A Bayesian Mixture Modelling Approach For Spatial Proteomics

Abstract

1Analysis of the spatial sub-cellular distribution of proteins is of vital importance 2 to fully understand context specic protein function. Some proteins can be found 3 with a single location within a cell, but up to half of proteins may reside in multiple 4 locations, can dynamically re-localise, or reside within an unknown functional com-5 partment. These considerations lead to uncertainty in associating a protein to a single 6 location. Currently, mass spectrometry (MS) based spatial proteomics relies on super-7 vised machine learning algorithms to assign proteins to sub-cellular locations based on 8 common gradient proles. However, such methods fail to quantify uncertainty associ-9 ated with sub-cellular class assignment. Here we reformulate the framework on which 10 we perform statistical analysis. We propose a Bayesian generative classier based on 11 Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus 12 proteins have a probability distribution over sub-cellular locations, with Bayesian com-13 putation performed using the expectation-maximisation (EM) algorithm, as well as 14 Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncer-15 tainty quantication, thus adding a further layer to the analysis of spatial proteomics. 16 Our framework is exible, allowing many dierent systems to be analysed and reveals 17 new modelling opportunities for spatial proteomics. We nd our methods perform 18 competitively with current state-of-the art machine learning methods, whilst simulta-19 neously providing more information. We highlight several examples where classication 20 based on the support vector machine is unable to make any conclusions, while uncer-21 tainty quantication using our approach provides biologically intriguing results. To our 22 knowledge this is the rst Bayesian model of MS-based spatial proteomics data. 23 * omc25@cam.ac.uk † lg390@cam.ac.uk Author summary 24Sub-cellular localisation of proteins provides insights into sub-cellular biological processes. 25 For a protein to carry out its intended function it must be localised to the correct sub-26 cellular environment, whether that be organelles, vesicles or any sub-cellular niche. Correct 27 sub-cellular localisation ensures the biochemical conditions for the protein to carry out its 28 molecular function are met, as well as being near its intended interaction partners. Therefore, 29 mis-localisation of proteins alters cell biochemistry and can disrupt, for example, signalling 30 pathways or inhibit the tracking of material around the cell. The sub-cellular distribution 31 of proteins is complicated by proteins that can reside in multiple micro-environments, or 32 those that move dynamically within the cell. Methods that predict protein sub-cellular 33 localisation often fail to quantify the uncertainty that arises from the complex and dynamic 34 nature of the sub-cellular environment. Here we present a Bayesian methodology to analyse 35 protein sub-cellular localisation. We ex...

Assessing sub-cellular resolution in spatial proteomics experiments

“…As interest in the spatial organisation of the proteome is increasing, it is imperative for the community to better define and assess the quality of spatial proteomics experiments and the reliability of protein sub-cellular assignments? The latter can assessed using improved probabilistic classifiers such as the Bayesian mixture modelling approach proposed by Crook et al [6]. In this work, we propose the QSep metric to assess the former.…”

“…Reciprocally, a broad array of computational methods have been applied, ranging from unsupervised learning e.g. clustering [29] and dimensionality reduction, and supervised learning such as classification (reviewed in [11]), semi-supervised learning and novelty detection [2] and, more recently, transfer learning [3] and Bayesian modelling [6].…”

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

“…These approaches used SILAC quantitation with dierential centrifugation-based fractionation. We analyse 6 replicates from the HeLa Transfer Learning (Breckels et al, 2016) Mclust (as used in Orre et al 2019)PhenoDisco (Breckels et al, 2013) TAGM (Crook et al, 2018) Novelty TAGM (This manuscript) Itzhak et al (2016) and 3 replicates from the mouse primary neuron experiments in Itzhak et al (2017). Hirst et al (2018) also used the DOM protocol coupled with CRISPR-CAS9 knockouts in order to explore the functional role of AP-5.…”

“…Recent work has demonstrated the importance of uncertainty quantication in spatial proteomics (Crook et al, 2018(Crook et al, , 2019a. Crook et al (2018) proposed a generative classication model and took a Bayesian approach to spatial proteomics data analysis by computing probability distributions of protein-organelle assignments using Markov-chain Monte-Carlo (MCMC). These probabilities were then used as the basis for organelle allocations, as well as to quantify the uncertainty in these allocations.…”

“…On the basis that some proteins cannot be well described by any of the annotated sub-cellular niches, a multivariate Student's T distribution was included in the model to enable outlier detection. The proposed T-Augmented Gaussian Mixture (TAGM) model was shown to achieve state-of-the-art predictive performance against other commonly used machine learning algorithms (Crook et al, 2018). Furthermore, the model has been successfully applied to reveal unrivalled insight into the spatial organisation of Toxoplasma gondii (Barylyuk et al, 2020) and identify cargo of the Golgins of the trans-Golgi network (Shin et al, 2019).…”