Bayesian multivariate sparse functional principal components analysis with application to longitudinal microbiome multiomics data.

ABSTRACT: 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 inter-relationships. 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 indep

SUBMITTER: Jiang L 

PROVIDER: S-EPMC12442853 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature