Using pRoloc for spatial proteomics data analysis
2 May 2023
Abstract
This tutorial illustrates the usage of the pRoloc R 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.
Package
pRoloc 1.40.1
Foreword
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:
- pRoloc-tutorial: pRoloc tutorial (this vignette).
- pRoloc-ml: Machine learning techniques available in pRoloc.
- pRoloc-transfer-learning: A transfer learning algorithm for spatial proteomics.
- pRoloc-goannotations: Annotating spatial proteomics data.
- pRoloc-bayesian: Bayesian spatial proteomics with pRoloc.
Questions and bugs
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
sessionInfo().
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/.
1 Introduction
1.1 Spatial proteomics
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.
1.2 About R and pRoloc
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))2 Data structures
2.1 Example data
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 pr800866n_si_004.xls
(http://pubs.acs.org/doi/suppl/10.1021/pr800866n/suppl_file/pr800866n_si_004.xls)
and imported into R as described below. We will concentrate on the
first replicate.
2.2 Importing and loading data
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.
2.2.1 The original data file
## 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, …
head(csv, n=3)## 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 unknown2.2.2 From csv files 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 readMSnSet constructor
function, that will hopefully be the most straightforward for new
users.
## 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.
head(exprsCsv, n=3)## 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 0.476000fdataFile.csvcontaining meta-data for the 888 features (here proteins).
head(fdataCsv, n=3)## 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 3pdataFile.csvcontaining samples (here fractions) meta-data. This simple file has been created manually.
pdataCsv## sampleNames Fractions
## 1 X114 4/5
## 2 X115 12/13
## 3 X116 19
## 4 X117 21A 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
comma-separated (with 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 May 2 17:55:25 2023 using readMSnSet.
## MSnbase version: 2.26.02.3 A shorter input work flow
The 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 character with
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 '-' or '(' will be transformed by
R into '.' 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: 2.26.0The ecol columns can also be queried interactively from R using the
getEcols and grepEcols function. The former return a character
with all column names, given a splitting character, i.e. the
separation value of the spreadsheet (typically "," for csv,
"\t" for tsv, …). The latter can be used to grep a
pattern of interest to obtain the relevant column indices.
getEcols(f0, ",")## [1] "\"Protein ID\"" "\"FBgn\""
## [3] "\"Flybase Symbol\"" "\"No. peptide IDs\""
## [5] "\"Mascot score\"" "\"No. peptides quantified\""
## [7] "\"area 114\"" "\"area 115\""
## [9] "\"area 116\"" "\"area 117\""
## [11] "\"PLS-DA classification\"" "\"Peptide sequence\""
## [13] "\"Precursor ion mass\"" "\"Precursor ion charge\""
## [15] "\"pd.2013\"" "\"pd.markers\""grepEcols(f0, "area", ",")## [1] 7 8 9 10e <- 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: 2.26.0The phenoData slot can now be updated accordingly using the
replacement functions phenoData<- or pData<- (see ?MSnSet for
details).
2.3.1 The MSnSet class
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.
Dimension requirements for the respective expression, feature and sample meta-data slots.
The individual parts of this data object can be accessed with their respective accessor methods:
- the quantitation data can be retrieved with
exprs(tan2009r1), - the feature meta-data with
fData(tan2009r1)and - the sample meta-data with
pData(tan2009r1).
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)## [1] 5 2exprs(smallTan)## 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 0.183000Several 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
the data function, as show below for the first replicate from
(Tan et al. 2009).
data(tan2009r1)The original marker proteins are available as a feature meta-data
variables called markers.orig and the output of the partial least
square discriminant analysis, applied in the original publication, in
the 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 addMarkers
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 833getMarkers(tan2009r1, fcol = "PLSDA")## organelleMarkers
## ER/Golgi PM mitochondrion unknown
## 235 180 74 399Important 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
(section 5.2).
2.4 pRoloc’s organelle markers
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
pRolocmarkers function:
pRolocmarkers()## 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 markershead(pRolocmarkers("dmel"))## Q7JZN0 Q7KLV9 Q9VIU7 P15348 Q7KMP8 O01367
## "ER" "Proteasome" "ER" "Nucleus" "Proteasome" "Nucleus"table(pRolocmarkers("dmel"))##
## Cytoskeleton ER Golgi Lysosome Nucleus
## 7 24 7 8 21
## PM Peroxisome Proteasome Ribosome 40S Ribosome 60S
## 25 4 14 22 32
## mitochondrion
## 15These 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.
2.5 Data processing
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.
summary(rowSums(exprs(tan2009r1)))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.9990 0.9999 1.0000 1.0000 1.0001 1.0010The 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
scale method.
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 26628.7278summary(rowSums(exprs(itraqdata)))## 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 May 2 17:55:30 2023
## Normalised (sum): Tue May 2 17:55:30 2023
## MSnbase version: 1.1.22head(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 0.2223407summary(rowSums(exprs(itraqnorm)))## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1 1 1 1 1 1 1Note above how the processing undergone by the MSnSet
instances itraqdata and itraqnorm is stored in
another such specific sub-container, the processingData
slot.
The different features (proteins in the tan2009r1 data
above, but these could also represent peptides or MS\(^2\) spectra) are
characterised by unique names. These can be retrieved with the
featureNames function.
head(featureNames(tan2009r1))## [1] "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 exprsFile.csv
and fdataFile.csv files. It is thus crucial for these respective
first columns to be identical. Similarly, the sample names can be
retrieved with sampleNames(tan2009r1).
3 Data visualisation
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.
3.1 Profile plots
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])
Figure 1: Distribution of protein intensities along the fractions of the separation gradient for 4 organelles: mitochondrion (red), ER/Golgi (blue, ER markers and green, Golgi markers) and plasma membrane (purple)
3.2 Sub-cellular cluster dendrogram
To gain a quick overview of the distance/similarity between the
sub-cellular clusters, it can useful to compare average marker
profiles, rather than all profiles, as presented in the profile plots
above. The mrkHClust calculates average class profiles and generates
the resulting dendrogram.
mrkHClust(tan2009r1)
Figure 2: Hierarchical clustering
Average distance between organelle classes.
On figure 2, we see that the lysosome and the ribosome 60S are separated by the smallest distance. The advantage of this representation is that it provides a quick snapshot of the average similarity between organelles using the complete profiles (as opposed to a PCA plot, discussed in the next section). The main drawback is that it ignores any variability in individual markers (cluster thighness). It is however a good guide for a more thorough exploration of the data, as described in the next sections.
Note the colours of the labels on the dendrogram (figure
2), which match the colours used to annotate PCA
plots, described in the next section (figure 4. These
colours are defined at the session level (see getStockcol and
setStockcol) and re-used throughout pRoloc for
consistent annotation.
3.3 Average organelle class profile plot
We can also visualise the average organelle class profiles generated by
mrkConsProfiles using plotConsProfiles. We can optionally order the organelle
classes on the y-axis according to the heirachical clustering from mrkHClust.
See ?mrkHClust for more details
## histogram
hc <- mrkHClust(tan2009r1, plot=FALSE)
## order of markers according to histogram
mm <- getMarkerClasses(tan2009r1)
m_order <- levels(factor(mm))[order.dendrogram(hc)]
## average marker profile
fmat <- mrkConsProfiles(tan2009r1)
plotConsProfiles(fmat, order=m_order)
Figure 3: Average organelle class profiles
Protein intensity indicated by colour. Organelle classes ordered by hierarchical clustering
3.4 Dimensionality reduction
Alternatively, we can combine all organelle groups in one single 2
dimensional figure by applying a dimensionality reduction technique
such as Principal Component Analysis (PCA) using the plot2D function
(see figure 4). The protein profile vectors are
summarised into 2 values that can be visualised in two dimensions, so
that the variability in the data will be maximised along the first
principal component (PC1). The second principal component (PC2) is
then chosen as to be orthogonal to PC1 while explaining as much
variance in the data as possible, and so on for PC3, PC4, etc.
Using a PCA representation to visualise a spatial proteomics
experiment, we can easily plot all the proteins on the same figure as
well a many sub-cellular clusters. These clusters are defined in a
feature meta-data column (slot featureData, accessed as a
data.frame with the fData function) that is
declared with the fcol argument (default is
"markers" which contains the most current known markers for
this experiment, while the original markers published with the data
can be found in the slot "markers.orig").
plot2D(tan2009r1, fcol = "markers")
addLegend(tan2009r1, fcol = "markers", cex = .7,
where = "bottomright", ncol = 2)
Figure 4: PCA plot
Representation of the proteins of tan2009r1 after reduction of the 4 reporter quantitation data to 2 principal components.
As the default value for the fcol argument is "markers", it is not
necessary to specify it. It is however mandatory to specify other
annotation feature variables, such as to visualise the set of markers
described in the original publication.
plot2D(tan2009r1, fcol = "markers.orig")
addLegend(tan2009r1, fcol = "markers.orig", where = "bottomright")
Figure 5: PCA plot
Reduced set of markers for the tan2009r1 data projected onto 2 principal components.
It is also possible to visualise the data along 3 dimensions using the plot3D function, which works like the 2 dimension version (figure below). The resulting figure can be rotated by the user.
plot3D(tan2009r1)
Snapshot of the 3-dimensional PCA plot. The tan2009r1 data is represented along the first 3 principal components.
As can be seen on the figures 4, 5
and on the 3D plot above, we indicate on the axis
labels the percentage of total variance explained by the individual
PCs. It is not unusual to reach over 75% along the first two PCs,
even for experiments with several tens of fractions. One can calculate
this information for all PCs by setting method = "scree" in
plot2D. On figure 6, we see that the four PCs in the
tan2009r1 data account for 58.53, 29.96, 11.52
and 0 percent of the total variance.
plot2D(tan2009r1, method = "scree")
Figure 6: Percentage of variance explained
A variety of dimensionality reduction methods are available in
plot2D: PCA, MDS, kpca, lda, t-SNE, nipals, hexbin, none, scree.
Except for scree (see above) and none (no data transformation,
which can be useful when the data is already transformed and only
needs to be plotted), these can used to produce visualisation of the
data in two dimensions. Two are worth some discussion here; the
readers are redirected to the manual page for more details.
Linear discriminant analysis (LDA) will project the protein occupancy profiles in a new set of dimensions using as a criterion the separation of marker classes by maximising the between class variance to the within class variance ratio. As opposed to the unsupervised PCA, the supervised LDA should not be used as an to explore the data or as a quality control, but can be useful to assess if one or more organelles have been preferentially separated.
The t-Distributed Stochastic Neighbour Embedding (t-SNE)3 https://lvdmaaten.github.io/tsne/ (Maaten and Hinton 2008) is widely applied in many areas in computational biology and more generally field that need to visualise high-dimensional data. The t-SNE method is non-linear, and will emphasise separation of different features while grouping features with similar profiles. In addition, different transformations are applied on different regions leading to plots that can substantially differ from a PCA plot. As a result, proximity in two dimensions and tightness of the clusters can’t be related to these quantities in the original data. See How to Use t-SNE Effectively4 http://distill.pub/2016/misread-tsne/ for a useful non-technical introduction.
The results of the algorithm crucially depend on the values of its input parameters, in particular the perplexity, which balances global and local aspects of the data. The suggested range of value ranges from 5 to 50, and should not be greater than the number of data points (which we can assume not to be the case in modern spatial proteomics experiments). Below, we test the effect of this parameter along the suggested range, including the default value of 30, at which the algorithm converges.
perps <- sort(c(30, seq(5, 50, 15)))
data(HEK293T2011)
par(mfrow = c(2, 3))
plot2D(HEK293T2011, main = "PCA")
sapply(perps,
function(perp) {
plot2D(HEK293T2011, method = "t-SNE",
methargs = list(perplexity = perp))
title(main = paste("t-SNE, perplexity", perp))
})
Effect of t-SNE’s perplexity parameter on the human HEK293T2011 data.
Other parameters that can effect the results are the number of iterations and the learning rate epsilon.
The t-SNE algorithm takes much more time to complete than the other
available methods. In such cases, saving the results and re-plotting
with method none can be useful (see ?plot2D). In the case of this
document, the figure above, was pre-generated rather than
computed upon compilation.
3.5 Features of interest
In addition to highlighting sub-cellular niches as coloured clusters on the PCA plot, it is also possible to define some arbitrary features of interest that represent, for example, proteins of a particular pathway or a set of interaction partners. Such sets of proteins are recorded as FeaturesOfInterest instances, as illstrated below (using the ten first features of our experiment):
foi1 <- FeaturesOfInterest(description = "Feats of interest 1",
fnames = featureNames(tan2009r1[1:10]))
description(foi1)## [1] "Feats of interest 1"foi(foi1)## [1] "P20353" "P53501" "Q7KU78" "P04412" "Q7KJ73" "Q7JZN0" "Q7KLV9" "Q9VM65"
## [9] "Q9VCK0" "Q9VIU7"Several such features of interest can be combined into collections:
foi2 <- FeaturesOfInterest(description = "Feats of interest 2",
fnames = featureNames(tan2009r1[880:888]))
foic <- FoICollection(list(foi1, foi2))
foic## A collection of 2 features of interest.FeaturesOfInterest instances can now be overlaid on the PCA plot
with the highlightOnPlot function. The highlightOnPlot3D can be
used to overlay data onto a 3 dimensional figure produced by plot3D.
plot2D(tan2009r1, fcol = "PLSDA")
addLegend(tan2009r1, fcol = "PLSDA",
where = "bottomright",
cex = .7)
highlightOnPlot(tan2009r1, foi1,
col = "black", lwd = 2)
highlightOnPlot(tan2009r1, foi2,
col = "purple", lwd = 2)
legend("topright", c("FoI 1", "FoI 2"),
bty = "n", col = c("black", "purple"),
pch = 1)
Figure 7: Adding features of interest on a PCA plot
See ?FeaturesOfInterest and ?highlightOnPlot for more details.
3.6 Interactive visualisation
The pRolocGUI application allows one to explore the spatial proteomics data using an interactive, web-based interface (RStudio and Inc. 2014). The package is available from Bioconductor and can be installed and started as follows:
library("BiocManager")
BiocManager::install("pRolocGUI")
library("pRolocGUI")
pRolocVis(tan2009r1)
Screenshot of the pRolocGUI interface. The GUI is also available as an online app for the hyperLOPIT experiment from (Christoforou et al. 2016) at https://lgatto.shinyapps.io/christoforou2015/.
More details are available in the vignette that can be started from
the application by clicking on any of the question marks, by starting
the vignette from R with vignette("pRolocGUI") or can be accessed
online
(http://bioconductor.org/packages/devel/bioc/vignettes/pRolocGUI/inst/doc/pRolocGUI.html).
4 Assessing sub-cellular resolution
The sub-cellular resolution of a spatial proteomics experiment,
i.e. the quantitation of how well the respective sub-cellular niches
are separated, can be computed with the QSep function. Briefly, this
function compares, for each pairs of sub-cellular niches, the ratio
between the the average Euclidean distance between niche i and j and
the the average within distance of cluster j. A large ratio indicates
that i and j are well separated with respect to the thighness of
cluster j. The larger the distances, the better the spatial proteomics
experiment.
Below, we calulate and visualise the QSep distances for the
hyperLOPIT2015 data:
library("pRolocdata")
data(hyperLOPIT2015)
## Create the object and get a summary
hlq <- QSep(hyperLOPIT2015)
hlq## Object of class 'QSep'.
## Data: hyperLOPIT2015
## With 14 sub-cellular clusters.levelPlot(hlq)
plot(hlq)
See Assessing sub-cellular resolution in spatial proteomics experiments (Gatto, Breckels, and Lilley 2018) for details, including a large meta-analysis on 29 different spatial proteomics experiments.
5 Data analysis
Classification of proteins, i.e. assigning sub-cellular localisation to proteins, is the main aspect of the present data analysis. The principle is the following and is, in its basic form, a 2 step process. First, an algorithm learns from the known markers that are shown to him and models the data space accordingly. This phase is also called the training phase. In the second phase, un-labelled proteins, i.e. those that have not been labelled as resident of any organelle, are matched to the model and assigned to a group (an organelle). This 2 step process is called machine learning (ML), because the computer (machine) learns by itself how to recognise instances that possess certain characteristics and classifies them without human intervention. That does however not mean that results can be trusted blindly.
In the above paragraph, we have defined what is called supervised ML,
because the algorithm is presented with some know instances from which
it learns (see section 5.2). Alternatively, un-supervised
ML does not make any assumptions about the group memberships, and uses
the structure of the data itself to defined sub-groups (see section
5.1). It is of course possible to classify data based on
labelled and unlabelled data. This extension of the supervised
classification problem described above is called semi-supervised
learning. In this case, the training data consists of both labelled
and unlabelled instances with the obvious goal of generating a better
classifier than would be possible with the labelled data only. The
phenoDisco algorithm, will be illustrated in that context (section
5.4).
5.1 Unsupervised ML
The plot2D can also be used to visualise the data on a PCA plot
omitting any marker definitions, as shown on figure
8. This approach avoids any bias towards marker
definitions and concentrate on the data and its underlying structure
itself.
plot2D(tan2009r1, fcol = NULL)
Figure 8: Plain PCA representation of the tan2009r1 data
Alternatively, pRoloc also gives access to
MLInterfaces’s MLean unified interface for, among
others, unsupervised approaches using k-means (figure
9), hierarchical (figure 10) or
partitioning around medoids (figure 11), clustering.
kcl <- MLearn( ~ ., tan2009r1, kmeansI, centers=5)
plot(kcl, exprs(tan2009r1))
Figure 9: k-means clustering on the tan2009r1 data
hcl <- MLearn( ~ ., tan2009r1,
hclustI(distFun = dist,
cutParm = list(k = 5)))
plot(hcl, labels = FALSE)
Figure 10: Hierarchical clustering on the tan2009r1 data
pcl <- MLearn( ~ ., tan2009r1, pamI(dist), k = 5)
plot(pcl, data = exprs(tan2009r1))