title: Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.
abstract: Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.
authors.affiliation: Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.|Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Breckels
authors.affiliation: Computer Laboratory, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Sean B
authors.initials: SB
authors.lastname: Holden
authors.affiliation: Quantitative Biology Center, Universität Tübingen, Tübingen, Germany.
authors.forename: David
authors.initials: D
authors.lastname: Wojnar
authors.affiliation: Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Claire M
authors.initials: CM
authors.lastname: Mulvey
authors.affiliation: Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Andy
authors.initials: A
authors.lastname: Christoforou
authors.affiliation: Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Arnoud
authors.initials: A
authors.lastname: Groen
authors.affiliation: Celgene Institute for Translational Research Europe (CITRE), Sevilla, Spain.
authors.forename: Matthew W B
authors.initials: MW
authors.lastname: Trotter
authors.affiliation: Quantitative Biology Center, Universität Tübingen, Tübingen, Germany.|Center for Bioinformatics, Universität Tübingen, Tübingen, Germany.|Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany.
authors.forename: Oliver
authors.initials: O
authors.lastname: Kohlbacher
authors.affiliation: Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Kathryn S
authors.initials: KS
authors.lastname: Lilley
authors.affiliation: Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.|Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Laurent
authors.initials: L
authors.lastname: Gatto
mesh_terms: D000465:Algorithms; D000818:Animals; D017360:Arabidopsis; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D004330:Drosophila; D053595:Embryonic Stem Cells / Q000378:metabolism; D006801:Humans; D016247:Information Storage and Retrieval; D013058:Mass Spectrometry; D051379:Mice; D020543:Proteome / Q000145:classification / Q000378:metabolism*; D040901:Proteomics / Q000706:statistics & numerical data*; D012984:Software; D013347:Subcellular Fractions / Q000378:metabolism; D060388:Support Vector Machine
chemical_list: D020543:Proteome