---
title: "A transfer learning algorithm for spatial proteomics"
author:
- name: Lisa M. Breckels
affiliation: Computational Proteomics Unit, Cambridge, UK
- name: Laurent Gatto
affiliation: de Duve Institute, UCLouvain, Belgium
package: pRoloc
abstract: >
This vignette illustrates the application of a *transfer learning*
algorithm to assign proteins to sub-cellular localisations. The
*knntlClassification* algorithm combines *primary* experimental
spatial proteomics data (LOPIT, PCP, etc.) and an *auxiliary* data
set (for example binary data based on Gene Ontology terms) to
improve the sub-cellular assignment given an optimal combination of
these data sources.
output:
BiocStyle::html_document:
toc_float: true
bibliography: pRoloc.bib
vignette: >
%\VignetteIndexEntry{A transfer learning algorithm for spatial proteomics}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteKeywords{Bioinformatics, Machine learning, Organelle, Spatial Proteomics}
%\VignetteEncoding{UTF-8}
---
```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```
```{r env, include=FALSE, echo=FALSE, cache=FALSE}
library("knitr")
opts_chunk$set(stop_on_error = 1L)
suppressPackageStartupMessages(library("MSnbase"))
suppressWarnings(suppressPackageStartupMessages(library("pRoloc")))
suppressPackageStartupMessages(library("pRolocdata"))
suppressPackageStartupMessages(library("class"))
set.seed(1)
setStockcol(NULL)
```
# Introduction {#sec:intro}
Our main data source to study protein sub-cellular localisation are
high-throughput mass spectrometry-based experiments such as LOPIT, PCP
and similar designs (see [@Gatto2010] for an general
introduction). Recent optimised experiments result in high quality
data enabling the identification of over 6000 proteins and
discriminate numerous sub-cellular and sub-organellar niches
[@Christoforou:2016]. Supervised and semi-supervised machine learning
algorithms can be applied to assign thousands of proteins to annotated
sub-cellular niches [@Breckels2013,Gatto:2014] (see also the
*pRoloc-tutorial* vignette). These data constitute our main
source for protein localisation and are termed thereafter
*primary* data.
There are other sources of data about sub-cellular localisation of
proteins, such as the Gene Ontology [@Ashburner:2000] (in
particular the cellular compartment name space), quantitative features
derived from protein sequences (such as pseudo amino acid composition)
or the Human Protein Atlas [@Uhlen:2010] to cite a few. These
data, while not optimised to a specific system at hand and, in the
case of annotation feature, not as reliable as our experimental data,
constitute an invaluable, often plentiful source of *auxiliary*
information.
The aim of a *transfer learning* algorithm is to combine
different sources of data to improve overall classification. In
particular, the goal is to support/complement the primary target
domain (experimental data) with auxiliary data (annotation) features
without compromising the integrity of our primary data. In this
vignette, we describe the application of transfer learning algorithms
for the localisation of proteins from the `r Biocpkg("pRoloc")` package, as
described in
> 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](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004920).
Two algorithms were developed: a transfer learning algorithm based on
the $k$-nearest neighbour classifier, coined kNN-TL hereafter,
described in section \@ref(sec:knntl), and one based on the support
vector machine algorithm, termed SVM-TL, described in section
\@ref(sec:svmtl).
```{r loadpkg}
library("pRoloc")
```
# Preparing the auxiliary data {#sec:aux}
## The Gene Ontology {#sec:goaux}
The auxiliary data is prepared from the primary data's features. All
the GO terms associated to these features are retrieved and used to
create a binary matrix where a one (zero) at position $(i,j)$
indicates that term $j$ has (not) been used to annotate feature $i$.
The GO terms are retrieved from an appropriate repository using the
`r Biocpkg("biomaRt")` package. The specific Biomart repository and query
will depend on the species under study and the type of features. The
first step is to prepare annotation parameters that will enable to
perform the query. The `r Biocpkg("pRoloc")` package provides a dedicated
infrastructure to set up the query to the annotation resource and
prepare the GO data for subsequent analyses. This infrastructure is
composed of:
1. define the annotation parameters based on the species and feature
types;
2. query the resource defined in (1) to retrieve relevant terms and
use the terms to prepare the auxiliary data.
We will demonstrate these steps using a LOPIT experiment on Human
Embryonic Kidney (HEK293T) fibroblast cells [@Breckels2013],
available and documented in the `r Biocexptpkg("pRolocdata")` experiment
package as `andy2011`.
```{r loaddata}
library("pRolocdata")
data(andy2011)
```
### Preparing the query parameters {#sec:ap}
The query parameters are stored as *AnnotationParams* objects that are
created with the *setAnnotationParams* function. The function will
present a first menu with `r nrow(pRoloc:::getMartTab())`. Once the
species has been selected, a set of possible identifier types is
displayed.
{#fig:apgui}
It is also possible to pass patterns\footnote{These patterns must
match uniquely or an error will be thrown.} to match against the
species (`"Human genes"`) and feature type (`"UniProtKB/Swiss-prot ID"`).
```{r ap}
ap <- setAnnotationParams(inputs =
c("Human genes",
"UniProtKB/Swiss-Prot ID"))
ap
```
The *setAnnotationParams* function automatically sets the annotation
parameters globally so that the `ap` object does not need to be
explicitly set later on. The default parameters can be retrieved with
*getAnnotationParams*.
### Preparing the auxiliary data from the GO ontology {#sec:auxgo}
The feature names of the `andy2011` data are UniProt identifiers, as
defined in the `ap` accession parameters.
```{r pdata}
data(andy2011)
head(featureNames(andy2011))
```
The *makeGoSet* function takes an *MSnSet* class (from which the
feature names will be extracted) or, directly a vector of characters
containing the feature names of interest to retrieve the associated GO
terms and construct an auxiliary `MSnSet`. By default, it downloads
*cellular component* terms and does not do any filtering on the terms
evidence codes (see the *makeGoSet* manual for details). Unless passed
as argument, the default, globally set *AnnotationParams* are used to
define the Biomart server and the query\footnote{The annotation
parameters could also be passed explicitly through the `params`
argument.}.
```{r andgoset}
andygoset <- makeGoSet(andy2011)
andygoset
exprs(andygoset)[1:7, 1:4]
```
```{r testandsamefeats, echo=FALSE}
stopifnot(all.equal(featureNames(andy2011), featureNames(andygoset)))
```
We now have a primary data set, composed of `r nrow(andy2011)` protein
quantitative profiles for `r ncol(andy2011)` fractions along the
density gradient and an auxiliary data set for `r ncol(andygoset)`
cellular compartment GO terms for the same `r nrow(andygoset)`
features.
### A note on reproducibility {#sec:annotrepro}
The generation of the auxiliary data relies on specific Biomart server
*Mart* instances in the *AnnotationParams* class and the actual query
to the server to obtain the GO terms associated with the features. The
utilisation of online servers, which undergo regular updates, does not
guarantee reproducibility of feature/term association over time. It is
recommended to save and store the *AnnotationParams* and auxiliary
*MSnSet* instances. Alternatively, it is possible to use other
Bioconductor infrastructure, such as specific organism annotations and
the `r Biocannopkg("GO.db")` package to use specific versioned (and
thus traceable) annotations.
## The Human Protein Atlas {#sec:hpaaux}
The feature names of our LOPIT experiment are UniProt identifiers,
while the Human Protein Atlas uses Ensembl gene identifiers. This
first code chunk matches both identifier types using the Ensembl
Biomart server and `left_join` from the `r CRANpkg("dplyr")`
package. In this section, we copy the experimental data to `andyhpa`.
```{r hparprep, eval=TRUE}
andyhpa <- andy2011
fvarLabels(andyhpa)[1] <- "accession" ## for left_join matching
## convert protein accession numbers to ensembl gene identifiers
library("biomaRt")
mart <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
filter <- "uniprotswissprot"
attrib <- c("uniprot_gn_symbol", "uniprotswissprot", "ensembl_gene_id")
bm <- getBM(attributes = attrib,
filters = filter,
values = fData(andyhpa)[, "accession"],
mart = mart)
head(bm)
## HPA data
library("hpar")
## using old version for traceability
setHparOptions(hpadata = "hpaSubcellularLoc14")
hpa <- getHpa(bm$ensembl_gene_id)
hpa$Reliability <- droplevels(hpa$Reliability)
colnames(hpa)[1] <- "ensembl_gene_id"
hpa <- dplyr::left_join(hpa, bm)
hpa <- hpa[!duplicated(hpa$uniprotswissprot), ]
## match HPA/LOPIT
colnames(hpa)[7] <- "accession"
fd <- dplyr::left_join(fData(andyhpa), hpa)
rownames(fd) <- featureNames(andyhpa)
fData(andyhpa) <- fd
stopifnot(validObject(andyhpa))
## Let's get rid of features without any hpa data
lopit <- andyhpa[!is.na(fData(andyhpa)$Main.location), ]
```
Below, we deparse the multiple ';'-delimited locations contained in
the Human Protein sub-cellular Atlas, create the auxiliary binary data
matrix (only localisations with reliability equal to *Supportive* are
considered; *Uncertain* assignments are ignored - see
http://www.proteinatlas.org/about/antibody+validation for details) and
filter proteins without any localisation data.
```{r hpadata, eval=TRUE}
## HPA localisation
hpalocs <- c(as.character(fData(lopit)$Main.location),
as.character(fData(lopit)$Other.location))
hpalocs <- hpalocs[!is.na(hpalocs)]
hpalocs <- unique(unlist(strsplit(hpalocs, ";")))
makeHpaSet <- function(x, score2, locs = hpalocs) {
hpamat <- matrix(0, ncol = length(locs), nrow = nrow(x))
colnames(hpamat) <- locs
rownames(hpamat) <- featureNames(x)
for (i in 1:nrow(hpamat)) {
loc <- unlist(strsplit(as.character(fData(x)[i, "Main.location"]), ";"))
loc2 <- unlist(strsplit(as.character(fData(x)[i, "Other.location"]), ";"))
score <- score2[fData(x)[i, "Reliability"]]
hpamat[i, loc] <- score
hpamat[i, loc2] <- score
}
new("MSnSet", exprs = hpamat,
featureData = featureData(x))
}
hpaset <- makeHpaSet(lopit,
score2 = c(Supportive = 1, Uncertain = 0))
hpaset <- filterZeroRows(hpaset)
dim(hpaset)
exprs(hpaset)[c(1, 6, 200), 1:3]
```
## Protein-protein interactions {#sec:ppi}
Protein-protein interaction data can also be used as auxiliary data
input to the transfer learning algorithm. Several sources can be used
to do so directly from R:
* The `r Biocpkg("PSICQUIC")` package provides an R interfaces to the
HUPO Proteomics Standard Initiative (HUPO-PSI) project, which
standardises programmatic access to molecular interaction
databases. This approach enables to query great many resources in
one go but, as noted in the vignettes, for bulk interactions, it is
recommended to directly download databases from individual PSICQUIC
providers.
* The `r Biocpkg("STRINGdb")` package provides a direct interface to
the STRING protein-protein interactions database. This package can
be used to generate a table as the one used below. The exact
procedure is described in the `STRINGdb` vignette and involves
mapping the protein identifiers with the *map* and retrieve the
interaction partners with the *get_neighbors* method.
* Finally, it is possible to use any third-party PPI inference results
and adequately prepare these results for transfer learning. Below,
we will described this case with PPI data in a tab-delimited format,
as retrieved directly from the STRING database.
Below, we access the PPI spreadsheet file for our test data, that is
distributed with the `r Biocexptpkg("pRolocdata")` package.
```{r tabdelim}
ppif <- system.file("extdata/tabdelimited._gHentss2F9k.txt.gz", package = "pRolocdata")
ppidf <- read.delim(ppif, header = TRUE, stringsAsFactors = FALSE)
head(ppidf)
```
The file contains `r nrow(ppidf)` pairwise interactions and the STRING
combined interaction score. Below, we create a contingency matrix that
uses these scores to encode and weight interactions.
```{r ppiset}
uid <- unique(c(ppidf$X.node1, ppidf$node2))
ppim <- diag(length(uid))
colnames(ppim) <- rownames(ppim) <- uid
for (k in 1:nrow(ppidf)) {
i <- ppidf[[k, "X.node1"]]
j <- ppidf[[k, "node2"]]
ppim[i, j] <- ppidf[[k, "combined_score"]]
}
ppim[1:5, 1:8]
```
We now have a contingency matrix reflecting a total of
`r sum(ppim != 0)` interactions between `r nrow(ppim)`
proteins. Below, we only keep proteins that are also available in our
spatial proteomics data (renamed to `andyppi`), subset the PPI and
LOPIT data, create the appropriate `MSnSet` object, and filter out
proteins without any interaction scores.
```{r ppiset2}
andyppi <- andy2011
featureNames(andyppi) <- sub("_HUMAN", "", fData(andyppi)$UniProtKB.entry.name)
cmn <- intersect(featureNames(andyppi), rownames(ppim))
ppim <- ppim[cmn, ]
andyppi <- andyppi[cmn, ]
ppi <- MSnSet(ppim, fData = fData(andyppi),
pData = data.frame(row.names = colnames(ppim)))
ppi <- filterZeroCols(ppi)
```
We now have two `MSnSet` objects containing respectively
`r nrow(andyppi)` primary experimental protein profiles along a
sub-cellular density gradient (`andyppi`) and `r nrow(ppi)` auxiliary
interaction profiles (`ppi`).
# Support vector machine transfer learning {#sec:svmtl}
The SVM-TL method descibed in [@Breckels:2016] has not yet been
incorporated in the `r Biocpkg("pRoloc")` package. The code
implementing the method is currently available in its own repository:
https://github.com/ComputationalProteomicsUnit/lpsvm-tl-code
# Nearest neighbour transfer learning {#sec:knntl}
## Optimal weights {#sec:theopt}
```{r mclasses, echo=FALSE}
data(andy2011) ## load clean LOPIT data
## marker classes for andy2011
m <- unique(fData(andy2011)$markers.tl)
m <- m[m != "unknown"]
```
The weighted nearest neighbours transfer learning algorithm estimates
optimal weights for the different data sources and the spatial niches
described for the data at hand with the *knntlOptimisation*
function. For instance, for the human data modelled by the `andy2011`
and `andygoset`
objects^[We will use the sub-cellular markers defined in the `markers.tl` feature variable, instead of the default `markers`.]
and the `r length(m)` annotated sub-cellular localisations
(`r paste(m[-1], collapse = ", ")` and `r m[1]`), we want to know how
to optimally combine primary and auxiliary data. If we look at figure
\@ref(fig:andypca), that illustrates the experimental separation of
the `r length(m)` spatial classes on a principal component plot, we
see that some organelles such as the mitochondrion or the cytosol and
cytosol/nucleus are well resolved, while others such as the Golgi or
the ER are less so. In this experiment, the former classes are not
expected to benefit from another data source, while the latter should
benefit from additional information.
```{r andypca, echo=FALSE, fig.cap = "PCA plot of `andy2011`. The multivariate protein profiles are summarised along the two first principal components. Proteins of unknown localisation are represented by empty grey points. Protein markers, which are well-known residents of specific sub-cellular niches are colour-coded and form clusters on the figure."}
setStockcol(paste0(getStockcol(), "80"))
plot2D(andy2011, fcol = "markers.tl")
setStockcol(NULL)
addLegend(andy2011, fcol = "markers.tl",
where = "topright", bty = "n", cex = .7)
```
Let's define a set of three possible weights: 0, 0.5 and 1. A weight
of 1 indicates that the final results rely exclusively on the
experimental data and ignore completely the auxiliary data. A weight
of 0 represents the opposite situation, where the primary data is
ignored and only the auxiliary data is considered. A weight of 0.5
indicates that each data source will contribute equally to the final
results. It is the algorithm's optimisation step task to identify the
optimal combination of class-specific weights for a given primary and
auxiliary data pair. The optimisation process can be quite time
consuming for many weights and many sub-cellular classes, as all
combinations (there are $number~of~classes^{number~of~weights}$
possibilities; see below). One would generally defined more weights
(for example `r seq(0, 1, by = 0.25)` or `r round(seq(0, 1, length.out = 4), 2)`)
to explore more fine-grained integration opportunities. The possible
weight combinations can be calculated with the *thetas* function:
* 3 classes, 3 weights
```{r thetas0, echo=TRUE}
head(thetas(3, by = 0.5))
dim(thetas(3, by = 0.5))
```
* 5 classes, 4 weights
```{r thetas1, echo=TRUE}
dim(thetas(5, length.out = 4))
```
* for the human `andy2011` data, considering 4 weights, there are very
many combinations:
```{r thetaandy}
## marker classes for andy2011
m <- unique(fData(andy2011)$markers.tl)
m <- m[m != "unknown"]
th <- thetas(length(m), length.out=4)
dim(th)
```
The actual combination of weights to be tested can be defined in
multiple ways: by passing a weights matrix explicitly (as those
generated with *thetas* above) through the `th` argument; or by
defining the increment between weights using `by`; or by specifying
the number of weights to be used through the `length.out` argument.
Considering the sub-cellular resolution for this experiment, we would
anticipate that the mitochondrion, the cytosol and the cytosol/nucleus
classes would get high weights, while the ER and Golgi would be
assigned lower weights.
As we use a nearest neighbour classifier, we also need to know how
many neighbours to consider when classifying a protein of unknown
localisation. The *knnOptimisation* function (see the
*pRoloc-tutorial* vignette and the functions manual page) can be run
on the primary and auxiliary data sources independently to estimate
the best $k_P$ and $k_A$ values. Here, based on *knnOptimisation*, we
use 3 and 3, for $k_P$ and $k_A$ respectively.
Finally, to assess the validity of the weight selection, it should be
repeated a certain number of times (default value is 50). As the
weight optimisation can become very time consuming for a wide range of
weights and many target classes, we would recommend to start with a
lower number of iterations, pre-analyse the results, proceed with
further iterations and eventually combine the optimisation results
data with the *combineThetaRegRes* function before
proceeding with the selection of best weights.
```{r thetaopt0, eval=FALSE}
topt <- knntlOptimisation(andy2011, andygoset,
th = th,
k = c(3, 3),
fcol = "markers.tl",
times = 50)
```
The above code chunk would take too much time to be executed in the
frame of this vignette. Below, we pass a very small subset of theta
matrix to minimise the computation time. The *knntlOptimisation*
function supports parallelised execution using various backends thanks
to the `r Biocpkg("BiocParallel")` package; an appropriate backend
will be defined automatically according to the underlying architecture
and user-defined backends can be defined through the `BPPARAM`
argument^[Large scale applications of this algorithms were run on a cluster using an MPI backend defined with `SnowParams(256, type="MPI")`.].
Also, in the interest of time, the weights optimisation is repeated
only 5 times below.
```{r thetaopt, eval=TRUE}
set.seed(1)
i <- sample(nrow(th), 12)
topt <- knntlOptimisation(andy2011, andygoset,
th = th[i, ],
k = c(3, 3),
fcol = "markers.tl",
times = 5)
topt
```
The optimisation is performed on the labelled marker examples
only. When removing unlabelled non-marker proteins (the `unknowns`),
some auxiliary GO columns end up containing only 0 (the GO-protein
association was only observed in non-marker proteins), which are
temporarily removed.
The `topt` result stores all the result from the optimisation step,
and in particular the observed theta weights, which can be directly
plotted as shown on the [bubble plot](#fig:bubble) below. These bubble
plots give the proportion of best weights for each marker class that
was observed during the optimisation phase. We see that the
mitochondrion, the cytosol and cytosol/nucleus classes predominantly
are scored with height weights (2/3 and 1), consistent with high
reliability of the primary data. The Golgi and the ribosomal clusters
(and to a lesser extend the ER) favour smaller scores, indicating a
substantial benefit of the auxiliary data.
{#fig:bubble}
## Choosing weights {#sec:choosep}
A set of best weights must be chosen and applied to the classification
of the unlabelled proteins (formally annotated as `unknown`). These
can be defined manually, based on the pattern observed in the weights
[bubble plot](#fig:bubble), or automatically extracted with the
*getParams*
method^[Note that the scores extracted here are based on the random subsest of weights.]. See
*?getParams* for details and the *favourPrimary* function, if it is
desirable to systematically favour the primary data (i.e. high
weights) when different weight combinations perform equally well.
```{r getParam}
getParams(topt)
```
We provide the best parameters for the extensive parameter
optimisation search, as provided by *getParams*:
```{r besttheta}
(bw <- experimentData(andy2011)@other$knntl$thetas)
```
## Applying best *theta* weights {#sec:thclass}
To apply our best weights and learn from the auxiliary data
accordingly when classifying the unlabelled proteins to one of the
sub-cellular niches considered in `markers.tl` (as displayed on figure
\@ref(fig:andypca)), we pass the primary and auxiliary data sets, best
weights, best k's (and, on our case the marker's feature variable we
want to use, default would be `markers`) to the *knntlClassification*
function.
```{r tlclass}
andy2011 <- knntlClassification(andy2011, andygoset,
bestTheta = bw,
k = c(3, 3),
fcol = "markers.tl")
```
This will generate a new instance of class *MSnSet*, identical to the
primary data, including the classification results and classifications
scores of the transfer learning classification algorithm (as `knntl`
and `knntl.scores` feature variables respectively). Below, we extract
the former with the *getPrediction* function and plot the results of
the classification.
```{r tlpreds}
andy2011 <- getPredictions(andy2011, fcol = "knntl")
```
```{r andypca2, fig.width=6, fig.height=6, fig.cap = "PCA plot of `andy2011` after transfer learning classification. The size of the points is proportional to the classification scores."}
setStockcol(paste0(getStockcol(), "80"))
ptsze <- exp(fData(andy2011)$knntl.scores) - 1
plot2D(andy2011, fcol = "knntl", cex = ptsze)
setStockcol(NULL)
addLegend(andy2011, where = "topright",
fcol = "markers.tl",
bty = "n", cex = .7)
```
Please read the *pRoloc-tutorial* vignette, and in particular the
classification section, for more details on how to proceed with
exploration the classification results and classification scores.
# Conclusions {#sec:ccl}
This vignette describes the application of a weighted $k$-nearest
neighbour transfer learning algorithm and its application to the
sub-cellular localisation prediction of proteins using quantitative
proteomics data as primary data and Gene Ontology-derived binary data
as auxiliary data source. The algorithm can be used with various data
sources (we show how to compile binary data from the Human Protein
Atlas in section \@ref(sec:hpaaux)) and have successfully applied the
algorithm [@Breckels:2016] on third-party quantitative auxiliary data.
# Session information {-}
All software and respective versions used to produce this document are
listed below.
```{r sessioninfo, echo=FALSE}
sessionInfo()
```
# References {-}