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A Bayesian mixture modelling approach for spatial proteomics

Author

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  • Oliver M Crook
  • Claire M Mulvey
  • Paul D W Kirk
  • Kathryn S Lilley
  • Laurent Gatto

Abstract

Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.Author summary: Sub-cellular localisation of proteins provides insights into sub-cellular biological processes. For a protein to carry out its intended function it must be localised to the correct sub-cellular environment, whether that be organelles, vesicles or any sub-cellular niche. Correct sub-cellular localisation ensures the biochemical conditions for the protein to carry out its molecular function are met, as well as being near its intended interaction partners. Therefore, mis-localisation of proteins alters cell biochemistry and can disrupt, for example, signalling pathways or inhibit the trafficking of material around the cell. The sub-cellular distribution of proteins is complicated by proteins that can reside in multiple micro-environments, or those that move dynamically within the cell. Methods that predict protein sub-cellular localisation often fail to quantify the uncertainty that arises from the complex and dynamic nature of the sub-cellular environment. Here we present a Bayesian methodology to analyse protein sub-cellular localisation. We explicitly model our data and use Bayesian inference to quantify uncertainty in our predictions. We find our method is competitive with state-of-the-art machine learning methods and additionally provides uncertainty quantification. We show that, with this additional information, we can make deeper insights into the fundamental biochemistry of the cell.

Suggested Citation

  • Oliver M Crook & Claire M Mulvey & Paul D W Kirk & Kathryn S Lilley & Laurent Gatto, 2018. "A Bayesian mixture modelling approach for spatial proteomics," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-29, November.
  • Handle: RePEc:plo:pcbi00:1006516
    DOI: 10.1371/journal.pcbi.1006516
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    References listed on IDEAS

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    1. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
    2. Lisa M Breckels & Sean B Holden & David Wojnar & Claire M Mulvey & Andy Christoforou & Arnoud Groen & Matthew W B Trotter & Oliver Kohlbacher & Kathryn S Lilley & Laurent Gatto, 2016. "Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-26, May.
    3. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    4. Chris Fraley & Adrian E. Raftery, 2007. "Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 155-181, September.
    5. Andy Christoforou & Claire M. Mulvey & Lisa M. Breckels & Aikaterini Geladaki & Tracey Hurrell & Penelope C. Hayward & Thomas Naake & Laurent Gatto & Rosa Viner & Alfonso Martinez Arias & Kathryn S. L, 2016. "A draft map of the mouse pluripotent stem cell spatial proteome," Nature Communications, Nature, vol. 7(1), pages 1-12, April.
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    Cited by:

    1. Oliver M Crook & Aikaterini Geladaki & Daniel J H Nightingale & Owen L Vennard & Kathryn S Lilley & Laurent Gatto & Paul D W Kirk, 2020. "A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-21, November.
    2. Oliver M. Crook & Colin T. R. Davies & Lisa M. Breckels & Josie A. Christopher & Laurent Gatto & Paul D. W. Kirk & Kathryn S. Lilley, 2022. "Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE," Nature Communications, Nature, vol. 13(1), pages 1-21, December.
    3. Nicola M. Moloney & Konstantin Barylyuk & Eelco Tromer & Oliver M. Crook & Lisa M. Breckels & Kathryn S. Lilley & Ross F. Waller & Paula MacGregor, 2023. "Mapping diversity in African trypanosomes using high resolution spatial proteomics," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Jordan Currie & Vyshnavi Manda & Sean K. Robinson & Celine Lai & Vertica Agnihotri & Veronica Hidalgo & R. W. Ludwig & Kai Zhang & Jay Pavelka & Zhao V. Wang & June-Wha Rhee & Maggie P. Y. Lam & Edwar, 2024. "Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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