%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{A transfer learning algorithm for spatial proteomics}
%\VignetteKeywords{Bioinformatics, Machine learning, Organelle, Spatial Proteomics}
%\VignettePackage{pRoloc}

\documentclass[12pt, oneside]{article}

<<style, eval=TRUE, echo=FALSE, results="asis">>=
BiocStyle::latex()
@


\author{
  Lisa M. Breckels and Laurent Gatto\footnote{\email{lg390@cam.ac.uk}}\\
  Computational Proteomics Unit\\
  University of Cambridge, UK
}


\bioctitle[\Biocpkg{pRoloc} transfer learning]{A transfer learning
  algorithm for spatial proteomics}

\begin{document}

\maketitle

%% Abstract and keywords %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\vskip 0.3in minus 0.1in
\hrule
\begin{abstract}
  This vignette illustrates the application of a \emph{transfer
    learning} algorithm to assign proteins to sub-cellular
  localisations. The \emph{knntlClassification} algorithm combines
  \emph{primary} experimental spatial proteomics data (LOPIT, PCP,
  etc.)  and an \emph{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.
\end{abstract}
\textit{Keywords}: Bioinformatics, organelle, spatial proteomics,
machine learning, transfer learning 

\vskip 0.1in minus 0.05in \hrule \vskip 0.2in minus 0.1in
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\newpage

\tableofcontents

\newpage

<<env, include=FALSE, echo=FALSE, cache=FALSE>>=
library("knitr")
opts_chunk$set(fig.align = 'center', 
               fig.show = 'hold', 
               par = TRUE,
               prompt = TRUE,
               eval = TRUE,
               stop_on_error = 1L,
               comment = NA)
options(replace.assign = TRUE, 
        width = 55)
suppressPackageStartupMessages(library("dplyr"))
suppressPackageStartupMessages(library("MSnbase"))
suppressWarnings(suppressPackageStartupMessages(library("pRoloc")))
suppressPackageStartupMessages(library("pRolocdata"))
suppressPackageStartupMessages(library("class"))
set.seed(1)
@ 
%%$

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Section
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\section{Introduction}\label{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 \cite{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
\cite{Breckels:2015}. Supervised and semi-supervised machine learning
algorithms can be applied to assign thousands of proteins to annotated
sub-cellular niches \cite{Breckels2013,Gatto:2014} (see also the
\textit{pRoloc-tutorial} vignette). These data constitute our main
source for protein localisation and are termed thereafter
\emph{primary} data.

There are other sources of data about sub-cellular localisation of
proteins, such as the Gene Ontology \cite{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 \cite{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 \emph{auxiliary}
information.

The aim of a \emph{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 \Biocpkg{pRoloc} package, as
described in  

\begin{quote}
<<ref, echo=FALSE, results='asis'>>=
cits <- citation("pRoloc")
i <- which(lengths(sapply(cits, function(xx) grep("heterogeneous", xx$title))) > 0)
print(cits[[i]], style = "LaTeX")
@
\end{quote}

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}.

<<loadpkg>>=
library("pRoloc")
@

\section{Preparing the auxiliary data}\label{sec:aux}

\subsection{The Gene Ontology}\label{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
\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 \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:

\begin{enumerate}
\item define the annotation parameters based on the species and
  feature types;
\item query the resource defined in (1) to retrieve relevant terms and
  use the terms to prepare the auxiliary data.
\end{enumerate}

We will demonstrate these steps using a LOPIT experiment on Human
Embryonic Kidney (HEK293T) fibroblast cells \cite{Breckels2013},
available and documented in the \Biocexptpkg{pRolocdata} experiment
package as \Robject{andy2011}.

<<loaddata>>=
library("pRolocdata")
data(andy2011)
@

\subsubsection{Preparing the query parameters}\label{sec:ap}

The query parameters are stored as \Rclass{AnnotationParams} objects
that are created with the \Rfunction{setAnnotationParams}
function. The function will present a first menu with
\Sexpr{nrow(pRoloc:::getMartTab())}. Once the species has been
selected, a set of possible identifier types is displayed.

\begin{figure}[h]
  \centering
  \includegraphics[height=5cm]{./Figures/ap1.png}\hspace{1cm}
  \includegraphics[height=5cm]{./Figures/ap2.png}
  \caption{Selecting species (left) and feature type (right) to create
    an \Robject{AnnotationParams} instance for the human
    \Robject{andy2011} data.}
  \label{fig:apgui}
\end{figure}

It is also possible to pass patterns\footnote{These patterns must
  match uniquely or an error will be thrown.} to match against the
species (\texttt{"Homo sapiens"}) and feature type
(\texttt{"UniProt/Swissprot ID"}).

<<ap>>=
ap <- setAnnotationParams(inputs =
                              c("Homo sapiens",
                                "UniProt/Swissprot Accession"))
ap
@

The \Rfunction{setAnnotationParams} function automatically sets the
annotation parameters globally so that the \Robject{ap} object does
not need to be explicitly set later on. The default parameters can be
retrieved with \Rfunction{getAnnotationParams}.

\subsubsection{Preparing the auxiliary data from the GO ontology}\label{sec:auxgo}

The feature names of the \Robject{andy2011} data are UniProt
identifiers, as defined in the \Robject{ap} accession parameters.

<<pdata>>=
data(andy2011)
head(featureNames(andy2011))
@

The \Rfunction{makeGoSet} function takes an \Rclass{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 \Robject{MSnSet}. By
default, it downloads \textit{cellular component} terms and does not
do any filtering on the terms evidence codes (see the
\Rfunction{makeGoSet} manual for details). Unless passed as argument,
the default, globally set \Rclass{AnnotationParams} are used to define
the Biomart server and the query\footnote{The annotation parameters
  could also be passed explicitly through the \Robject{params}
  argument.}.

<<andgoset>>=
andygoset <- makeGoSet(andy2011)
andygoset
exprs(andygoset)[1:7, 1:4]
@

<<testandsamefeats, echo=FALSE>>=
stopifnot(all.equal(featureNames(andy2011), featureNames(andygoset)))
@

We now have a primary data set, composed of \Sexpr{nrow(andy2011)}
protein quantitative profiles for \Sexpr{ncol(andy2011)} fractions
along the density gradient and an auxiliary data set for
\Sexpr{ncol(andygoset)} cellular compartment GO terms for the same
\Sexpr{nrow(andygoset)} features.

\subsubsection{A note on reproducibility}\label{sec:annotrepro}

The generation of the auxiliary data relies on specific Biomart
server \Rclass{Mart} instances in the \Rclass{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
\Rclass{AnnotationParams} and auxiliary \Rclass{MSnSet}
instances. Alternatively, it is possible to use other Bioconductor
infrastructure, such as specific organism annotations and the
\Biocannopkg{GO.db} package to use specific versioned (and thus
traceable) annotations.

\subsection{The Human Protein Atlas}\label{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\footnote{This section has been updated on the 30th
  November to reflect changes in Biomart.}.

<<hparprep, eval=TRUE>>=
fvarLabels(andy2011)[1] <- "accession" ## for left_join matching
## convert protein accession numbers to ensembl gene identifiers

library("biomaRt")
mart <- useMart("ensembl",dataset="hsapiens_gene_ensembl")

filter <- "uniprot_swissprot"
attrib <- c("uniprot_genename", "uniprot_swissprot", "ensembl_gene_id")
bm <- getBM(attributes = attrib,
            filters = filter,
            values = fData(andy2011)[, "accession"],
            mart = mart)
head(bm)

## HPA data
library("hpar")
getHpaVersion()
getHpaDate()

setHparOptions(hpadata = "SubcellularLoc")
hpa <- getHpa(bm$ensembl_gene_id)
hpa$Reliability <- droplevels(hpa$Reliability)
colnames(hpa)[1] <- "ensembl_gene_id"

library("dplyr")
hpa <- left_join(hpa, bm)
hpa <- hpa[!duplicated(hpa$uniprot_swissprot), ]

## match HPA/LOPIT
colnames(hpa)[7] <- "accession"
fd <- left_join(fData(andy2011), hpa)
rownames(fd) <- featureNames(andy2011)
fData(andy2011) <- fd
stopifnot(validObject(andy2011))

## Let's get rid of features without any hpa data
lopit <- andy2011[!is.na(fData(andy2011)$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 \emph{Supportive}
are considered; \emph{Uncertain} assignments are ignored - see
\url{http://www.proteinatlas.org/about/quality+scoring#ifr} for
details) and filter proteins without any localisation data.

<<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(lopit))
}

hpaset <- makeHpaSet(lopit,
                     score2 = c(Supportive = 1, Uncertain = 0))
hpaset <- filterZeroRows(hpaset)
dim(hpaset)
exprs(hpaset)[c(1, 6, 200), 1:3]
@

\subsection{Protein-protein interactions}\label{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: 

\begin{itemize}

\item The \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.
  
\item The \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 \texttt{STRINGdb} vignette and
  involves mapping the protein identifiers with the \Rfunction{map}
  and retrieve the interaction partners with the
  \Rfunction{get\_neighbors} method.
  
\item 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.
\end{itemize}


Below, we access the PPI spreadsheet file for our test data, that is
distributed with the \Biocexptpkg{pRolocdata} package.

<<tabdelim>>=
ppif <- system.file("extdata/tabdelimited._gHentss2F9k.txt.gz", package = "pRolocdata")
ppidf <- read.delim(ppif, header = TRUE, stringsAsFactors = FALSE)
head(ppidf)
@

The file contains \Sexpr{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.

<<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
\Sexpr{sum(ppim != 0)} interactions between \Sexpr{nrow(ppim)}
proteins. Below, we only keep proteins that are also available in our
spatial proteomics data, subset the PPI and LOPIT data, create the
appropriate \Robject{MSnSet} object, and filter out proteins without
any interaction scores.

<<ppiset2>>=
featureNames(andy2011) <- sub("_HUMAN", "", fData(andy2011)$UniProtKB.entry.name)
cmn <- intersect(featureNames(andy2011), rownames(ppim))
ppim <- ppim[cmn, ]
andy2011 <- andy2011[cmn, ]

ppi <- MSnSet(ppim, fData = fData(andy2011),
              pData = data.frame(row.names = colnames(ppim)))
ppi <- filterZeroCols(ppi)
@

We now have two \Robject{MSnSet} objects containing respectively
\Sexpr{nrow(andy2011)} primary experimental protein profiles along a
sub-cellular density gradient (\texttt{andy2001}) and
\Sexpr{nrow(ppi)} auxiliary interaction profiles (\texttt{ppi}).

\section{Support vector machine transfer learning}\label{sec:svmtl}

The SVM-TL method descibed in \cite{Breckels:2015} has not yet been
incorporated in the \Biocpkg{pRoloc} package. The code implementing
the method is currently available in its own repository:

\indent \url{https://github.com/ComputationalProteomicsUnit/lpsvm-tl-code}

\section{Nearest neighbour transfer learning}\label{sec:knntl}

\subsection{Optimal weights}\label{sec:theopt}

<<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 \Rfunction{knntlOptimisation}
function. For instance, for the human data modelled by the
\Robject{andy2011} and \Robject{andygoset} objects\footnote{We will
  use the sub-cellular markers defined in the \texttt{markers.tl}
  feature variable, instead of the default \texttt{markers}. } and the
\Sexpr{length(m)} annotated sub-cellular localisations
(\Sexpr{paste(m[-1], collapse = ", ")} and \Sexpr{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 \Sexpr{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.

\begin{figure}[h]
  \centering
<<andypca, fig.width=6, fig.height=6, echo=FALSE>>=
setStockcol(paste0(getStockcol(), "80"))
plot2D(andy2011, fcol = "markers.tl")
setStockcol(NULL)
addLegend(andy2011, fcol = "markers.tl",
          where = "topright", bty = "n", cex = .7)
@  
 \caption{PCA plot of \Robject{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. }
  \label{fig:andypca}
\end{figure}

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 \Sexpr{seq(0, 1, by = 0.25)} or \Sexpr{round(seq(0, 1,
  length.out = 4), 2)}) to explore more fine-grained integration
opportunities. The possible weight combinations can be calculated with
the \Rfunction{thetas} function:

\begin{itemize}
\item 3 classes, 3 weights

<<thetas0, echo=TRUE>>=
head(thetas(3, by = 0.5))
dim(thetas(3, by = 0.5))
@

\item 5 classes, 4 weights

<<thetas1, echo=TRUE>>=
dim(thetas(5, length.out = 4))
@

\item for the human \Robject{andy2011} data, considering 4 weights,
there are very many combinations:

<<thetaandy>>=
## marker classes for andy2011
m <- unique(fData(andy2011)$markers.tl)
m <- m[m != "unknown"]
th <- thetas(length(m), length.out=4)
dim(th)
@
\end{itemize}

The actual combination of weights to be tested can be defined in
multiple ways: by passing a weights matrix explicitly (as those
generated with \Rfunction{thetas} above) through the \Robject{th}
argument; or by defining the increment between weights using
\Robject{by}; or by specifying the number of weights to be used
through the \Robject{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 \Rfunction{knnOptimisation} function (see the
\textit{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
\Rfunction{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 \Rfunction{combineThetaRegRes} function before
proceeding with the selection of best weights.

<<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
\Rfunction{knntlOptimisation} function supports parallelised execution
using various backends thanks to the \Biocpkg{BiocParallel} package;
an appropriate backend will be defined automatically according to the
underlying architecture and user-defined backends can be defined
through the \Robject{BPPARAM} argument\footnote{Large scale
  applications of this algorithms (\fixme{add ref}) were run on a
  cluster using an MPI backend defined with
  \Rfunction{SnowParams(256, type="MPI")}.}. Also, in the interest of
time, the weights optimisation is repeated only 5 times below.

<<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
\texttt{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 \Robject{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 figure \ref{fig:bubble}. These
\textit{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.

\begin{figure}[h]
  \centering
  \includegraphics[width=.7\linewidth]{./Figures/bubble-andy.pdf}
  \caption{Results obtained from an extensive optimisation on the
    primary \Robject{andy2011} and auxiliary \Robject{andygoset} data
    sets, as produced by \Rfunction{plot(topt)}. This figure is not
    the result for the previous code chunk, where only a random subset
    of 10 candidate weights have been tested. }
  \label{fig:bubble}
\end{figure}


\subsection{Choosing weights}\label{sec:choosep}

A set of best weights must be chosen and applied to the classification
of the unlabelled proteins (formally annotated as
\texttt{unknown}). These can be defined manually, based on the pattern
observed in the weights \textit{bubble} plot (figure
\ref{fig:bubble}), or automatically extracted with the
\Rfunction{getParams} method\footnote{Note that the scores extracted
  here are based on the random subsest of weights.}. See
\Rfunction{?getParams} for details and the \Rfunction{favourPrimary}
function, if it is desirable to systematically favour the primary data
(i.e. high weights) when different weight combinations perform equally
well.

<<getParam>>=
getParams(topt)
@

We provide the best parameters for the extensive parameter
optimisation search, as provided by \Rfunction{getParams}:

<<besttheta>>=
(bw <- experimentData(andy2011)@other$knntl$thetas)
@

\subsection{Applying best \textit{theta} weights}\label{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 \texttt{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 \texttt{markers}) to the
\Rfunction{knntlClassification} function.

<<tlclass>>=
andy2011 <- knntlClassification(andy2011, andygoset,
                                bestTheta = bw,
                                k = c(3, 3),
                                fcol = "markers.tl")
@

This will generate a new instance of class \Rclass{MSnSet},
identical to the primary data, including the classification results
and classifications scores of the transfer learning classification
algorithm (as \texttt{knntl} and \texttt{knntl.scores} feature
variables respectively). Below, we extract the former with the
\Rfunction{getPrediction} function and plot the results of the
classification.

<<tlpreds>>=
andy2011 <- getPredictions(andy2011, fcol = "knntl")
@

\begin{figure}[h]
  \centering
<<andypca2, fig.width=6, fig.height=6>>=
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)
@  
 \caption{PCA plot of \Robject{andy2011} after transfer learning
   classification. The size of the points is proportional to the
   classification scores. }
  \label{fig:andypca2}
\end{figure}

Please read the \textit{pRoloc-tutorial} vignette, and in particular
the classification section, for more details on how to proceed with
exploration the classification results and classification scores.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Section
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\section{Conclusions}\label{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 \cite{Breckels:2015} on third-party quantitative auxiliary
data.

\section*{Session information}\label{sec:sessionInfo} 

All software and respective versions used to produce this document are
listed below.

<<sessioninfo, results='asis', echo=FALSE>>=
toLatex(sessionInfo())
@

\bibliography{pRoloc}

\end{document}