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Lui A, Lee J, Thall PF, Daher M, Rezvani K, Basar R. A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data. J R Stat Soc Ser C Appl Stat 2023; 72:718-738. [PMID: 37325776 PMCID: PMC10264057 DOI: 10.1093/jrsssc/qlad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 04/02/2023] [Indexed: 06/17/2023]
Abstract
A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missingship mechanism. In contrast with conventional cell clustering methods, which cluster observed marker expression levels separately for each sample, the FAM-based method can be applied simultaneously to multiple samples, and also identify important cell subpopulations likely to be otherwise missed. The proposed FAM-based method is applied to jointly analyse three CyTOF datasets to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved NK cell therapies.
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Affiliation(s)
- Arthur Lui
- Department of Statistics, Baskin School of Engineering, University of California Santa Cruz, 1156 High Street, Santa Cruz, CA, 95064, USA
| | - Juhee Lee
- Department of Statistics, University of California at Santa Cruz, Santa Cruz, CA, USA
| | - Peter F Thall
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, TX, USA
| | - May Daher
- Department of Stem Cell Transplantation and Cellular Therapy, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Katy Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, M.D. Anderson Cancer Center, Houston, TX, USA
| | - Rafet Basar
- Department of Stem Cell Transplantation and Cellular Therapy, M.D. Anderson Cancer Center, Houston, TX, USA
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Wade S. Bayesian cluster analysis. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220149. [PMID: 36970819 PMCID: PMC10041359 DOI: 10.1098/rsta.2022.0149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 01/03/2023] [Indexed: 06/18/2023]
Abstract
Bayesian cluster analysis offers substantial benefits over algorithmic approaches by providing not only point estimates but also uncertainty in the clustering structure and patterns within each cluster. An overview of Bayesian cluster analysis is provided, including both model-based and loss-based approaches, along with a discussion on the importance of the kernel or loss selected and prior specification. Advantages are demonstrated in an application to cluster cells and discover latent cell types in single-cell RNA sequencing data to study embryonic cellular development. Lastly, we focus on the ongoing debate between finite and infinite mixtures in a model-based approach and robustness to model misspecification. While much of the debate and asymptotic theory focuses on the marginal posterior of the number of clusters, we empirically show that quite a different behaviour is obtained when estimating the full clustering structure. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.
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Affiliation(s)
- S. Wade
- School of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh, James Clerk Maxwell Building, Edinburgh, UK
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Wang Z, Ni Y, Jing B, Wang D, Zhang H, Xing E. DNB: A Joint Learning Framework for Deep Bayesian Nonparametric Clustering. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:7610-7620. [PMID: 34156951 DOI: 10.1109/tnnls.2021.3085891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Clustering algorithms based on deep neural networks have been widely studied for image analysis. Most existing methods require partial knowledge of the true labels, namely, the number of clusters, which is usually not available in practice. In this article, we propose a Bayesian nonparametric framework, deep nonparametric Bayes (DNB), for jointly learning image clusters and deep representations in a doubly unsupervised manner. In doubly unsupervised learning, we are dealing with the problem of "unknown unknowns," where we estimate not only the unknown image labels but also the unknown number of labels as well. The proposed algorithm alternates between generating a potentially unbounded number of clusters in the forward pass and learning the deep networks in the backward pass. With the help of the Dirichlet process mixtures, the proposed method is able to partition the latent representations space without specifying the number of clusters a priori. An important feature of this work is that all the estimation is realized with an end-to-end solution, which is very different from the methods that rely on post hoc analysis to select the number of clusters. Another key idea in this article is to provide a principled solution to the problem of "trivial solution" for deep clustering, which has not been much studied in the current literature. With extensive experiments on benchmark datasets, we show that our doubly unsupervised method achieves good clustering performance and outperforms many other unsupervised image clustering methods.
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Lijoi A, Prünster I, Rigon T. Finite-dimensional Discrete Random Structures and Bayesian Clustering. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2149406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Antonio Lijoi
- Department of Decision Sciences, Bocconi University, Milano
- Bocconi Institute for Data Science and Analytics (BIDSA)
| | - Igor Prünster
- Department of Decision Sciences, Bocconi University, Milano
- Bocconi Institute for Data Science and Analytics (BIDSA)
| | - Tommaso Rigon
- Department of Economics, Management and Statistics, University of Milano-Bicocca
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Beraha M, Argiento R, Møller J, Guglielmi A. MCMC Computations for Bayesian Mixture Models Using Repulsive Point Processes. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2021.2000424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Mario Beraha
- Department of Mathematics, Politecnico di Milano, Milano, Italy
- Department of Computer Science, Università di Bologna, Bologna, Italy
| | - Raffaele Argiento
- Department of Economics, Università degli Studi di Bergamo, Milano, Italy
| | - Jesper Møller
- Department of Mathematical Sciences, Aalborg University, Aalborg, Denmark
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Li Y, Bandyopadhyay D, Xie F, Xu Y. BAREB: A Bayesian repulsive biclustering model for periodontal data. Stat Med 2020; 39:2139-2151. [PMID: 32246534 PMCID: PMC7272289 DOI: 10.1002/sim.8536] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 02/12/2020] [Accepted: 03/07/2020] [Indexed: 11/11/2022]
Abstract
Preventing periodontal diseases (PD) and maintaining the structure and function of teeth are important goals for personal oral care. To understand the heterogeneity in patients with diverse PD patterns, we develop a Bayesian repulsive biclustering method that can simultaneously cluster the PD patients and their tooth sites after taking the patient- and site-level covariates into consideration. BAREB uses the determinantal point process prior to induce diversity among different biclusters to facilitate parsimony and interpretability. Since PD progression is hypothesized to be spatially referenced, BAREB factors in the spatial dependence among tooth sites. In addition, since PD is the leading cause for tooth loss, the missing data mechanism is nonignorable. Such nonrandom missingness is incorporated into BAREB. For the posterior inference, we design an efficient reversible jump Markov chain Monte Carlo sampler. Simulation studies show that BAREB is able to accurately estimate the biclusters, and compares favorably to alternatives. For real world application, we apply BAREB to a dataset from a clinical PD study, and obtain desirable and interpretable results. A major contribution of this article is the Rcpp implementation of our methodology, available in the R package BAREB.
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Affiliation(s)
- Yuliang Li
- Department of Applied Mathematics and Statistics, Johns Hopkins University, MD, U.S.A
| | | | - Fangzheng Xie
- Department of Applied Mathematics and Statistics, Johns Hopkins University, MD, U.S.A
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, MD, U.S.A
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Camerlenghi F, Dunson DB, Lijoi A, Prünster I, Rodríguez A. Latent Nested Nonparametric Priors (with Discussion). BAYESIAN ANALYSIS 2019; 14:1303-1356. [PMID: 35978607 PMCID: PMC9381042 DOI: 10.1214/19-ba1169] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Discrete random structures are important tools in Bayesian nonparametrics and the resulting models have proven effective in density estimation, clustering, topic modeling and prediction, among others. In this paper, we consider nested processes and study the dependence structures they induce. Dependence ranges between homogeneity, corresponding to full exchangeability, and maximum heterogeneity, corresponding to (unconditional) independence across samples. The popular nested Dirichlet process is shown to degenerate to the fully exchangeable case when there are ties across samples at the observed or latent level. To overcome this drawback, inherent to nesting general discrete random measures, we introduce a novel class of latent nested processes. These are obtained by adding common and group-specific completely random measures and, then, normalizing to yield dependent random probability measures. We provide results on the partition distributions induced by latent nested processes, and develop a Markov Chain Monte Carlo sampler for Bayesian inferences. A test for distributional homogeneity across groups is obtained as a by-product. The results and their inferential implications are showcased on synthetic and real data.
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Affiliation(s)
- Federico Camerlenghi
- Department of Economics, Management and Statistics, University of Milano - Bicocca, Piazza dell'Ateneo Nuovo 1, 20126 Milano, Italy
- Also affiliated to Collegio Carlo Alberto, Torino and BIDSA, Bocconi University, Milano, Italy
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC 27708-0251 U.S.A
| | - Antonio Lijoi
- Department of Decision Sciences and BIDSA, Bocconi University, via Röntgen 1, 20136 Milano, Italy
| | - Igor Prünster
- Department of Decision Sciences and BIDSA, Bocconi University, via Röntgen 1, 20136 Milano, Italy
| | - Abel Rodríguez
- Department of Applied Mathematics and Statistics, University of California at Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, U.S.A
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Fúquene J, Steel M, Rossell D. On choosing mixture components via non-local priors. J R Stat Soc Series B Stat Methodol 2019. [DOI: 10.1111/rssb.12333] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Affiliation(s)
- Fangzheng Xie
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD
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Affiliation(s)
- José J. Quinlan
- Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Garritt L. Page
- Department of Statistics, Brigham Young University, Provo, UT, USA
| | - Fernando A. Quintana
- Departamento de Estadística, Pontificia Universidad Católica de Chile, Santiago, Chile
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