Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Learning from heterogeneous data sources: an application in spatial proteomics

Lisa M. Breckels, Sean Holden, David Wonjar, Claire M. Mulvey, Andy Christoforou, Arnoud Groen, Oliver Kohlbacher, Kathryn S. Lilley, View ORCID ProfileLaurent Gatto
doi: https://doi.org/10.1101/022152
Lisa M. Breckels
1Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
2Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sean Holden
3Computer Laboratory, University of Cambridge, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David Wonjar
4Center for Bioinformatics, Universität Tübingen, Tübingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Claire M. Mulvey
2Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andy Christoforou
2Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Arnoud Groen
2Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Oliver Kohlbacher
4Center for Bioinformatics, Universität Tübingen, Tübingen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kathryn S. Lilley
2Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Laurent Gatto
1Computational Proteomics Unit, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
2Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Laurent Gatto
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

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 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 three different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to a 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.

Footnotes

  • ↵* email: lg390{at}cam.ac.uk

  • Abbreviations LOPIT: Localisation of Organelle Proteins by Isotope Tagging, PCP: Protein Correlation Profiling, ML: Machine learning, TL: Transfer learning, SVM: Support vector machine, PCA: Principal component analysis, GO: Gene Ontology, CC: Cellular compartment, iTRAQ: Isobaric tags for relative and absolute quantitation, TMT: Tandem mass tags, MS: Mass spectrometry

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted July 16, 2015.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Learning from heterogeneous data sources: an application in spatial proteomics
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Learning from heterogeneous data sources: an application in spatial proteomics
Lisa M. Breckels, Sean Holden, David Wonjar, Claire M. Mulvey, Andy Christoforou, Arnoud Groen, Oliver Kohlbacher, Kathryn S. Lilley, Laurent Gatto
bioRxiv 022152; doi: https://doi.org/10.1101/022152
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Learning from heterogeneous data sources: an application in spatial proteomics
Lisa M. Breckels, Sean Holden, David Wonjar, Claire M. Mulvey, Andy Christoforou, Arnoud Groen, Oliver Kohlbacher, Kathryn S. Lilley, Laurent Gatto
bioRxiv 022152; doi: https://doi.org/10.1101/022152

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4235)
  • Biochemistry (9136)
  • Bioengineering (6784)
  • Bioinformatics (24001)
  • Biophysics (12129)
  • Cancer Biology (9534)
  • Cell Biology (13778)
  • Clinical Trials (138)
  • Developmental Biology (7636)
  • Ecology (11702)
  • Epidemiology (2066)
  • Evolutionary Biology (15513)
  • Genetics (10644)
  • Genomics (14326)
  • Immunology (9483)
  • Microbiology (22840)
  • Molecular Biology (9090)
  • Neuroscience (48995)
  • Paleontology (355)
  • Pathology (1482)
  • Pharmacology and Toxicology (2570)
  • Physiology (3846)
  • Plant Biology (8331)
  • Scientific Communication and Education (1471)
  • Synthetic Biology (2296)
  • Systems Biology (6192)
  • Zoology (1301)