This vignette provides an introduction to the BANDLE package (Crook et al. 2021) and follows a short theortical example of how to perform differential localisation analysis of quantitative proteomics data using the BANDLE model. Explanation and general recommendations of the input parameters are provided here. For a more comprehensive workflow which follows a real-life use case, please see the second vignette in this package.
Bayesian ANalysis of Differential Localisation Experiments (BANDLE) is an integrative semi-supervised functional mixture model, developed by (Crook et al. 2021), to obtain the probability of a protein being differentially localised between two conditions.
In this vignette we walk users through how to install and use the R (R Development Core Team 2011)
Bioconductor (Gentleman et al. 2004)
by simulating a well-defined differential localisation experiment from spatial
proteomics data from the
pRolocdata package (Gatto et al. 2014).
The BANDLE method uses posterior Bayesian computations performed using Markov-chain Monte-Carlo (MCMC) and thus uncertainty estimates are available (Gilks, Richardson, and Spiegelhalter 1995). Throughout this vignette we use the term differentially localised to pertain to proteins which are assigned to different sub-cellular localisations between two conditions. One of the main outputs of BANDLE is the probability that a protein is differentially localised between two conditions.
The package can be installed with the
if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("bandle")
and then loaded,
For visualisation we also load the packages,
In this vignette and Crook et al. (2021), the main data source that we use to study
differential protein sub-cellular localisation are data from high-throughput
mass spectrometry-based experiments. The data from these types of experiments
traditionally yield a matrix of measurements wherein we have, for example, PSMs,
peptides or proteins along the rows, and samples/channels/fractions along the
bandle package uses the
MSnSet class as implemented in the
Bioconductor MSnbase package and thus requires users to import
and store their data as a
MSnSet instance. For more details on how to create a
MSnSet see the relevant vignettes in pRoloc. The
pRolocdata experiment data package is a good starting place to
look for test data. This data package contains tens of quantitative proteomics
experiments, stored as
To get started with the basics of using
bandle we begin by generating a simple
example dataset which simulates a differential localisation experiment (please
see the second vignette in this package for a full real-life biological use
case). In this example data, the key elements are replicates, and a perturbation
of interest. There is code within the bandle package to simulate
an example experiment.
In the code chunk below we begin by loading the pRolocdata
package to obtain a spatial proteomics dataset. This will be the basis of our
simulation which will use boostrapping to generate new datasets. The dataset we
have chosen to load is a dataset from 2009 (
tan2009r1). This is data from a
early LOPIT experiment performed on Drosophila embryos (Tan et al. 2009). The aim of
this experiment was to apply LOPIT to an organism with heterogeneous cell types.
This experiment used four isotopes across four distinct fractions and thus
yielded four measurements (features) per protein profile. We visualise the
data by using principal components analysis.
library("pRolocdata") data("tan2009r1") ## Let's set the stock colours of the classes to plot to be transparent setStockcol(NULL) setStockcol(paste0(getStockcol(), "90")) ## Plot the data using plot2D from pRoloc plot2D(tan2009r1, main = "An example spatial proteomics datasets", grid = FALSE) addLegend(tan2009r1, where = "topleft", cex = 0.7, ncol = 2)
The following code chuck simulates a differential localisation experiment. It
numRep/2 of each a control and treatment condition. We will also
simulate relocalisations for
set.seed(1) tansim <- sim_dynamic(object = tan2009r1, numRep = 6L, numDyn = 100L)
##  "markers"
The list of the 6 simulated experiments are found in
tansim$lopitrep. Each one
MSnSet instance (the standard data container for proteomics experimental
data). The first 3 are the simulated control experiments (see
tansim$lopitrep[1:3]), and the following 3 in the list are the treatment
condition simulated experiments (see
tansim$lopitrep[4:6]). We can plot them
plot2D function from
plot_title <- c(paste0("Replicate ", seq(3), " condition", " A"), paste0("Replicate ", seq(3), " condition", " B")) par(mfrow = c(2, 3)) out <- lapply(seq(tansim$lopitrep), function(z) plot2D(tansim$lopitrep[[z]], grid = FALSE, main = plot_title[z]))
For understanding, exploring and visualizing individual spatial proteomics
experiments, see the vignettes in
## MSnSet (storageMode: lockedEnvironment) ## assayData: 888 features, 4 samples ## element names: exprs ## protocolData: none ## phenoData ## sampleNames: X114 X115 X116 X117 ## varLabels: Fractions ## varMetadata: labelDescription ## featureData ## featureNames: P20353 P53501 ... P07909 (888 total) ## fvarLabels: FBgn Protein.ID ... knn.scores (18 total) ## fvarMetadata: labelDescription ## experimentData: use 'experimentData(object)' ## pubMedIds: 19317464 ## Annotation: ## - - - Processing information - - - ## Added markers from 'mrk' marker vector. Thu Jul 16 22:53:44 2015 ## Performed knn prediction (k=10) Tue Oct 24 16:21:36 2023 ## MSnbase version: 1.17.12
The main function of the package is
bandle, this uses a complex model
to analyse the data. Markov-Chain Monte-Carlo (MCMC) is used to sample the
posterior distribution of parameters and latent variables. From which statistics
of interest can be computed. Here we only run a few iterations for brevity but
typically one needs to run thousands of iterations to ensure convergence, as
well as multiple parallel chains.
First, we need to fit non-parametric regression functions to the markers
profiles, upon which we place our analysis. This uses Gaussian processes. The
fitGPmaternPC function can be used and fits some default penalised complexity
?fitGP), which works well. However, these can be altered, which is
demonstrated in the next code chunk
par(mfrow = c(4, 3)) gpParams <- lapply(tansim$lopitrep, function(x) fitGPmaternPC(x, hyppar = matrix(c(10, 60, 250), nrow = 1)))
We apply the
fitGPmaternPC function to each datasets by calling
tansim$lopitrep list of datasets. The output of
fitGPmaternPC returns a
list of posterior predictive means and standard deviations. As well as MAP
hyperparamters for the GP.
Note here we the use the default
hyppar = matrix(c(10, 60, 250), nrow = 1) as
a starting point for fitting the GPs to the marker profile distributions. In the
Crook et al. (2021) manuscript we found that these values worked well for smaller spatial
proteomics datasets. This was visually assessed by passing these values and
visualising the GP fit using the
plotGPmatern function can be used to plot the profiles for each
class in each replicate condition with the posterior predictive distributions
overlayed with the markers protein profiles.
For example, to plot the predictive distributions of the first dataset,
par(mfrow = c(4, 3)) plotGPmatern(tansim$lopitrep[], params = gpParams[])