pRolocR package for the analysis and interpretation of spatial proteomics data. It walks the reader through the creation of MSnSet instances, that hold the quantitative proteomics data and meta-data and introduces several aspects of data analysis, including data visualisation and application of machine learning to predict protein localisation.
MSnbase and pRoloc are under active developed; current functionality is evolving and new features are added on a regular basis. This software is free and open-source software. If you use it, please support the project by citing it in publications:
Gatto L. and Lilley K.S. MSnbase - an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28, 288-289 (2011).
Gatto L, Breckels LM, Wieczorek S, Burger T, Lilley KS. Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinformatics. 2014 May 1;30(9):1322-4..
Breckels LM, Mulvey CM, Lilley KS and Gatto L. A Bioconductor workflow for processing and analysing spatial proteomics data. F1000Research 2016, 5:2926 doi: 10.12688/f1000research.10411.1.
If you are using the phenoDisco function, please also cite
Breckels L.M., Gatto L., Christoforou A., Groen A.J., Kathryn Lilley K.S. and Trotter M.W. The effect of organelle discovery upon sub-cellular protein localisation. J Proteomics, S1874-3919(13)00094-8 (2013).
If you are using any of the transfer learning functions, please also cite
Breckels LM, Holden S, Wonjar D, Mulvey CM, Christoforou A, Groen A, Trotter MW, Kohlbacker O, Lilley KS and Gatto L (2016). Learning from heterogeneous data sources: an application in spatial proteomics. PLoS Comput Biol 13;12(5):e1004920. doi: 10.1371/journal.pcbi.1004920.
If you are using any of the Bayesian generative models, please also cite
A Bayesian Mixture Modelling Approach For Spatial Proteomics Oliver M Crook, Claire M Mulvey, Paul D. W. Kirk, Kathryn S Lilley, Laurent Gatto bioRxiv 282269; doi: https://doi.org/10.1101/282269
For an introduction to spatial proteomics data analysis:
Gatto L, Breckels LM, Burger T, Nightingale DJ, Groen AJ, Campbell C, Nikolovski N, Mulvey CM, Christoforou A, Ferro M, Lilley KS. A foundation for reliable spatial proteomics data analysis. Mol Cell Proteomics. 2014 Aug;13(8):1937-52. doi: 10.1074/mcp.M113.036350.
The pRoloc package contains additional vignettes and reference material:
You are welcome to contact me directly about pRoloc.
For bugs, typos, suggestions or other questions, please file an issue
in our issue tracking system
(https://github.com/lgatto/pRoloc/issues) providing as much
information as possible, a reproducible example and the output of
If you wish to reach a broader audience for general questions about
proteomics analysis using
R, you may want to use the Bioconductor
support site: https://support.bioconductor.org/.
Spatial (or organelle) proteomics is the study of the localisation of proteins inside cells. The sub-cellular compartment can be organelles, i.e. structures defined by lipid bi-layers,macro-molecular assemblies of proteins and nucleic acids or large protein complexes. In this document, we will focus on mass-spectrometry based approaches that assay a population of cells, as opposed as microscopy based techniques that monitor single cells, as the former is the primary concern of pRoloc, although the techniques described below and the infrastructure in place could also be applied the processed image data. The typical experimental use-case for using pRoloc is a set of fractions, originating from a total cell lysate. These fractions can originate from a continuous gradient, like in the LOPIT (Dunkley et al. 2006) or PCP (Foster et al. 2006) approaches, or can be discrete fractions. The content of the fractions is then identified and quantified (using labelled or un-labelled quantitation techniques). Using relative quantitation of known organelle residents, termed organelle markers, organelle-specific profiles along the gradient are determined and new residents are identified based on matching of these distribution profiles. See for example (Gatto et al. 2010) and references therein for a detailed review on organelle proteomics.
It should be noted that large protein complexes, that are not necessarily separately enclosed within their own lipid bi-layer, can be detected by such techniques, as long as a distinct profile can be defined across the fractions.
R (R Development Core Team 2011) is a statistical programming language and interactive working environment. It can be expanded by so-called packages to confer new functionality to users. Many such packages have been developed for the analysis of high-throughput biology, notably through the Bioconductor project (Gentleman et al. 2004). Two packages are of particular interest here, namely MSnbase (Gatto and Lilley 2012) and pRoloc. The former provides flexible infrastructure to store and manipulate quantitative proteomics data and the associated meta-data and the latter implements specific algorithmic technologies to analyse organelle proteomics data.
Among the advantages of R are robust statistical procedures, good visualisation capabilities, excellent documentation, reproducible research1 The content of this document is compiled (the code is executed and its output, text and figures, is displayed dynamically) to generate the pdf file., power and flexibility of the R language and environment itself and a rich environment for specialised functionality in many domains of bioinformatics: tools for many omics technologies, including proteomics, bio-statistics, gene ontology and biological pathway analysis, … Although there exists some specific graphical user interfaces (GUI), interaction with R is executed through a command line interface. While this mode of interaction might look alien to new users, experience has proven that after a first steep learning curve, great results can be achieved by non-programmers. Furthermore, specific and general documentation is plenty and beginners and advanced course material are also widely available.
Once R is started, the first step to enable functionality of a
specific packages is to load them using the
library function, as
shown in the code chunk below:
library("MSnbase") library("pRoloc") library("pRolocdata")
MSnbase implements the data containers that are used by pRoloc. pRolocdata is a data package that supplies several published organelle proteomics data sets.
As a final setup step, we set the default colour palette for some of our custom plotting functionality to use semi-transparent colours in the code chunk below (see ?setStockcol for details). This facilitates visualisation of overlapping points.
setStockcol(NULL) ## reset first setStockcol(paste0(getStockcol(), 70))
The data used in this tutorial has been published in
(Tan et al. 2009). The LOPIT technique (Dunkley et al. 2006) is used to
localise integral and associated membrane proteins in
Drosophila melanogaster embryos. Briefly, embryos were
collected at 0 – 16 hours, homogenised and centrifuged to collect the
supernatant, removing cell debris and nuclei. Membrane fractionation
was performed on a iodixanol gradient and fractions were quantified
using iTRAQ isobaric tags (Ross et al. 2004) as follows: fractions 4/5,
114; fractions 12/13, 115; fraction 19, 116 and fraction 21,
117. Labelled peptides were then separated using cation exchange
chromatography and analysed by LS-MS/MS on a QSTAR XL
quadrupole-time-of-flight mass spectrometer (Applied Biosystems). The
original localisation analysis was performed using partial least
square discriminant analysis (PLS-DA). Relative quantitation data was
retrieved from the supplementary file
and imported into R as described below. We will concentrate on the
This section illustrates how to import data in comma-separated value
(csv) format into an appropriate R data structure. The first section
shows the original
csv (comma separated values) spreadsheet, as
published by the authors, and how one can read such a file into
using the read.csv function. This spreadsheet file is similar to the
output of many quantitation software.
In the next section, we show 2
csv files containing a subset of the
columns of original
pr800866n_si_004-rep1.csv file and another
short file, created manually, that will be used to create the
appropriate R data.
## The original data for replicate 1, available ## from the pRolocdata package f0 <- dir(system.file("extdata", package = "pRolocdata"), full.names = TRUE, pattern = "pr800866n_si_004-rep1.csv") csv <- read.csv(f0)
The three first lines of the original spreadsheet, containing the data for replicate one, are illustrated below (using the function head). It contains 888 rows (proteins) and 16 columns, including protein identifiers, database accession numbers, gene symbols, reporter ion quantitation values, information related to protein identification, …
## Protein.ID FBgn Flybase.Symbol No..peptide.IDs Mascot.score ## 1 CG10060 FBgn0001104 G-ialpha65A 3 179.86 ## 2 CG10067 FBgn0000044 Act57B 5 222.40 ## 3 CG10077 FBgn0035720 CG10077 5 219.65 ## No..peptides.quantified area.114 area.115 area.116 area.117 ## 1 1 0.379000 0.281000 0.225000 0.114000 ## 2 9 0.420000 0.209667 0.206111 0.163889 ## 3 3 0.187333 0.167333 0.169667 0.476000 ## PLS.DA.classification Peptide.sequence Precursor.ion.mass ## 1 PM ## 2 PM ## 3 ## Precursor.ion.charge pd.2013 pd.markers ## 1 PM unknown ## 2 PM unknown ## 3 unknown unknown
csvfiles to R data
There are several ways to create the desired R data object, termed
MSnSet, that will be used to perform the actual sub-cellular
localisation prediction. Here, we illustrate a method that uses
separate spreadsheet files for quantitation data, feature meta-data
and sample (fraction) meta-data and the
function, that will hopefully be the most straightforward for new
## The quantitation data, from the original data f1 <- dir(system.file("extdata", package = "pRolocdata"), full.names = TRUE, pattern = "exprsFile.csv") exprsCsv <- read.csv(f1) ## Feature meta-data, from the original data f2 <- dir(system.file("extdata", package = "pRolocdata"), full.names = TRUE, pattern = "fdataFile.csv") fdataCsv <- read.csv(f2) ## Sample meta-data, a new file f3 <- dir(system.file("extdata", package = "pRolocdata"), full.names = TRUE, pattern = "pdataFile.csv") pdataCsv <- read.csv(f3)
exprsFile.csvcontaining the quantitation (expression) data for the 888 proteins and 4 reporter tags.
## FBgn X114 X115 X116 X117 ## 1 FBgn0001104 0.379000 0.281000 0.225000 0.114000 ## 2 FBgn0000044 0.420000 0.209667 0.206111 0.163889 ## 3 FBgn0035720 0.187333 0.167333 0.169667 0.476000
fdataFile.csvcontaining meta-data for the 888 features (here proteins).
## FBgn ProteinID FlybaseSymbol NoPeptideIDs MascotScore ## 1 FBgn0001104 CG10060 G-ialpha65A 3 179.86 ## 2 FBgn0000044 CG10067 Act57B 5 222.40 ## 3 FBgn0035720 CG10077 CG10077 5 219.65 ## NoPeptidesQuantified PLSDA ## 1 1 PM ## 2 9 PM ## 3 3
pdataFile.csvcontaining samples (here fractions) meta-data. This simple file has been created manually.
## sampleNames Fractions ## 1 X114 4/5 ## 2 X115 12/13 ## 3 X116 19 ## 4 X117 21
A self-contained data structure, called MSnSet (defined in
the MSnbase package) can now easily be generated using the
readMSnSet constructor, providing the respective
csv file names shown above and specifying that the data is
sep = ","). Below, we call that object
tan2009r1 and display its content.
tan2009r1 <- readMSnSet(exprsFile = f1, featureDataFile = f2, phenoDataFile = f3, sep = ",") tan2009r1
## 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: FBgn0001104 FBgn0000044 ... FBgn0001215 (888 total) ## fvarLabels: ProteinID FlybaseSymbol ... PLSDA (6 total) ## fvarMetadata: labelDescription ## experimentData: use 'experimentData(object)' ## Annotation: ## - - - Processing information - - - ## Quantitation data loaded: Tue Oct 27 22:33:59 2020 using readMSnSet. ## MSnbase version: 2.16.0
readMSnSet2 function provides a simplified import pipeline. It
takes a single spreadsheet as input (default is
csv) and extract the
columns identified by
ecol to create the expression data, while the
others are used as feature meta-data.
ecol can be a
the respective column labels or a numeric with their indices. In the
former case, it is important to make sure that the names match
exactly. Special characters like
'(' will be transformed by
'.' when the
csv file is read in. Optionally, one can also
specify a column to be used as feature names. Note that these must be
unique to guarantee the final object validity.
ecol <- paste("area", 114:117, sep = ".") fname <- "Protein.ID" eset <- readMSnSet2(f0, ecol, fname) eset
## MSnSet (storageMode: lockedEnvironment) ## assayData: 888 features, 4 samples ## element names: exprs ## protocolData: none ## phenoData: none ## featureData ## featureNames: CG10060 CG10067 ... CG9983 (888 total) ## fvarLabels: Protein.ID FBgn ... pd.markers (12 total) ## fvarMetadata: labelDescription ## experimentData: use 'experimentData(object)' ## Annotation: ## - - - Processing information - - - ## MSnbase version: 2.16.0
ecol columns can also be queried interactively from R using the
grepEcols function. The former return a character
with all column names, given a splitting character, i.e. the
separation value of the spreadsheet (typically
tsv, …). The latter can be used to grep a
pattern of interest to obtain the relevant column indices.
##  "\"Protein ID\"" "\"FBgn\"" ##  "\"Flybase Symbol\"" "\"No. peptide IDs\"" ##  "\"Mascot score\"" "\"No. peptides quantified\"" ##  "\"area 114\"" "\"area 115\"" ##  "\"area 116\"" "\"area 117\"" ##  "\"PLS-DA classification\"" "\"Peptide sequence\"" ##  "\"Precursor ion mass\"" "\"Precursor ion charge\"" ##  "\"pd.2013\"" "\"pd.markers\""
grepEcols(f0, "area", ",")
##  7 8 9 10
e <- grepEcols(f0, "area", ",") readMSnSet2(f0, e)
## MSnSet (storageMode: lockedEnvironment) ## assayData: 888 features, 4 samples ## element names: exprs ## protocolData: none ## phenoData: none ## featureData ## featureNames: 1 2 ... 888 (888 total) ## fvarLabels: Protein.ID FBgn ... pd.markers (12 total) ## fvarMetadata: labelDescription ## experimentData: use 'experimentData(object)' ## Annotation: ## - - - Processing information - - - ## MSnbase version: 2.16.0
phenoData slot can now be updated accordingly using the
Although there are additional specific sub-containers for additional
meta-data (for instance to make the object MIAPE compliant), the
feature (the sub-container, or slot
featureData) and sample (the
phenoData slot) are the most important ones. They need to meet the
following validity requirements (see figure below):
the number of row in the expression/quantitation data and feature data must be equal and the row names must match exactly, and
the number of columns in the expression/quantitation data and number of row in the sample meta-data must be equal and the column/row names must match exactly.
It is common, in the context of pRoloc to update the
feature meta-data (described in section 5) by adding
new columns, without breaking the objects validity. Similarly, the
sample meta-data can also be updated by adding new sample variables. A
detailed description of the
MSnSet class is available by typing
?MSnSet in the R console.
The individual parts of this data object can be accessed with their respective accessor methods:
The advantage of this structure is that it can be manipulated as a whole and the respective parts of the data object will remain compatible. The code chunk below, for example, shows how to extract the first 5 proteins and 2 first samples:
smallTan <- tan2009r1[1:5, 1:2] dim(smallTan)
##  5 2
## X114 X115 ## FBgn0001104 0.379000 0.281000 ## FBgn0000044 0.420000 0.209667 ## FBgn0035720 0.187333 0.167333 ## FBgn0003731 0.247500 0.253000 ## FBgn0029506 0.216000 0.183000
Several data sets, including the 3 replicates from (Tan et al. 2009), are
distributed as MSnSet instances in the pRolocdata
package. Others include, among others, the Arabidopsis thaliana
LOPIT data from (Dunkley et al. 2006) (
dunkley2006) and the mouse PCP data
from (Foster et al. 2006) (
foster2006). Each data set can be loaded with
data function, as show below for the first replicate from
(Tan et al. 2009).
The original marker proteins are available as a feature meta-data
markers.orig and the output of the partial least
square discriminant analysis, applied in the original publication, in
PLSDA feature variable. The most up-to-date marker list for the
experiment can be found in
markers. This feature meta-data column
can be added from a simple
csv markers files using the
function - see
?addMarkers for details.
The organelle markers are illustrated below using the convenience function getMarkers, but could also be done manually by accessing feature variables directly using fData().
getMarkers(tan2009r1, fcol = "markers.orig")
## organelleMarkers ## ER Golgi PM mitochondrion unknown ## 20 6 15 14 833
getMarkers(tan2009r1, fcol = "PLSDA")
## organelleMarkers ## ER/Golgi PM mitochondrion unknown ## 235 180 74 399
Important As can be seen above, some proteins are labelled
"unknown", defining non marker proteins. This is a convention in
many pRoloc functions. Missing annotations (empty
string) will not be considered as of unknown localisation; we prefer
to avoid empty strings and make the absence of known localisation
explicit by using the
"unknown" tag. This information will be used
to separate marker and non-marker (unlabelled) proteins when
proceeding with data visualisation and clustering (sections
3 and 5.1) and classification analysis
The pRoloc package distributes a set of markers that
have been obtained by mining the pRolocdata
datasets and curation by various members of the
Cambridge Centre for Proteomics. The
available marker sets can be obtained and loaded using the
## 7 marker lists available: ## Arabidopsis thaliana [atha]: ## Ids: TAIR, 543 markers ## Drosophila melanogaster [dmel]: ## Ids: Uniprot, 179 markers ## Gallus gallus [ggal]: ## Ids: IPI, 102 markers ## Homo sapiens [hsap]: ## Ids: Uniprot, 872 markers ## Mus musculus [mmus]: ## Ids: Uniprot, 937 markers ## Saccharomyces cerevisiae [scer_sgd]: ## Ids: SGD, 259 markers ## Saccharomyces cerevisiae [scer_uniprot]: ## Ids: Uniprot Accession, 259 markers
## Q7JZN0 Q7KLV9 Q9VIU7 P15348 Q7KMP8 O01367 ## "ER" "Proteasome" "ER" "Nucleus" "Proteasome" "Nucleus"
## ## Cytoskeleton ER Golgi Lysosome Nucleus ## 7 24 7 8 21 ## PM Peroxisome Proteasome Ribosome 40S Ribosome 60S ## 25 4 14 22 32 ## mitochondrion ## 15
These markers can then be added to a new MSnSet using the addMarkers function by matching the marker names (protein identifiers) and the feature names of the MSnSet. See ?addMarkers for examples.
The quantitation data obtained in the supplementary file is normalised to the sum of intensities of each protein; the sum of fraction quantitation for each protein equals 1 (considering rounding errors). This can quickly be verified by computing the row sums of the expression data.
## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 0.9990 0.9999 1.0000 1.0000 1.0001 1.0010
normalise method (also available as
normalize) from the
MSnbase package can be used to obtain relative
quantitation data, as illustrated below on another iTRAQ test data
set, available from MSnbase. Several normalisation
methods are available and described in
?normalise. For many
algorithms, including classifiers in general and support vector
machines in particular, it is important to properly per-process the
data. Centering and scaling of the data is also available with the
In the code chunk below, we first create a test MSnSet
instance2 Briefly, the
itraqdata raw iTRAQ4-plex data is quantified by trapezoidation using MSnbase functionality. See the
MSnbase-demo vignette for details.
and illustrate the effect of
normalise(..., method = "sum").
## create a small illustrative test data data(itraqdata) itraqdata <- quantify(itraqdata, method = "trap", reporters = iTRAQ4) ## the quantification data head(exprs(itraqdata), n = 3)
## iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117 ## X1 1347.6158 2247.3097 3927.6931 7661.1463 ## X10 739.9861 799.3501 712.5983 940.6793 ## X11 27638.3582 33394.0252 32104.2879 26628.7278
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 59.06 5638.09 15344.43 38010.87 42256.61 305739.04 1
## normalising to the sum of feature intensitites itraqnorm <- normalise(itraqdata, method = "sum") processingData(itraqnorm)
## - - - Processing information - - - ## Data loaded: Wed May 11 18:54:39 2011 ## Updated from version 0.3.0 to 0.3.1 [Fri Jul 8 20:23:25 2016] ## iTRAQ4 quantification by trapezoidation: Tue Oct 27 22:34:02 2020 ## Normalised (sum): Tue Oct 27 22:34:02 2020 ## MSnbase version: 1.1.22
head(exprs(itraqnorm), n = 3)
## iTRAQ4.114 iTRAQ4.115 iTRAQ4.116 iTRAQ4.117 ## X1 0.08875373 0.1480074 0.2586772 0.5045617 ## X10 0.23178064 0.2503748 0.2232022 0.2946424 ## X11 0.23077081 0.2788287 0.2680598 0.2223407
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 1 1 1 1 1 1 1
Note above how the processing undergone by the MSnSet
itraqnorm is stored in
another such specific sub-container, the
The different features (proteins in the
above, but these could also represent peptides or MS\(^2\) spectra) are
characterised by unique names. These can be retrieved with the
##  "P20353" "P53501" "Q7KU78" "P04412" "Q7KJ73" "Q7JZN0"
If we look back at section 2.2.2, we see that these have been
automatically assigned using the first columns in the
fdataFile.csv files. It is thus crucial for these respective
first columns to be identical. Similarly, the sample names can be
The following sections will focus on two closely related aspects, data visualisation and data analysis (i.e. organelle assignments). Data visualisation is used in the context on quality control, to convince ourselves that the data displays the expected properties so that the output of further processing can be trusted. Visualising results of the localisation prediction is also essential, to control the validity of these results, before proceeding with orthogonal (and often expensive) dry or wet validation.
The underlying principle of gradient approaches is that we have
separated organelles along the gradient and by doing so, generated
organelle-specific protein distributions along the gradient
fractions. The most natural visualisation is shown on figure
1, obtained using the sub-setting functionality of
MSnSet instances and the
plotDist function, as illustrated below.
## indices of the mito markers j <- which(fData(tan2009r1)$markers.orig == "mitochondrion") ## indices of all proteins assigned to the mito i <- which(fData(tan2009r1)$PLSDA == "mitochondrion") plotDist(tan2009r1[i, ], markers = featureNames(tan2009r1)[j])