TPClust: Temporal Profile-Guided Subtyping Using High-Dimensional Omics Data.

ABSTRACT: Clustering is widely used to identify subtypes in heterogeneous populations, yet most approaches rarely integrate longitudinal phenotypic trajectories with high-dimensional molecular profiles, limiting their ability to resolve biologically and clinically meaningful heterogeneity in progressive diseases. We developed TPClust, a supervised, semi-parametric clustering method that integrates high-dimensional omics data with longitudinal phenotypes including outcomes and covariates for outcome-guided subtyping. TPClust jointly models latent subtype membership and longitudinal outcome trajectories using multinomial logistic regression informed by molecular features selected via structured regularization, along with spline-based regression to capture subtype-specific, time-varying covariate effec

SUBMITTER: Hu B 

PROVIDER: S-EPMC12340809 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature