A transfer learning algorithm for spatial proteomics
Introduction
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 (Gatto et al. 2010) 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 et al. 2016). Supervised and semi-supervised machine learning algorithms can be applied to assign thousands of proteins to annotated sub-cellular niches (Lisa M. Breckels et al. 2013, 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 et al. 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 et al. 2010) to cite a few. These data, while not optimised to a specific system at hand and, in the case of annotation features, whilst 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 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.
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).
Preparing the data
To run the family of transfer learning algorithms in
pRoloc two datasets are required;
- A primary dataset, constructed as an
MSnSet, - an auxiliary dataset, constructed as an
MSnSet, and
also a set of common protein markers.
Example/test datasets are readily available in the pRolocdata package. We examine the structure of these datasets in the subsequent sections.
Primary data
We will not go into detail on how to construct a MSnSet
from protein correlation profiling data. We refer users to the main
proloc-tutorial vignette. We load a precomputed
MSnSet called andy2011. This dataset was a
proof-of-concept dataset from 2011, for the use of LOPIT with an
adherent mammalian cell culture. Human embryonic kidney fibroblast cells
(HEK293T) were used and LOPIT was employed with 8-plex iTRAQ reagents,
thus returning eight values per protein profile within a single
labelling experiment. Nuclei were discarded at an early stage in the
fractionation scheme as previously described, and membranes were not
carbonate washed in order to retain peripheral membrane and lumenal
proteins for analysis.
If we first look at the LOPIT data we see we have 1371 proteins and 8 columns of quantitation data.
## MSnSet (storageMode: lockedEnvironment)
## assayData: 1371 features, 8 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: X113 X114 ... X121 (8 total)
## varLabels: Fraction.information
## varMetadata: labelDescription
## featureData
## featureNames: O00767 P51648 ... O75312 (1371 total)
## fvarLabels: Accession.No. Protein.Description ...
## UniProtKB.entry.name (10 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:
## - - - Processing information - - -
## Loaded on Fri Sep 23 15:43:47 2016.
## Normalised to sum of intensities.
## Added markers from 'mrk' marker vector. Fri Sep 23 15:43:47 2016
## MSnbase version: 1.99.2The quantitation information is stored in the exprs
slot.
## X113 X114 X115 X116 X117 X118
## O00767 0.13605472 0.1495961 0.10623931 0.1465050 0.2773137 0.14294025
## P51648 0.19144560 0.2052463 0.05661169 0.1651138 0.2366302 0.09964387
## Q2TAA5 0.12970671 0.2014544 0.05456427 0.1460297 0.2923010 0.14634820
## Q9UKV5 0.09393056 0.2069976 0.04186618 0.2036268 0.3437386 0.10984031
## Q12797 0.17085535 0.1920020 0.00000000 0.1561016 0.2399472 0.11714296
## P16615 0.15532160 0.2236944 0.04018941 0.1723041 0.2820158 0.11350162
## X119 X121
## O00767 0.03796970 0.003381233
## P51648 0.01803788 0.027270640
## Q2TAA5 0.02057452 0.009021234
## Q9UKV5 0.00000000 0.000000000
## Q12797 0.00000000 0.123950875
## P16615 0.01297306 0.000000000The protein markers are found in the "markers" column of
the fData.
## organelleMarkers
## Cytosol Cytosol/Nucleus ER Golgi Lysosome
## 60 22 36 24 22
## Mitochondrion Nucleus PM Ribosome 40S Ribosome 60S
## 89 27 54 18 29
## unknown
## 990Auxiliary data
Auxiliary data can be generated from a number of data sources and we
give a few examples of some common sources in the proceeding sections.
Whatever source of information is used the data must be constructed as
an MSnSet to use with the knntl
algorithms.
The Gene Ontology
Gene Ontology (GO) terms can be used as a auxiliary source of information (providing they were not used to inform the proteins markers annotation). In Breckels et al 2016 the auxiliary data was prepared from the primary data’s features i.e. the proteins in the dataset. All the GO terms associated to these proteins 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 \(i\).
GO terms can be retrieved from an appropriate repository, for
example, using the r Biocpkg("biomaRt") package. The
specific Biomart repository and query will depend on the species under
study and the type of features. See, the vignettes/documentation
section of the R Bioconductor biomaRt package.
The pRolocdata
package contains five example Gene Ontology auxiliary datasets that were
used in (L. M. Breckels et al. 2016). In
the below code chunk the associated GO dataset to the
andy2011 dataset, this is called
andy2011goCC.
## MSnSet (storageMode: lockedEnvironment)
## assayData: 1371 features, 569 samples
## element names: exprs
## protocolData: none
## phenoData: none
## featureData
## featureNames: O00767 P51648 ... O75312 (1371 total)
## fvarLabels: Accession.No. Protein.Description ...
## UniProtKB.entry.name (10 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:
## - - - Processing information - - -
## Constructed GO set using cellular_component namespace: Mon Apr 29 12:24:10 2013
## MSnbase version: 1.9.1We see for the this GO set we have 1371 proteins and 569 columns. We pull just the first 10 rows and 10 columns below to show the binary structure of the data.
## [1] 1371 569## GO:0016021 GO:0005789 GO:0005783 GO:0005743 GO:0005792 GO:0005634
## O00767 1 1 1 0 0 0
## P51648 1 1 0 1 1 0
## Q2TAA5 1 1 0 0 0 0
## Q9UKV5 0 0 0 0 0 1
## Q12797 0 1 1 0 0 0
## P16615 0 1 0 0 1 0
## Q96SQ9 0 1 1 0 1 0
## Q16850 1 1 0 0 1 0
## P61803 1 1 0 0 0 0
## Q96HY6 0 0 1 0 0 0
## GO:0030176 GO:0043025 GO:0030426 GO:0030425
## O00767 0 0 0 0
## P51648 0 0 0 0
## Q2TAA5 0 0 0 0
## Q9UKV5 1 1 1 1
## Q12797 1 0 0 0
## P16615 0 0 0 0
## Q96SQ9 0 0 0 0
## Q16850 0 0 0 0
## P61803 0 0 0 0
## Q96HY6 0 0 0 0We note the we have the same proteins (and in the same order) in both datasets.
## [1] TRUE## [1] "O00767" "P51648" "Q2TAA5" "Q9UKV5" "Q12797" "P16615"## [1] "O00767" "P51648" "Q2TAA5" "Q9UKV5" "Q12797" "P16615"A note on reproducibility
The pulling of auxiliary data such as GO utilises online servers, which undergo regular updates, does not guarantee reproducibility of feature/term association over time. It is always recommended to save and store the data noting software version numbers and dates. In addition to the biomaRt package other Bioconductor infrastructure is available, such as specific organism annotations and the GO.db package to use specific versioned (and thus traceable) annotations.
The Human Protein Atlas
The Human Protein Atlas (HPA) provides another source of auxiliary
data for the prediction of a proteins location from correlation data.
The datasets andy2011hpa was constructed (with HPA, version
13, released on 11/06/2014) and we again load this directly from the
pRolocdata package.
## MSnSet (storageMode: lockedEnvironment)
## assayData: 1371 features, 8 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: X113 X114 ... X121 (8 total)
## varLabels: Fraction.information
## varMetadata: labelDescription
## featureData
## featureNames: O00767 P51648 ... O75312 (1371 total)
## fvarLabels: Accession.No. Protein.Description ...
## UniProtKB.entry.name (10 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:
## - - - Processing information - - -
## Loaded on Fri Sep 23 15:43:47 2016.
## Normalised to sum of intensities.
## Added markers from 'mrk' marker vector. Fri Sep 23 15:43:47 2016
## MSnbase version: 1.99.2Similar to the structure of the GO MSnSet, this
auxiliary data, was encoded as a binary matrix describing the
localisation of 670 proteins in 18 sub-cellular localisations.
Information for 192 of the 381 labelled marker proteins were
available.
Protein-protein interactions
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 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 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
STRINGdbvignette 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 pRolocdata package.
ppif <- system.file("extdata/tabdelimited._gHentss2F9k.txt.gz", package = "pRolocdata")
ppidf <- read.delim(ppif, header = TRUE, stringsAsFactors = FALSE)
head(ppidf)## X.node1 node2 node1_string_id node2_string_id node1_external_id
## 1 NUDT5 IMPDH2 1861432 1850365 ENSP00000419628
## 2 NOP2 RPL23 1858730 1858184 ENSP00000382392
## 3 HSPA4 ENO1 1848476 1843405 ENSP00000302961
## 4 RPS13 DKC1 1862013 1855055 ENSP00000435777
## 5 RPL35A DDOST 1859718 1856225 ENSP00000393393
## 6 RPL13A RPS6 1857955 1857216 ENSP00000375730
## node2_external_id neighborhood fusion cooccurence homology coexpression
## 1 ENSP00000321584 0.000 0 0 0 0.112
## 2 ENSP00000377865 0.000 0 0 0 0.064
## 3 ENSP00000234590 0.000 0 0 0 0.109
## 4 ENSP00000358563 0.462 0 0 0 0.202
## 5 ENSP00000364188 0.000 0 0 0 0.000
## 6 ENSP00000369757 0.000 0 0 0 0.931
## experimental knowledge textmining combined_score
## 1 0.000 0.0 0.370 0.416
## 2 0.868 0.0 0.000 0.871
## 3 0.222 0.0 0.436 0.575
## 4 0.000 0.0 0.354 0.698
## 5 0.000 0.9 0.265 0.923
## 6 0.419 0.9 0.240 0.996The file contains 18623 pairwise interactions and the STRING combined interaction score. Below, we create a contingency matrix that uses these scores to encode and weight interactions.
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]## NUDT5 NOP2 HSPA4 RPS13 RPL35A RPL13A CPS1 CTNNB1
## NUDT5 1 0 0 0 0.000 0.000 0 0
## NOP2 0 1 0 0 0.000 0.000 0 0
## HSPA4 0 0 1 0 0.000 0.000 0 0
## RPS13 0 0 0 1 0.997 0.998 0 0
## RPL35A 0 0 0 0 1.000 0.999 0 0We now have a contingency matrix reflecting a total of 19910
interactions between 1287 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.
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)## Removing 178 columns with only 0s.We now have two MSnSet objects containing respectively
520 primary experimental protein profiles along a sub-cellular density
gradient (andyppi) and 520 auxiliary interaction profiles
(ppi).
## MSnSet (storageMode: lockedEnvironment)
## assayData: 520 features, 8 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: X113 X114 ... X121 (8 total)
## varLabels: Fraction.information
## varMetadata: labelDescription
## featureData
## featureNames: ALG11 ASPH ... XYLT2 (520 total)
## fvarLabels: Accession.No. Protein.Description ...
## UniProtKB.entry.name (10 total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:
## - - - Processing information - - -
## Loaded on Fri Sep 23 15:43:47 2016.
## Normalised to sum of intensities.
## Added markers from 'mrk' marker vector. Fri Sep 23 15:43:47 2016
## Subset [1371,8][520,8] Tue Nov 11 16:53:38 2025
## MSnbase version: 1.99.2Support vector machine transfer learning
The SVM-TL method descibed in (L. M. Breckels et al. 2016) has not yet been incorporated in the pRoloc package. The code implementing the method is currently available in its own repository:
Nearest neighbour transfer learning
Optimal weights
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 objects1 and the 10 annotated
sub-cellular localisations (Golgi, Mitochondrion, PM, Lysosome, Cytosol,
Cytosol/Nucleus, Nucleus, Ribosome 60S, Ribosome 40S and ER), 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 10 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.
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.
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 0, 0.25, 0.5, 0.75, 1 or 0, 0.33, 0.67, 1) to explore more fine-grained integration opportunities. The possible weight combinations can be calculated with the thetas function:
- 3 classes, 3 weights
## Weigths:
## (0, 0.5, 1)## [,1] [,2] [,3]
## [1,] 0 0.0 0.0
## [2,] 0 0.0 0.5
## [3,] 0 0.0 1.0
## [4,] 0 0.5 0.0
## [5,] 0 0.5 0.5
## [6,] 0 0.5 1.0## Weigths:
## (0, 0.5, 1)## [1] 27 3- 5 classes, 4 weights
## Weigths:
## (0, 0.333333333333333, 0.666666666666667, 1)## [1] 1024 5- for the human
andy2011data, considering 4 weights, there are very many combinations:
## marker classes for andy2011
m <- unique(fData(andy2011)$markers.tl)
m <- m[m != "unknown"]
th <- thetas(length(m), length.out=4)## Weigths:
## (0, 0.333333333333333, 0.666666666666667, 1)## [1] 1048576 10The 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.
topt <- knntlOptimisation(andy2011, andy2011goCC,
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 BiocParallel
package; an appropriate backend will be defined automatically according
to the underlying architecture and user-defined backends can be defined
through the BPPARAM argument2. Also, in the interest
of time, the weights optimisation is repeated only 5 times below.
set.seed(1)
i <- sample(nrow(th), 12)
topt <- knntlOptimisation(andy2011, andy2011goCC,
th = th[i, ],
k = c(3, 3),
fcol = "markers.tl",
times = 5)## Removing 308 columns with only 0s.## Note: vector will be ordered according to classes: Cytosol Cytosol/Nucleus ER Golgi Lysosome Mitochondrion Nucleus PM Ribosome 40S Ribosome 60S (as names are not explicitly defined)## Object of class "ThetaRegRes"
## Algorithm: theta
## Theta hyper-parameters:
## weights: 0 0.3333333 0.6666667 1
## k: 3 3
## nrow: 12
## Design:
## Replication: 5 x 5-fold X-validation
## Partitioning: 0.2/0.8 (test/train)
## Results
## macro F1:
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6843 0.8132 0.8255 0.8061 0.8475 0.8603
## best theta:
## Cytosol Cytosol.Nucleus ER Golgi Lysosome Mitochondrion Nucleus PM
## weight:0 0 1 4 4 0 0 0 1
## weight:0.33 0 4 1 1 0 0 1 4
## weight:0.67 1 0 0 0 0 0 0 0
## weight:1 4 0 0 0 5 5 4 0
## Ribosome.40S Ribosome.60S
## weight:0 5 0
## weight:0.33 0 4
## weight:0.67 0 0
## weight:1 0 1The 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 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.
andy2011 and auxiliary
andygoset data sets, as produced by
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.Choosing weights
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,
or automatically extracted with the getParams method3. 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.
## Cytosol Cytosol/Nucleus ER Golgi Lysosome
## 1.0000000 0.3333333 0.0000000 0.0000000 1.0000000
## Mitochondrion Nucleus PM Ribosome 40S Ribosome 60S
## 1.0000000 1.0000000 0.3333333 0.0000000 0.3333333We provide the best parameters for the extensive parameter optimisation search, as provided by getParams:
## Cytosol Cytosol/Nucleus ER Golgi Lysosome
## 0.6666667 0.6666667 0.3333333 0.3333333 0.6666667
## Mitochondrion Nucleus PM Ribosome 40S Ribosome 60S
## 0.6666667 0.3333333 0.3333333 0.0000000 0.3333333Applying best theta weights
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.
andy2011 <- knntlClassification(andy2011, andy2011goCC,
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.
## ans
## Chromatin associated Cytosol Cytosol/Nucleus
## 11 265 67
## ER Endosome Golgi
## 193 12 69
## Lysosome Mitochondrion Nucleus
## 69 258 117
## PM Ribosome 40S Ribosome 60S
## 230 18 62setStockcol(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)
PCA plot of andy2011 after transfer learning
classification. The size of the points is proportional to the
classification scores.
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
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 (L. M. Breckels et al. 2016) on third-party quantitative auxiliary data.
Session information
All software and respective versions used to produce this document are listed below.
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
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## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
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## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] class_7.3-23 pRolocdata_1.48.0 pRoloc_1.50.0
## [4] BiocParallel_1.44.0 MLInterfaces_1.90.0 cluster_2.1.8.1
## [7] annotate_1.88.0 XML_3.99-0.20 AnnotationDbi_1.72.0
## [10] IRanges_2.44.0 MSnbase_2.36.0 ProtGenerics_1.42.0
## [13] S4Vectors_0.48.0 mzR_2.44.0 Rcpp_1.1.0
## [16] Biobase_2.70.0 BiocGenerics_0.56.0 generics_0.1.4
## [19] knitr_1.50 BiocStyle_2.38.0
##
## loaded via a namespace (and not attached):
## [1] splines_4.5.2 filelock_1.0.3
## [3] tibble_3.3.0 hardhat_1.4.2
## [5] preprocessCore_1.72.0 pROC_1.19.0.1
## [7] rpart_4.1.24 lifecycle_1.0.4
## [9] httr2_1.2.1 doParallel_1.0.17
## [11] globals_0.18.0 lattice_0.22-7
## [13] MASS_7.3-65 MultiAssayExperiment_1.36.0
## [15] dendextend_1.19.1 magrittr_2.0.4
## [17] limma_3.66.0 plotly_4.11.0
## [19] sass_0.4.10 rmarkdown_2.30
## [21] jquerylib_0.1.4 yaml_2.3.10
## [23] MsCoreUtils_1.21.0 DBI_1.2.3
## [25] buildtools_1.0.0 RColorBrewer_1.1-3
## [27] lubridate_1.9.4 abind_1.4-8
## [29] GenomicRanges_1.62.0 purrr_1.2.0
## [31] mixtools_2.0.0.1 AnnotationFilter_1.34.0
## [33] nnet_7.3-20 rappdirs_0.3.3
## [35] ipred_0.9-15 lava_1.8.2
## [37] listenv_0.10.0 gdata_3.0.1
## [39] maketools_1.3.2 parallelly_1.45.1
## [41] ncdf4_1.24 codetools_0.2-20
## [43] DelayedArray_0.36.0 tidyselect_1.2.1
## [45] Spectra_1.20.0 farver_2.1.2
## [47] viridis_0.6.5 matrixStats_1.5.0
## [49] BiocFileCache_3.0.0 Seqinfo_1.0.0
## [51] jsonlite_2.0.0 caret_7.0-1
## [53] e1071_1.7-16 survival_3.8-3
## [55] iterators_1.0.14 foreach_1.5.2
## [57] segmented_2.1-4 tools_4.5.2
## [59] progress_1.2.3 glue_1.8.0
## [61] prodlim_2025.04.28 gridExtra_2.3
## [63] SparseArray_1.10.1 BiocBaseUtils_1.12.0
## [65] xfun_0.54 MatrixGenerics_1.22.0
## [67] dplyr_1.1.4 withr_3.0.2
## [69] BiocManager_1.30.26 fastmap_1.2.0
## [71] digest_0.6.37 timechange_0.3.0
## [73] R6_2.6.1 colorspace_2.1-2
## [75] gtools_3.9.5 lpSolve_5.6.23
## [77] biomaRt_2.66.0 RSQLite_2.4.4
## [79] tidyr_1.3.1 hexbin_1.28.5
## [81] data.table_1.17.8 recipes_1.3.1
## [83] FNN_1.1.4.1 prettyunits_1.2.0
## [85] PSMatch_1.14.0 httr_1.4.7
## [87] htmlwidgets_1.6.4 S4Arrays_1.10.0
## [89] ModelMetrics_1.2.2.2 pkgconfig_2.0.3
## [91] gtable_0.3.6 timeDate_4051.111
## [93] blob_1.2.4 S7_0.2.0
## [95] impute_1.84.0 XVector_0.50.0
## [97] sys_3.4.3 htmltools_0.5.8.1
## [99] MALDIquant_1.22.3 clue_0.3-66
## [101] scales_1.4.0 png_0.1-8
## [103] gower_1.0.2 MetaboCoreUtils_1.18.0
## [105] reshape2_1.4.4 coda_0.19-4.1
## [107] nlme_3.1-168 curl_7.0.0
## [109] proxy_0.4-27 cachem_1.1.0
## [111] stringr_1.6.0 parallel_4.5.2
## [113] mzID_1.48.0 vsn_3.78.0
## [115] pillar_1.11.1 grid_4.5.2
## [117] vctrs_0.6.5 pcaMethods_2.2.0
## [119] randomForest_4.7-1.2 dbplyr_2.5.1
## [121] xtable_1.8-4 evaluate_1.0.5
## [123] mvtnorm_1.3-3 cli_3.6.5
## [125] compiler_4.5.2 rlang_1.1.6
## [127] crayon_1.5.3 future.apply_1.20.0
## [129] labeling_0.4.3 LaplacesDemon_16.1.6
## [131] mclust_6.1.2 QFeatures_1.20.0
## [133] affy_1.88.0 plyr_1.8.9
## [135] fs_1.6.6 stringi_1.8.7
## [137] viridisLite_0.4.2 Biostrings_2.78.0
## [139] lazyeval_0.2.2 Matrix_1.7-4
## [141] hms_1.1.4 bit64_4.6.0-1
## [143] future_1.67.0 ggplot2_4.0.0
## [145] KEGGREST_1.50.0 statmod_1.5.1
## [147] SummarizedExperiment_1.40.0 kernlab_0.9-33
## [149] igraph_2.2.1 memoise_2.0.1
## [151] affyio_1.80.0 bslib_0.9.0
## [153] sampling_2.11 bit_4.6.0References
We will use the sub-cellular markers defined in the
markers.tlfeature variable, instead of the defaultmarkers.↩︎Large scale applications of this algorithms were run on a cluster using an MPI backend defined with
SnowParams(256, type="MPI").↩︎Note that the scores extracted here are based on the random subsest of weights.↩︎