BAYESIAN MULTIVARIATE SPARSE FUNCTIONAL PRINCIPAL COMPONENTS ANALYSIS WITH APPLICATION TO LONGITUDINAL MICROBIOME MULTIOMICS DATA

• 成果类型: Article
• 署名作者: Jiang, Lingjing; Elrod, Chris; Kim, Jane J.; Swafford, Austin D.; Knight, Rob; Thompson, Wesley K.
• 署名单位: University of California System; University of California San Diego; University of California System; University of California San Diego; University of California System; University of California San Diego; University of California System; University of California San Diego
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/21-AOAS1587
• 发表日期: 2022
• 页码: 2231-2249
• 关键词: gut microbiome; infection; rates; omics

摘要

Microbiome researchers often need to model the temporal dynamics of multiple complex, nonlinear outcome trajectories simultaneously. This motivates our development of multivariate Sparse Functional Principal Components Analysis (mSFPCA), extending existing SFPCA methods to simultaneously characterize multiple temporal trajectories and their interrelationships. As with existing SFPCA methods, the mSFPCA algorithm characterizes each trajectory as a smooth mean plus a weighted combination of the smooth major modes of variation about the mean, where the weights are given by the component scores for each subject. Unlike existing SFPCA methods, the mSFPCA algorithm allows estimation of multiple trajectories simultaneously, such that the component scores, which are constrained to be independent within a particular outcome for identifiability, may be arbitrarily correlated with component scores for other outcomes. A Cholesky decomposition is used to estimate the component score covariance matrix efficiently and guarantee positive semidefiniteness given these constraints. Mutual information is used to assess the strength of marginal and conditional temporal associations across outcome trajectories. Importantly, we implement mSFPCA as a Bayesian algorithm using R and stan, enabling easy use of packages such as PSIS-LOO for model selection and graphical posterior predictive checks to assess the validity of mSFPCA models. Although we focus on application of mSFPCA to microbiome data in this paper, the mSFPCA model is of general utility and can be used in a wide range of real-world applications.