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Park HG. Bayesian estimation of covariate assisted principal regression for brain functional connectivity. Biostatistics 2024:kxae023. [PMID: 38981041 DOI: 10.1093/biostatistics/kxae023] [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: 06/14/2023] [Revised: 03/25/2024] [Accepted: 06/02/2024] [Indexed: 07/11/2024] Open
Abstract
This paper presents a Bayesian reformulation of covariate-assisted principal regression for covariance matrix outcomes to identify low-dimensional components in the covariance associated with covariates. By introducing a geometric approach to the covariance matrices and leveraging Euclidean geometry, we estimate dimension reduction parameters and model covariance heterogeneity based on covariates. This method enables joint estimation and uncertainty quantification of relevant model parameters associated with heteroscedasticity. We demonstrate our approach through simulation studies and apply it to analyze associations between covariates and brain functional connectivity using data from the Human Connectome Project.
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Affiliation(s)
- Hyung G Park
- Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, 180 Madison Ave., New York, NY 10016, USA
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2
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Cremaschi A, De Iorio M, Kothandaraman N, Yap F, Tint MT, Eriksson J. Joint modeling of association networks and longitudinal biomarkers: An application to childhood obesity. Stat Med 2024; 43:1135-1152. [PMID: 38197220 DOI: 10.1002/sim.9994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 01/11/2024]
Abstract
The prevalence of chronic non-communicable diseases such as obesity has noticeably increased in the last decade. The study of these diseases in early life is of paramount importance in determining their course in adult life and in supporting clinical interventions. Recently, attention has been drawn to approaches that study the alteration of metabolic pathways in obese children. In this work, we propose a novel joint modeling approach for the analysis of growth biomarkers and metabolite associations, to unveil metabolic pathways related to childhood obesity. Within a Bayesian framework, we flexibly model the temporal evolution of growth trajectories and metabolic associations through the specification of a joint nonparametric random effect distribution, with the main goal of clustering subjects, thus identifying risk sub-groups. Growth profiles as well as patterns of metabolic associations determine the clustering structure. Inclusion of risk factors is straightforward through the specification of a regression term. We demonstrate the proposed approach on data from the Growing Up in Singapore Towards healthy Outcomes cohort study, based in Singapore. Posterior inference is obtained via a tailored MCMC algorithm, involving a nonparametric prior with mixed support. Our analysis has identified potential key pathways in obese children that allow for the exploration of possible molecular mechanisms associated with childhood obesity.
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Affiliation(s)
| | - Maria De Iorio
- Singapore Institute for Clinical Sciences, A*STAR, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Statistical Science, University College London, London, UK
| | | | - Fabian Yap
- Department of Paediatrics, KK Women's and Children's Hospital, Singapore
| | - Mya Thway Tint
- Singapore Institute for Clinical Sciences, A*STAR, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Johan Eriksson
- Singapore Institute for Clinical Sciences, A*STAR, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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3
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Molinari M, Cremaschi A, De Iorio M, Chaturvedi N, Hughes A, Tillin T. Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases. J Appl Stat 2022; 51:114-138. [PMID: 38179161 PMCID: PMC10763914 DOI: 10.1080/02664763.2022.2116746] [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: 10/05/2020] [Accepted: 08/14/2022] [Indexed: 10/14/2022]
Abstract
We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.
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Affiliation(s)
- Marco Molinari
- Department of Statistical Science, University College, London, London, UK
| | | | - Maria De Iorio
- Department of Statistical Science, University College, London, London, UK
- Singapore Institute for Clinical Sciences, A*STAR, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Nishi Chaturvedi
- Department of Population Science and Experimental Medicine, University College London, London, UK
| | - Alun Hughes
- Department of Population Science and Experimental Medicine, University College London, London, UK
| | - Therese Tillin
- Department of Population Science and Experimental Medicine, University College London, London, UK
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4
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Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo. STAT METHOD APPL-GER 2022. [DOI: 10.1007/s10260-021-00600-7] [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]
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5
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van den Boom W, Beskos A, De Iorio M. The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models. J Comput Graph Stat 2022. [DOI: 10.1080/10618600.2022.2050250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
| | - Alexandros Beskos
- Department of Statistical Science, University College London
- Alan Turing Institute, UK
| | - Maria De Iorio
- Yong Loo Lin School of Medicine, National University of Singapore
- Department of Statistical Science, University College London
- Singapore Institute for Clinical Sciences, A*STAR
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6
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Wang Z, Kaseb AO, Amin HM, Hassan MM, Wang W, Morris JS. Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer. J Am Stat Assoc 2022; 117:533-546. [PMID: 36090952 PMCID: PMC9454401 DOI: 10.1080/01621459.2021.2000866] [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: 02/18/2019] [Revised: 07/13/2021] [Accepted: 10/24/2021] [Indexed: 10/19/2022]
Abstract
It is well-established that interpatient heterogeneity in cancer may significantly affect genomic data analyses and in particular, network topologies. Most existing graphical model methods estimate a single population-level graph for genomic or proteomic network. In many investigations, these networks depend on patient-specific indicators that characterize the heterogeneity of individual networks across subjects with respect to subject-level covariates. Examples include assessments of how the network varies with patient-specific prognostic scores or comparisons of tumor and normal graphs while accounting for tumor purity as a continuous predictor. In this paper, we propose a novel edge regression model for undirected graphs, which estimates conditional dependencies as a function of subject-level covariates. We evaluate our model performance through simulation studies focused on comparing tumor and normal graphs while adjusting for tumor purity. In application to a dataset of proteomic measurements on plasma samples from patients with hepatocellular carcinoma (HCC), we ascertain how blood protein networks vary with disease severity, as measured by HepatoScore, a novel biomarker signature measuring disease severity. Our case study shows that the network connectivity increases with HepatoScore and a set of hub genes as well as important gene connections are identified under different HepatoScore, which may provide important biological insights to the development of precision therapies for HCC.
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Affiliation(s)
- Zeya Wang
- Department of Statistics, Rice University; Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Veerabhadran Baladandayuthapani; Department of Biostatistics, University of Michigan
| | - Ahmed O Kaseb
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center
| | - Hesham M Amin
- Department of Hematopathology, The University of Texas MD Anderson Cancer Center
| | - Manal M Hassan
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center
| | - Wenyi Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center
| | - Jeffrey S Morris
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
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Codazzi L, Colombi A, Gianella M, Argiento R, Paci L, Pini A. Gaussian graphical modeling for spectrometric data analysis. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2021.107416] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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8
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Hu J, Chen Z, Lin W, Fan X. Bayesian detection of event spreading pattern from multivariate binary time series. COMMUN STAT-SIMUL C 2021. [DOI: 10.1080/03610918.2021.1879138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Jie Hu
- Department of Probability and Statistics, School of Mathematical Science, Xiamen University, Fujian, China
| | - Zirui Chen
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Wenwei Lin
- Department of Probability and Statistics, School of Mathematical Science, Xiamen University, Fujian, China
| | - Xiaodan Fan
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
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Castelletti F, La Rocca L, Peluso S, Stingo FC, Consonni G. Bayesian learning of multiple directed networks from observational data. Stat Med 2020; 39:4745-4766. [PMID: 32969059 DOI: 10.1002/sim.8751] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 06/29/2020] [Accepted: 08/25/2020] [Indexed: 11/08/2022]
Abstract
Graphical modeling represents an established methodology for identifying complex dependencies in biological networks, as exemplified in the study of co-expression, gene regulatory, and protein interaction networks. The available observations often exhibit an intrinsic heterogeneity, which impacts on the network structure through the modification of specific pathways for distinct groups, such as disease subtypes. We propose to infer the resulting multiple graphs jointly in order to benefit from potential similarities across groups; on the other hand our modeling framework is able to accommodate group idiosyncrasies. We consider directed acyclic graphs (DAGs) as network structures, and develop a Bayesian method for structural learning of multiple DAGs. We explicitly account for Markov equivalence of DAGs, and propose a suitable prior on the collection of graph spaces that induces selective borrowing strength across groups. The resulting inference allows in particular to compute the posterior probability of edge inclusion, a useful summary for representing flow directions within the network. Finally, we detail a simulation study addressing the comparative performance of our method, and present an analysis of two protein networks together with a substantive interpretation of our findings.
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Affiliation(s)
- Federico Castelletti
- Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Luca La Rocca
- Department of Physics, Informatics and Mathematics, Università degli Studi di Modena e Reggio Emilia, Modena, Italy
| | - Stefano Peluso
- Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Francesco C Stingo
- Department of Statistics, Computer Science, Applications "G. Parenti", Università degli Studi di Firenze, Florence, Italy
| | - Guido Consonni
- Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
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Affiliation(s)
- Simón Lunagómez
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Sofia C. Olhede
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Statistical Science, UCL, London, UK
| | - Patrick J. Wolfe
- Department of Statistics, Purdue University, West Lafayette, IN
- Department of Computer Science, Purdue University, West Lafayette, IN
- Department of Electrical & Computer Engineering, Purdue University, West Lafayette, IN
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11
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Cao X, Khare K, Ghosh M. Consistent Bayesian sparsity selection for high-dimensional Gaussian DAG models with multiplicative and beta-mixture priors. J MULTIVARIATE ANAL 2020. [DOI: 10.1016/j.jmva.2020.104628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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Narisetty NN. Discussion. Int Stat Rev 2020. [DOI: 10.1111/insr.12392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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13
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Das P, Peterson CB, Do KA, Akbani R, Baladandayuthapani V. NExUS: Bayesian simultaneous network estimation across unequal sample sizes. Bioinformatics 2020; 36:798-804. [PMID: 31504175 PMCID: PMC8215919 DOI: 10.1093/bioinformatics/btz636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 06/25/2019] [Accepted: 08/26/2019] [Indexed: 01/31/2023] Open
Abstract
MOTIVATION Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited. RESULTS We develop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple networks while avoiding artefactual relationship between sample size and network sparsity. We demonstrate through simulations that NExUS outperforms existing network estimation methods in this context, and apply it to learn network similarity and shared pathway activity for groups of cancers with related origins represented in The Cancer Genome Atlas (TCGA) proteomic data. AVAILABILITY AND IMPLEMENTATION The NExUS source code is freely available for download at https://github.com/priyamdas2/NExUS. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Priyam Das
- Department of Biostatistics, TX 77030, USA
| | | | - Kim-Anh Do
- Department of Biostatistics, TX 77030, USA
| | - Rehan Akbani
- Department of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Castelletti F, Consonni G. Objective Bayes model selection of Gaussian interventional essential graphs for the identification of signaling pathways. Ann Appl Stat 2019. [DOI: 10.1214/19-aoas1275] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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