logical` indicating whether to use a penalised complexity prior. Default is TRUE.
`pcPrior`	`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.
`nu`	`integer` indicating the smoothness of the matern prior. Default is 2.
`propSd`	If MH is used to learn posterior hyperparameters then the proposal standard deviations. A Gaussian random-walk proposal is used.
`numChains`	`integer` indicating the number of parallel chains to run. Defaults to 4.
`BPPARAM`	BiocParallel parameter. Defaults to machine default backend using bpparam()

Details

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

Value

bandle returns an instance of class bandleParams

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))

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)

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))

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)

Number of outliers at each iteration of MCMC

Description

Helper function to get the number of outliers at each MCMC iteration for each chain

Usage

bandle_get_outliers(params)
bandle_get_outliers(params)

Arguments

`x`	Object of class `bandleParams`

Value

A list of length length(x).

Infrastructure to to store and process MCMC results

Description

The bandleParams infrastructure is used to store and process MCMC results for bandle model from Crook et al 2021

Usage

chains(object)

## S4 method for signature 'bandleParams'
show(object)

## S4 method for signature 'nicheParam'
show(object)

## S4 method for signature 'bandleChain'
show(object)

## S4 method for signature 'bandleChains'
length(x)

## S4 method for signature 'bandleParams'
length(x)

## S4 method for signature 'bandleSummaries'
length(x)

## S4 method for signature 'nicheParams'
length(x)

## S4 method for signature 'nicheParams'
length(x)

posteriorEstimates(object)

## S4 method for signature 'bandleSummary'
posteriorEstimates(object)

summaries(object)

params(object)

bandleJoint(object)

## S4 method for signature 'bandleSummary'
bandleJoint(object)

## S4 method for signature 'bandleChains,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'bandleParams,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'bandleChains,ANY,ANY,ANY'
x[i, j = "missing", drop = "missing"]

## S4 method for signature 'bandleParams,ANY,ANY,ANY'
x[i, j = "missing", drop = "missing"]

## S4 method for signature 'bandleChains'
show(object)

## S4 method for signature 'bandleSummaries'
show(object)

## S4 method for signature 'bandleSummaries,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'bandleSummaries,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'bandleSummaries,ANY,ANY,ANY'
x[i, j = "missing", drop = "missing"]

## S4 method for signature 'nicheParams,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'nicheParams,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'nicheParams,ANY,ANY,ANY'
x[i, j = "missing", drop = "missing"]

## S4 method for signature 'nicheParams'
show(object)
chains(object)

## S4 method for signature 'bandleParams'
show(object)

## S4 method for signature 'nicheParam'
show(object)

## S4 method for signature 'bandleChain'
show(object)

## S4 method for signature 'bandleChains'
length(x)

## S4 method for signature 'bandleParams'
length(x)

## S4 method for signature 'bandleSummaries'
length(x)

## S4 method for signature 'nicheParams'
length(x)

## S4 method for signature 'nicheParams'
length(x)

posteriorEstimates(object)

## S4 method for signature 'bandleSummary'
posteriorEstimates(object)

summaries(object)

params(object)

bandleJoint(object)

## S4 method for signature 'bandleSummary'
bandleJoint(object)

## S4 method for signature 'bandleChains,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'bandleParams,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'bandleChains,ANY,ANY,ANY'
x[i, j = "missing", drop = "missing"]

## S4 method for signature 'bandleParams,ANY,ANY,ANY'
x[i, j = "missing", drop = "missing"]

## S4 method for signature 'bandleChains'
show(object)

## S4 method for signature 'bandleSummaries'
show(object)

## S4 method for signature 'bandleSummaries,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'bandleSummaries,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'bandleSummaries,ANY,ANY,ANY'
x[i, j = "missing", drop = "missing"]

## S4 method for signature 'nicheParams,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'nicheParams,ANY,ANY'
x[[i, j = "missing", drop = "missing"]]

## S4 method for signature 'nicheParams,ANY,ANY,ANY'
x[i, j = "missing", drop = "missing"]

## S4 method for signature 'nicheParams'
show(object)

Arguments

`object`	object of class nicheParams.
`x`	Object to be subset.
`i`	An `integer()`. Should be of length 1 for `[[`.
`j`	Missing.
`drop`	Missing.

Details

Objects of the bandleParams class are created with the bandle() function These objects store the priors for the model and the results of the MCMC chains, which themselves are stored as an instance of class bandleChains and can be accessed with the chains() function. A summary of the bandleChains (or class bandleSummary) can be further computed with the bandleProcess function.

see the bandle vignette for examples

Value

An object of class bandleParams which stores the main results for the analysis when using bandle

Slots

chains: list() containing the individual full MCMC chain results in an bandleChains instance. Each element must be a valid bandleChain instance.
posteriorEstimates: A DataFrame documenting the posteriors in an bandleSummary instance
diagnostics: A matrix of dimensions 1 by 2 containing the bandleSummary diagnostics.
bandle.joint: A matrix of dimensions N by K storing the joint probability in an bandleSummary instance for each of the first condition
chains: list() containing the individual bandle Summaries for different conditions results in an bandleSummaries instance. Each element must be a valid bandleSummary instance.
method: A character() storing the bandle method name
priors: A list() with the priors for the parameters
seed: An integer() with the random number generation seed.
summary: Object of class bandleSummary the summarised MCMC results available in the bandleParams instance.
chains: Object of class bandleChains containing the full MCMC results in the bandleParams instance
datset: character indicating which dataset i.e control or treatment
replicate: integer an integer indicating which replicate
K: integer(1) indicating the number of components.
D: integer(1) indicating the number of samples.
method: character(1) defining the method used. Currently bandle
mk: matrix(K, D)
lambdak: numeric(K)
nuk: numeric(K)
sk: array(K, D, D)
params: list() containing the individual nicheParam objects results in an bandleParams instance. Each element must be a valid bandleParam instance.
dataset: character indicating the dataset usaully control or treatment
replicate: integer indicating the number of dataset replicate
n: integer(1) indicating the number of MCMC interactions. Stored in an bandleChain instance.
K: integer(1) indicating the number of components. Stored in an bandleChain instance.
N: integer(1) indicating the number of proteins. Stored in an bandleChain instance.
niche: matrix(N, n) component allocation results of an bandleChain instance.
nicheProb: matrix(N, n, K) component allocation probabilities of an bandleChain instance.
outlier: matrix(N, n) outlier allocation results.
outlierProb: matrix(N, n, 2) outlier allocation probabilities of an bandleChain instance.

Make predictions from a bandle analysis

Description

Make predictions from a bandle analysis

Usage

bandlePredict(objectCond1, objectCond2, params, fcol = "markers")
bandlePredict(objectCond1, objectCond2, params, fcol = "markers")

Arguments

`objectCond1`	A list of instances of class `MSnbase::MSnSet`s where each is an experimental replicate for the first condition, usually a control
`objectCond2`	A list of instance of class `MSnbase::MSnSet`s where each is an experimental replicate for the second condition, usually a treatment
`params`	An instance of class `bandleParams`, as generated by `bandle()`.
`fcol`	A feature column indicating the markers. Defaults to "markers"

Value

bandlePredict returns an instance of class MSnbase::MSnSet containing the localisation predictions as a new bandle.allocation feature variable. The allocation probability is encoded as bandle.probability (corresponding to the mean of the distribution probability). In addition the upper and lower quantiles of the allocation probability distribution are available as bandle.probability.lowerquantile and bandle.probability.upperquantile feature variables. The Shannon entropy is available in the bandle.mean.shannon feature variable, measuring the uncertainty in the allocations (a high value representing high uncertainty; the highest value is the natural logarithm of the number of classes). An additional variable indicating the differential localization probability is also added as bandle.differential.localisation

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))
mcmc1 <- bandleProcess(mcmc1)
out <- bandlePredict(objectCond1 = control1, objectCond2 = treatment1, params = 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 = 5L, burnin = 1L, thin = 2L,
                                     numChains = 1, BPPARAM = SerialParam(RNGseed = 1))
mcmc1 <- bandleProcess(mcmc1)
out <- bandlePredict(objectCond1 = control1, objectCond2 = treatment1, params = mcmc1)

process bandle results

Description

process bandle results

Usage

bandleProcess(params)
bandleProcess(params)

Arguments

params

An object of class bandleParams

Value

bandleProcess returns an instance of class bandleParams with its summary slot populated.

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))
mcmc1 <- bandleProcess(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 = 5L, burnin = 1L, thin = 2L,
                                     numChains = 1, BPPARAM = SerialParam(RNGseed = 1))
mcmc1 <- bandleProcess(mcmc1)

bessel function of the second kind from boost library

Description

Leapfrog routine

Usage

besselK_boost(x, v)

besselK(x, v)

matern(nu, a, rho, tau, D)

trenchDetcpp(c)

trenchInvcpp(v)

loglikeGPcpp(Y, Z, A, logcovDet, sigmak, nk, D, Y2)

likelihoodGPcpp(Xk, tau, h, nk, D, materncov = 0L, nu = 2)

gradientrhomatern(Y, drvrhomatern, nk, D, Z, A, sigmak)

gradientamatern(Y, amatern, nk, D, Z, A, sigmak)

gradientGPcppmatern(Xk, tau, h, nk, D, nu)

LeapfrogGPcppPC(Xk, lambda, tau, p, x, m, nk, D, L, delta, nu)

sampleGPmeanmaterncpp(Xk, tau, h, nk, D, nu)

makeComponent(X, BX, Y, BY, j)

sampleGPmeancpp(Xk, tau, h, nk, D)

normalisedData(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, j)

normalisedDatamatern(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, j, nu)

centeredDatamatern(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, K, nu)

componentloglike(centereddata, sigmak)

comploglike(centereddata, sigmak)

comploglikelist(centereddata, sigmak)

sampleDirichlet(numSamples, alpha)

sampleOutliercpp(allocoutlierprob)

sampleAlloccpp(allocprob)

centeredData(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, K)

mahaInt(X, mu, sigma, isChol = FALSE)

dmvtInt(X, mu, cholDec, log, df)

dmvtCpp(X_, mu_, sigma_, df_, log_, isChol_)

gradientGPcpp(Xk, tau, h, nk, D)

LeapfrogGPcpp(Xk, tau, p, x, m, nk, D, L, delta)

rcpp_pgdraw(b, c)
besselK_boost(x, v)

besselK(x, v)

matern(nu, a, rho, tau, D)

trenchDetcpp(c)

trenchInvcpp(v)

loglikeGPcpp(Y, Z, A, logcovDet, sigmak, nk, D, Y2)

likelihoodGPcpp(Xk, tau, h, nk, D, materncov = 0L, nu = 2)

gradientrhomatern(Y, drvrhomatern, nk, D, Z, A, sigmak)

gradientamatern(Y, amatern, nk, D, Z, A, sigmak)

gradientGPcppmatern(Xk, tau, h, nk, D, nu)

LeapfrogGPcppPC(Xk, lambda, tau, p, x, m, nk, D, L, delta, nu)

sampleGPmeanmaterncpp(Xk, tau, h, nk, D, nu)

makeComponent(X, BX, Y, BY, j)

sampleGPmeancpp(Xk, tau, h, nk, D)

normalisedData(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, j)

normalisedDatamatern(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, j, nu)

centeredDatamatern(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, K, nu)

componentloglike(centereddata, sigmak)

comploglike(centereddata, sigmak)

comploglikelist(centereddata, sigmak)

sampleDirichlet(numSamples, alpha)

sampleOutliercpp(allocoutlierprob)

sampleAlloccpp(allocprob)

centeredData(Xknown, BX, Xunknown, BXun, hypers, nk, tau, D, K)

mahaInt(X, mu, sigma, isChol = FALSE)

dmvtInt(X, mu, cholDec, log, df)

dmvtCpp(X_, mu_, sigma_, df_, log_, isChol_)

gradientGPcpp(Xk, tau, h, nk, D)

LeapfrogGPcpp(Xk, tau, p, x, m, nk, D, L, delta)

rcpp_pgdraw(b, c)

Arguments

`x`	position
`v`	argument of trench algorithm
`nu`	smoothness parameter of matern covariance
`a`	amplitude
`rho`	length-scale
`tau`	indexing term
`D`	number of samples
`c`	parameter of PG distribution
`Y`	pointer to data to be subset. X and Y will be joined
`Z`	special matrix from trench algorithm (see Crook et al arxiv 2019)
`A`	special matrix from trench algorithm (see Crook et al arxiv 2019)
`logcovDet`	log determine of the covariancematrix
`sigmak`	variance term
`nk`	number of observations
`Y2`	vectorised data (see Crook et al arxiv 2019)
`Xk`	The data
`h`	vector of hyperparamters
`materncov`	logical indicating whether to use matern or gaussian covariance. Defaults to Guassian covariance
`drvrhomatern`	deterivate of matern covariance wrt to rho
`amatern`	deterivate of matern covariance wrt to amplitude
`lambda`	parameters of penalised complexity prior
`p`	momentum
`m`	mass
`L`	iterations
`delta`	stepsize
`X`	data
`BX`	indexing set to make component
`BY`	pointer to subsetting matrix
`j`	indicator of localisations i.e. niche j
`Xknown`	data with known localisations
`Xunknown`	data with unknown localisations
`BXun`	indexing set for unknown localisations
`hypers`	vector of hyperparameters
`K`	number of components
`centereddata`	pointer to centered data
`numSamples`	The number of samples desired
`alpha`	The concentration parameter
`allocoutlierprob`	The probabilities of being allocated to the outlier component
`allocprob`	probability of being allocated to particular component
`mu`	mean
`sigma`	variance matrix
`isChol`	boolen indicated whether sigma is cholesky decomposition
`cholDec`	Cholesky decomposition of variance matrix
`log`	boolen of log density
`df`	degrees of freedom for t distribution
`X_`	the data
`mu_`	the mean
`sigma_`	the variance matrix
`df_`	the degrees of freedom
`log_`	return log density (boolean).
`isChol_`	is variance matrix in cholesky decomposition
`b`	parameter of PG distribution

Value

A numeric indicating the density of the t-distribution

Examples

dmvtCpp(diag(1,1,1), 1, diag(1,1,1), 1, TRUE, TRUE)

dmvtCpp(diag(1,1,1), 1, diag(1,1,1), 1, TRUE, TRUE)

Calculate the Gelman and Rubin diagnostic for bandle output

Description

This function is a wrapper function for the gelman.diag function from the coda package. It takes a bandleParams object and calculates the Gelman and Rubin's convergence diagnostic (otherwise known as the potential scale reduction factor) for all pairwise MCMC chain combinations, together with upper and lower confidence limits.

Usage

calculateGelman(params)
calculateGelman(params)

Arguments

params

An instance of class bandleParams

Value

A list of 2 matrix array's, one for each condition containing the point estimates of the potential scale reduction factor (labelled Point est.) and their upper confidence limits (labelled Upper C.I.).

Examples

## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1,
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x)
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment,
              gpParams = gpParams,
              fcol = "markers",
              numIter = 20L,
              burnin = 1L,
              thin = 2L,
              numChains = 2,
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)

## Process the results
calculateGelman(res)
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1,
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x)
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment,
              gpParams = gpParams,
              fcol = "markers",
              numIter = 20L,
              burnin = 1L,
              thin = 2L,
              numChains = 2,
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)

## Process the results
calculateGelman(res)

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)
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)
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)

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)
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)

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)

Fit a Gaussian process to spatial proteomics data

Description

The fitGP function is a helper function to fit GPs with squared exponential co-variances, maximum marginal likelihood

The fitGPmaternPC function is a helper function to fit matern GPs to data with penalised complexity priors on the hyperparameters.

The fitGPmatern function fits matern GPs to data.

The plotGPmatern function plots matern GPs

Usage

fitGP(object = object, fcol = "markers")

fitGPmaternPC(
  object = object,
  fcol = "markers",
  materncov = TRUE,
  nu = 2,
  hyppar = matrix(c(10, 60, 250), nrow = 1)
)

fitGPmatern(object = object, fcol = "markers", materncov = TRUE, nu = 2)

plotGPmatern(object = object, params = params, fcol = "markers")
fitGP(object = object, fcol = "markers")

fitGPmaternPC(
  object = object,
  fcol = "markers",
  materncov = TRUE,
  nu = 2,
  hyppar = matrix(c(10, 60, 250), nrow = 1)
)

fitGPmatern(object = object, fcol = "markers", materncov = TRUE, nu = 2)

plotGPmatern(object = object, params = params, fcol = "markers")

Arguments

`object`	A instance of class `MSnSet`
`fcol`	feature column to indicate markers. Default is `"markers"`.
`materncov`	`logical` indicating whether matern covariance is used.
`nu`	matern smoothness parameter. Default is 2.
`hyppar`	The vector of penalised complexity hyperparameters, you must provide a matrix with 3 columns and 1 row. The order is hyperparameters on length-scale, amplitude, variance.
`params`	The output of running `fitGPmatern`, `fitGPmaternPC` or `fitGP` which is of class `gpParams`

Details

This set of functions allow users to fit GPs to their data. The fitGPmaternPC function allows users to pass a vector of penalised complexity hyperparameters using the hyppar argument. You must provide a matrix with 3 columns and 1 row. The order of these 3 columns represent the hyperparameters length-scale, amplitude, variance. We have found that the matrix(c(10, 60, 250), nrow = 1) worked well for the spatial proteomics datasets tested in Crook et al (2021). This was visually assessed by passing these values and visualising the GP fit using the plotGPmatern function (please see vignette for an example of the output). Generally, (1) increasing the lengthscale parameter (the first column of the hyppar matrix) increases the spread of the covariance i.e. the similarity between points, (2) increasing the amplitude parameter (the second column of the hyppar matrix) increases the maximum value of the covariance and lastly (3) decreasing the variance (third column of the hyppar matrix) reduces the smoothness of the function to allow for local variations. We strongly recommend users start with the recommended parameters and change and assess them as necessary for their dataset by visually evaluating the fit of the GPs using the plotGPmatern function. Please see the vignettes for more details and examples.

Value

Returns an object of class gpParams which stores the posterior predictive means, standard deviations, variances and also the MAP hyperparamters for the GP.

The functions plotGPmatern plot the posterior predictives overlayed with the markers for each subcellular class.

Examples

library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
gpParams <- lapply(tansim$lopitrep, function(x) fitGP(x))

## ====== fitGPmaternPC =====
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
## Please note that hyppar should be chosen carefully and tested
## by checking the GP fit with the plotGPmatern function
## (please see details above)
gpParams <- lapply(tansim$lopitrep, 
function(x) fitGPmaternPC(x, hyppar = matrix(c(10, 60, 100), nrow = 1)))

## ====== fitGPmatern =====
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
gpParams <- lapply(tansim$lopitrep, function(x) fitGPmaternPC(x))

## ====== plotGPmatern =====
## generate example data
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
## fit a GP
gpParams <- lapply(tansim$lopitrep, function(x) fitGP(x))

## Overlay posterior predictives onto profiles
## Dataset1 1
par(mfrow = c(2, 3))
plotGPmatern(tansim$lopitrep[[1]], gpParams[[1]])

## Dataset 2, etc.
par(mfrow = c(2, 3))
plotGPmatern(tansim$lopitrep[[2]], gpParams[[2]])
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
gpParams <- lapply(tansim$lopitrep, function(x) fitGP(x))

## ====== fitGPmaternPC =====
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
## Please note that hyppar should be chosen carefully and tested
## by checking the GP fit with the plotGPmatern function
## (please see details above)
gpParams <- lapply(tansim$lopitrep, 
function(x) fitGPmaternPC(x, hyppar = matrix(c(10, 60, 100), nrow = 1)))

## ====== fitGPmatern =====
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
gpParams <- lapply(tansim$lopitrep, function(x) fitGPmaternPC(x))

## ====== plotGPmatern =====
## generate example data
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
## fit a GP
gpParams <- lapply(tansim$lopitrep, function(x) fitGP(x))

## Overlay posterior predictives onto profiles
## Dataset1 1
par(mfrow = c(2, 3))
plotGPmatern(tansim$lopitrep[[1]], gpParams[[1]])

## Dataset 2, etc.
par(mfrow = c(2, 3))
plotGPmatern(tansim$lopitrep[[2]], gpParams[[2]])

Container for GP results

Description

The gpParams infrastructure is used to store and process the GP results for output from using the fitGP functions in bandle

Details

Objects of the gpParams class are created with the fitGP, fitGPmaternPC or fitGPmatern functions

These objects a list of posterior predictive means and standard deviations. As well as maximum marginal likelihood for the GP

Slots

method: character indicating the GP method used
M: A list of the posterior predictive means for each K components of GPs fitted to the data
sigma: A numeric of length K standard deviations fitted to the data
V: A list of the variance fitted to the data
params: A matrix array of the MAP hyperparameters for the GP

Compute GP gradient

Description

Internal R function to pass R to C++, not for external use.

Function to perform Metropolis-Hastings for GP hyperparameters with different priors

Usage

gradientGP(Xk, tau, h, nk, D)

gradientGPmatern(Xk, tau, h, nk, D, materncov, nu)

posteriorgradientGPmatern(Xk, tau, h, nk, D, materncov, nu, hyppar)

gradientlogprior(h, hyppar)

likelihoodGP(Xk, tau, h, nk, D)

likelihoodGPmatern(Xk, tau, h, nk, D, materncov, nu)

posteriorGPmatern(Xk, tau, h, nk, D, materncov, nu, hyppar)

Gumbel(x, lambda, log = TRUE)

PCrhomvar(rho, a, lambda1, lambda2, log = TRUE)

metropolisGP(
  inith,
  X,
  tau,
  nk,
  D,
  niter,
  hyperMean = c(0, 0, 0),
  hyperSd = c(1, 1, 1)
)

metropolisGPmatern(
  inith,
  X,
  tau,
  nk,
  D,
  niter,
  nu = 2,
  hyppar = c(1, 1, 1),
  propSd = c(0.3, 0.1, 0.1)
)

Gumbel(x, lambda, log = TRUE)

PCrhomvar(rho, a, lambda1, lambda2, log = TRUE)
gradientGP(Xk, tau, h, nk, D)

gradientGPmatern(Xk, tau, h, nk, D, materncov, nu)

posteriorgradientGPmatern(Xk, tau, h, nk, D, materncov, nu, hyppar)

gradientlogprior(h, hyppar)

likelihoodGP(Xk, tau, h, nk, D)

likelihoodGPmatern(Xk, tau, h, nk, D, materncov, nu)

posteriorGPmatern(Xk, tau, h, nk, D, materncov, nu, hyppar)

Gumbel(x, lambda, log = TRUE)

PCrhomvar(rho, a, lambda1, lambda2, log = TRUE)

metropolisGP(
  inith,
  X,
  tau,
  nk,
  D,
  niter,
  hyperMean = c(0, 0, 0),
  hyperSd = c(1, 1, 1)
)

metropolisGPmatern(
  inith,
  X,
  tau,
  nk,
  D,
  niter,
  nu = 2,
  hyppar = c(1, 1, 1),
  propSd = c(0.3, 0.1, 0.1)
)

Gumbel(x, lambda, log = TRUE)

PCrhomvar(rho, a, lambda1, lambda2, log = TRUE)

Arguments

`Xk`	The data
`tau`	The indexing parameters
`h`	GP hyperparameters
`nk`	Number of observations
`D`	number of samples
`materncov`	`logical` indicating whether matern covariance is used
`nu`	Smoothness of the matern covariance
`hyppar`	A vector indicating the penalised complexity prior hyperparameters. Default is `c(1,1,1)`
`x`	observation
`lambda`	scale parameter of the type-2 Gumbel distribution
`log`	`logical` indicating whether to return `log`. Default is `TRUE`
`rho`	length-scale parameter
`a`	amplitude
`lambda1`	first parameter of distribution
`lambda2`	second parameter of distribution
`inith`	initial hyperparamters
`X`	The data
`niter`	Number of MH iteractions
`hyperMean`	A vector indicating the log-normal means. Default is `c(0,0,0)`.
`hyperSd`	A vector indicating the log-normal standard deviations. Default is `c(1,1,1)`
`propSd`	The proposal standard deviation. Default is `c(0.3,0.1,0.1)`. Do not change unless you know what you are doing.

Value

Returns gp gradient

Returns the gradient of the posterior

return the gradient of the log prior, length-scale, aamplitude and noise

Returns gp negative log likelihood

Returns the negative log posterior of the GP

Returns the likelihood of the type-2 GUmbel distribution

Returns the likelihood of the bivariate penalised complexity prior

Returns new hyperparamters and the acceptance rate

Returns the likelihood of the type-2 GUmbel distribution

Returns the likelihood of the bivariate penalised complexity prior

Examples

Gumbel(3, lambda = 1)

Gumbel(3, lambda = 1)

Computes the Kullback-Leibler divergence between Polya-Gamma and Dirichlet priors

Description

Computes the Kullback-Leibler divergence between Polya-Gamma and Dirichlet priors

Compute the KL divergence between two Dirichlet distributions

A function to compute the prior predictive distribution of the Dirichlet prior.

A function to compute the prior predictive distribution of the Polya-Gamma prior.

Usage

kldirpg(sigma = diag(1, 1, 1), mu = c(0, 0, 0), alpha = c(1))

kldir(alpha, beta)

prior_pred_dir(object, fcol = "markers", iter = 5000, dirPrior = NULL, q = 15)

prior_pred_pg(
  objectCond1,
  objectCond2,
  fcol = "markers",
  tau = 0.2,
  lambda = 0.01,
  mu_prior = NULL,
  iter = 10000,
  q = 15
)
kldirpg(sigma = diag(1, 1, 1), mu = c(0, 0, 0), alpha = c(1))

kldir(alpha, beta)

prior_pred_dir(object, fcol = "markers", iter = 5000, dirPrior = NULL, q = 15)

prior_pred_pg(
  objectCond1,
  objectCond2,
  fcol = "markers",
  tau = 0.2,
  lambda = 0.01,
  mu_prior = NULL,
  iter = 10000,
  q = 15
)

Arguments

`sigma`	the sigma parameter of the Polya-Gamma prior. A positive-definite symmetric matrix.
`mu`	the mu parameter of the Polya-Gamma prior. A vector of means
`alpha`	The concentration parameter of the first Dirichlet distribution
`beta`	The concentration parameter of the second Dirichlet distribution
`object`	An instance of class `MSnSet`
`fcol`	The feature column indiating the markers. Default is "markers"
`iter`	Number of sample to use from prior predictive distribution. Default is 10000
`dirPrior`	The Dirichlet prior used. If NULL (default) will generate a a default Dirichlet prior. This should be a matrix with the same dimensions as the number of subcellular niches. The diagonal terms correspond to the prior probability of not differentially localising. The (i,j) term corresponds to prior probability of differntially localising between niche i and j.
`q`	The upper tail value. That is the prior probability of having more than q differential localisations. Default is 15.
`objectCond1`	An instance of class `MSnSet`, usually the control dataset
`objectCond2`	An instance of class `MSnSet`, usually the treatment dataset
`tau`	The `tau` parameter of the Polya-Gamma prior. Default is 0.2.
`lambda`	The `lambda` ridge parameter used for numerical stability. Default is 0.01
`mu_prior`	The mean of the Polya-Gamma prior. Default is NULL which generates a default Polya-Gamma prior.

Value

returns a numeric indicating the KL divergence

a numeric indicating the KL divergence

A list contain the prior predictive distribution of differential localisations, the mean number of differential localised proteins and the probability than more than q are differentially localised

Examples

kldirpg(sigma = diag(c(1,1,1)), mu = c(0,0,0), alpha = 1)

kldir(c(1,1), c(3,1))

library(pRolocdata)
data("tan2009r1")

out <- prior_pred_dir(object = tan2009r1)

library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
d1 <- tansim$lopitrep
control1 <- d1[1:3]
treatment1 <- d1[4:6]
out <- prior_pred_pg(objectCond1 = control1[[1]],
objectCond2 = treatment1[[1]])


kldirpg(sigma = diag(c(1,1,1)), mu = c(0,0,0), alpha = 1)

kldir(c(1,1), c(3,1))

library(pRolocdata)
data("tan2009r1")

out <- prior_pred_dir(object = tan2009r1)

library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
d1 <- tansim$lopitrep
control1 <- d1[1:3]
treatment1 <- d1[4:6]
out <- prior_pred_pg(objectCond1 = control1[[1]],
objectCond2 = treatment1[[1]])

Generate a violin plot showing the probabilitiy of protein localisation to different organelles

Description

These functions implement plotting functions for bandle objects

Usage

mcmc_plot_probs(
  params,
  fname,
  cond = 1,
  n = 1,
  bw = 0.05,
  scale = "width",
  trim = TRUE
)
mcmc_plot_probs(
  params,
  fname,
  cond = 1,
  n = 1,
  bw = 0.05,
  scale = "width",
  trim = TRUE
)

Arguments

`params`	An instance of class `bandleParams`
`fname`	The name of the protein to plot
`cond`	Which conditions do we want to plot. Must be `1` or `2`. Default is `1`
`n`	The chain from which we plot the probability distribution. Default is 1.
`bw`	The bandwidth use in probability distribution smoothing of geom_violin Default is 0.05.
`scale`	Scaling of geom_violin. Defaults to width.
`trim`	trim parameter of geom_violin. Defaults to true.

Value

returns a named vector of differential localisation probabilities

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))
mcmc_plot_probs(params = mcmc1, fname = rownames(tan2009r1)[1])

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))
mcmc_plot_probs(params = mcmc1, fname = rownames(tan2009r1)[1])

Computes Organelle means and variances using markers

Description

Computes Organelle means and variances using markers

Usage

meanOrganelle(object, fcol = "markers")
meanOrganelle(object, fcol = "markers")

Arguments

`object`	a instance of class `MSnset`
`fcol`	a feature column indicating which feature define the markers

Value

returns a list of means and variances for each

Examples

library(pRolocdata)
data("tan2009r1")
meanOrganelle(object = tan2009r1)

library(pRolocdata)
data("tan2009r1")
meanOrganelle(object = tan2009r1)

Generates a histogram of ranks (a rank plot) for convergence

Description

Produces a rank plot to analyse convergence of MCMC algorithm

Usage

plotConvergence(params)
plotConvergence(params)

Arguments

params

An instance of class bandleParams

Value

Returns the ranks of the number of outliers in each chain. The side effect returns rank plots. Number of rank plots is equal to the number of chains

Examples

## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 2, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process bandle results
bandleres <- bandleProcess(res)

## Convergence plots
par(mfrow = c(1, 2))
plotConvergence(bandleres)  
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 2, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process bandle results
bandleres <- bandleProcess(res)

## Convergence plots
par(mfrow = c(1, 2))
plotConvergence(bandleres)

Generate trace and density plots for all chains

Description

This function takes the output from running bandle i.e. a bandleParams object and generates trace and density plots for each MCMC chain in each condition. The output plots can be used to help assess convergence of MCMC chains.

Usage

plotOutliers(params, auto.layout = TRUE)
plotOutliers(params, auto.layout = TRUE)

Arguments

`params`	An instance of class `bandleParams`
`auto.layout`	A `logical` specifying whether to automatically determine the arrangement of each plot. Default is `TRUE`.

Value

Generates trace and density plots for each chain for each condition/experiment.

Examples

## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1,
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x)
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment,
              gpParams = gpParams,
              fcol = "markers",
              numIter = 20L,
              burnin = 1L,
              thin = 2L,
              numChains = 2,
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)

## Process the results
plotOutliers(res)
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1,
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x)
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment,
              gpParams = gpParams,
              fcol = "markers",
              numIter = 20L,
              burnin = 1L,
              thin = 2L,
              numChains = 2,
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)

## Process the results
plotOutliers(res)

Generate a table of differential localisations

Description

This function produces a table summarising differential localisation results between two experiments

Usage

plotTable(params, all = FALSE, fcol)
plotTable(params, all = FALSE, fcol)

Arguments

`params`	An instance of class `bandleParams` or an instance of class `MSnSetList` of length 2.
`all`	A `logical` specifying whether to count all proteins or only show those that have changed in location between conditions. Default is `FALSE`.
`fcol`	If `params` is a `list` of `MSnSets`. Then `fcol` must be defined. This is a `character` vector of length 2 to set different labels for each dataset. If only one label is specified, and the `character` is of length 1 then this single label will be used to identify the annotation column in both datasets.

Value

Returns a summary table of translocations of proteins between conditions.

Examples

## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 2, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process bandle results
bandleres <- bandleProcess(res)

## Tabulate results
plotTable(bandleres)  
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 2, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process bandle results
bandleres <- bandleProcess(res)

## Tabulate results
plotTable(bandleres)

Plot changes in localisation between two conditions/datasets

Description

This function produces a chord diagram (also known as a circos plot) or an alluvial plot (also known as a Sankey diagram) to show changes in location between two conditions or datasets.

Usage

plotTranslocations(
  params,
  type = "alluvial",
  all = FALSE,
  fcol,
  col,
  labels = TRUE,
  labels.par = "adj",
  cex = 1,
  spacer = 4,
  ...
)
plotTranslocations(
  params,
  type = "alluvial",
  all = FALSE,
  fcol,
  col,
  labels = TRUE,
  labels.par = "adj",
  cex = 1,
  spacer = 4,
  ...
)

Arguments

`params`	An instance of class `bandleParams` or an instance of class `MSnSetList` of length 2.
`type`	A `character` specifying the type of visualisation to plot. One of `"alluvial"` (default) or `"chord"`.
`all`	A `logical` specifying whether to count all proteins or only show those that have changed in location between conditions. Default is `FALSE`.
`fcol`	If `params` is a `list` of `MSnSets`. Then `fcol` must be defined. This is a `character` vector of length 2 to set different labels for each dataset. If only one label is specified, and the `character` is of length 1 then this single label will be used to identify the annotation column in both datasets.
`col`	A list of colours to define the classes in the data. If not defined then the default `pRoloc` colours in `getStockCol()` are used.
`labels`	A `logical` indicating whether to display class/organelle labels for the chord segments or alluvial stratum. Default is `TRUE`.
`labels.par`	If `type` is `"alluvial"`. Label style can be specified as one of `"adj"`, `"repel"`. Default is `"adj"`.
`cex`	Text size. Default is 1.
`spacer`	A `numeric`. Default is 4. Controls the white space around the circos plotting region.
`...`	Additional arguments passed to the `chordDiagram` function.

Value

Returns a directional circos/chord diagram showing the translocation of proteins between conditions. If type = "alluvial" ouput is a ggplot object.

Examples

## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 1, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process the results
bandleres <- bandleProcess(res)

## plot the results
plotTranslocations(bandleres)
plotTranslocations(bandleres, type = "chord")
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 1, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process the results
bandleres <- bandleProcess(res)

## plot the results
plotTranslocations(bandleres)
plotTranslocations(bandleres, type = "chord")

sample allocations, probabilities and compute loglikilihoods

Description

Internal sampling function, not for outside use documented for completness

Usage

proteinAllocation(loglikelihoods, currentweights, alloctemp, cond)

outlierAllocationProbs(
  outlierlikelihood,
  loglikelihoods,
  epsilon,
  alloctemp,
  cond
)

sampleOutlier(allocoutlierprob)

covOrganelle(object, fcol = "markers")

pg_prior(object_cond1, object_cond2, K, pgPrior = NULL, fcol = "markers")

sample_weights_pg(nk_mat, pgPrior, w, K, tau = 0.2)

sample_weights_dir(nk_mat, dirPrior)
proteinAllocation(loglikelihoods, currentweights, alloctemp, cond)

outlierAllocationProbs(
  outlierlikelihood,
  loglikelihoods,
  epsilon,
  alloctemp,
  cond
)

sampleOutlier(allocoutlierprob)

covOrganelle(object, fcol = "markers")

pg_prior(object_cond1, object_cond2, K, pgPrior = NULL, fcol = "markers")

sample_weights_pg(nk_mat, pgPrior, w, K, tau = 0.2)

sample_weights_dir(nk_mat, dirPrior)

Arguments

`loglikelihoods`	the log likelihoods
`currentweights`	the current allocations weights
`alloctemp`	the current protein allocations
`cond`	the control = 1, treatment = 2
`outlierlikelihood`	the outlier log likelihoods
`epsilon`	the outlier component weight
`allocoutlierprob`	the outlier probabilities
`object`	An instance of class `MSnSet`
`fcol`	The feature column containing the markers.
`object_cond1`	A list of instance of class `MSnSets` usually control
`object_cond2`	A list of instance of class `MSnSets` usually treatment
`K`	The number of organelle classes
`pgPrior`	The Polya-Gamma prior
`nk_mat`	The summary matrix of allocations
`w`	The Polya-Gamma auxiliary variable
`tau`	The empirical bayes parameter for the Polya-Gamma variable. Defaults to 0.2.
`dirPrior`	The Dirichlet prior

Value

returns samples for protein allocations, log likelihoods and probabilities

returns outlier probabilities

returns outlier allocations

returns covariance of organelles using marker proteins

returns the Polya-Gamma prior

returns A sample of the weights using Polya-Gamma priors.

returns A sample of the weights using Dirichlet prior.

Examples

library(pRolocdata)
data("tan2009r1")
covOrganelle(object = tan2009r1)


library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
d1 <- tansim$lopitrep
control1 <- d1[1:3]
treatment1 <- d1[4:6]
out <- pg_prior(object_cond1 = control1,
 object_cond2 = treatment1, K = 11) 


library(pRolocdata)
data("tan2009r1")
covOrganelle(object = tan2009r1)


library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
d1 <- tansim$lopitrep
control1 <- d1[1:3]
treatment1 <- d1[4:6]
out <- pg_prior(object_cond1 = control1,
 object_cond2 = treatment1, K = 11)

robust Mahalanobis distance

Description

These function implement the MR method of Itzhak et al

Usage

robustMahalanobis(delta)

reprodScore(x, y, method = c("pearson"))

mrMethod(objectCond1, objectCond2, method = "2017")
robustMahalanobis(delta)

reprodScore(x, y, method = c("pearson"))

mrMethod(objectCond1, objectCond2, method = "2017")

Arguments

`delta`	The difference profile to compute the squared mahalanobis distance
`x`	Numeric vector to compute reproducibility score
`y`	Numeric vector to compute reproducibility score
`method`	Correlation method. Default is Pearson
`objectCond1`	A list of `MSnbase::MSnSet`s where each is an experimental replicate for the first condition, usually a control
`objectCond2`	A list of `MSnbase::MSnSet`s where each is an experimental replicate for the second condition, usually a treatment

Value

The squared Mahalanobis distance

The R score

The MR score of the Ithzak et al. 2016/2017

Examples

## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## compute delta matrix
deltaMatrix <- exprs(control[[1]]) - exprs(treatment[[1]])
res <- bandle:::robustMahalanobis(deltaMatrix)
##' @examples 
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## compute delta matrix
deltaMatrix1 <- exprs(control[[1]]) - exprs(treatment[[1]])
deltaMatrix2 <- exprs(control[[2]]) - exprs(treatment[[2]])
mr_score <- bandle:::reprodScore(deltaMatrix1, deltaMatrix2)
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
d1 <- tansim$lopitrep
control1 <- d1[1:3]
treatment1 <- d1[4:6]
mr1 <- mrMethod(objectCond1 = control1, objectCond2 = treatment1)
plot(mr1$Mscore, mr1$Rscore, pch = 21, 
     xlab = "MScore", ylab = "RScore")
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## compute delta matrix
deltaMatrix <- exprs(control[[1]]) - exprs(treatment[[1]])
res <- bandle:::robustMahalanobis(deltaMatrix)
##' @examples 
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## compute delta matrix
deltaMatrix1 <- exprs(control[[1]]) - exprs(treatment[[1]])
deltaMatrix2 <- exprs(control[[2]]) - exprs(treatment[[2]])
mr_score <- bandle:::reprodScore(deltaMatrix1, deltaMatrix2)
library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                    numRep = 6L,
                   numDyn = 100L)
d1 <- tansim$lopitrep
control1 <- d1[1:3]
treatment1 <- d1[4:6]
mr1 <- mrMethod(objectCond1 = control1, objectCond2 = treatment1)
plot(mr1$Mscore, mr1$Rscore, pch = 21, 
     xlab = "MScore", ylab = "RScore")

Generate a dynamic spatial proteomics experiment

Description

A function to simulate dynamic spatial proteomics data using a bootstrap method

Usage

sim_dynamic(
  object,
  subsample = NULL,
  knn_par = 10L,
  fcol = "markers",
  numRep = 6L,
  method = "wild",
  batch = FALSE,
  frac_perm = FALSE,
  nu = 2,
  numDyn = 20L
)
sim_dynamic(
  object,
  subsample = NULL,
  knn_par = 10L,
  fcol = "markers",
  numRep = 6L,
  method = "wild",
  batch = FALSE,
  frac_perm = FALSE,
  nu = 2,
  numDyn = 20L
)

Arguments

`object`	A instance of class `MSnSet` from which to generate a spatial proteomics dataset.
`subsample`	how many proteins to subsample to speed up analysis. Default is NULL.
`knn_par`	the number of nearest neighbours to use in KNN classification to simulate dataset. Default is 10
`fcol`	feature column to indicate markers. Default is "markers". Proteins with unknown localisations must be encoded as "unknown".
`numRep`	The total number of datasets to generate. Default is 6. An integer must be provided
`method`	The bootstrap method to use to simulate dataset. Default is "wild". refer to BANDLE paper for more details.
`batch`	Whether or not to include batch effects. Default is FALSE.
`frac_perm`	whether or not to permute the fractions. Default is FALSE
`nu`	parameter to generate residual inflated noise. Default is 2. See BANDLE paper for more details
`numDyn`	An integer number of protein to simulate dynamic transitions. Default is 20

Value

returns simulate dynamic lopit datasets and the name of the relocalated protein.

Examples

library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, numRep = 6L, numDyn = 100L)


library(pRolocdata)
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, numRep = 6L, numDyn = 100L)

Generate a PCA plot with smoothed probability contours

Description

Generate a PCA plot with smoothed probability contours

Usage

spatial2D(
  object,
  params,
  fcol = "markers",
  dims = c(1, 2),
  cov.function = NULL,
  theta = 2,
  derivative = 2,
  k = 1,
  cond = 1,
  n = 1,
  breaks = c(0.99, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7),
  aspect = 0.5
)
spatial2D(
  object,
  params,
  fcol = "markers",
  dims = c(1, 2),
  cov.function = NULL,
  theta = 2,
  derivative = 2,
  k = 1,
  cond = 1,
  n = 1,
  breaks = c(0.99, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7),
  aspect = 0.5
)

Arguments

`object`	An instance of class `MSnSet` to provide the pca coordinates
`params`	An instance of class `bandleParams`
`fcol`	Feature columns that defines the markers. Defaults to "markers".
`dims`	The PCA dimensions to plot. Defaults to `c(1,2)`
`cov.function`	The covariance function for the smoothing kernel. Defaults to wendland.cov
`theta`	The theta parameter of the wendland.cov. Defaults to 2.
`derivative`	The derivative paramter of the wendland.cov. Defaults to 2.
`k`	The k parameter of the wendland.cov
`cond`	Which conditions do we want to plot. Must be `1` or `2`. Default is `1`
`n`	The chain from which we plot the probability distribution. Default is 1.
`breaks`	The levels at which to plot the contours. Defaults to c(0.99, 0.95, 0.9, 0.85, 0.8, 0.75, 0.7)
`aspect`	The aspect ratio of the pca plots. Defaults to 0.5.

Value

returns a named vector of differential localisation probabilities

Examples

 ## Not run: 
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 1, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process the results
bandleres <- bandleProcess(res)

## plot the results
spatial2D(control[[1]], bandleres)

## End(Not run)
## Not run: 
## Generate some example data
library("pRolocdata")
data("tan2009r1")
set.seed(1)
tansim <- sim_dynamic(object = tan2009r1, 
                      numRep = 4L,
                      numDyn = 100L)
data <- tansim$lopitrep
control <- data[1:2]
treatment <- data[3:4]

## fit GP params
gpParams <- lapply(tansim$lopitrep, function(x) 
fitGPmaternPC(x, hyppar = matrix(c(0.5, 1, 100), nrow = 1)))

## run bandle
res <- bandle(objectCond1 = control,
              objectCond2 = treatment, 
              gpParams = gpParams,
              fcol = "markers",  
              numIter = 5L, 
              burnin = 1L, 
              thin = 2L,
              numChains = 1, 
              BPPARAM = SerialParam(RNGseed = 1),
              seed = 1)
               
## Process the results
bandleres <- bandleProcess(res)

## plot the results
spatial2D(control[[1]], bandleres)

## End(Not run)

inherits StatSratum

Description

inherits StatSratum

Usage

StatStratum
StatStratum

Format

An object of class StatStratum (inherits from Stat, ggproto, gg) of length 5.

Package 'bandle'

Help Index

An R package for the Bayesian analysis of differential subcellular localisation experiments

Description

Details

Author(s)

References

Differential localisation experiments using the bandle method

Description

Usage

Arguments

Details

Value

Examples

Number of outliers at each iteration of MCMC

Description

Usage

Arguments

Value

Infrastructure to to store and process MCMC results

Description

Usage

Arguments

Details

Value

Slots

Make predictions from a bandle analysis

Description

Usage

Arguments

Value

Examples

process bandle results

Description

Usage

Arguments

Value

Examples

bessel function of the second kind from boost library

Description

Usage

Arguments

Value

Examples

Calculate the Gelman and Rubin diagnostic for bandle output

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

Compute the expected False Discovery Rate

Description

Usage

Arguments

Value

Examples

Fit a Gaussian process to spatial proteomics data

Description

Usage

Arguments

Details

Value

Examples

Container for GP results

Description

Details

Slots

Compute GP gradient

Description

Usage

Arguments

Value

Examples

Computes the Kullback-Leibler divergence between Polya-Gamma and Dirichlet priors