Longitudinal omics data analysis: approaches and applications.

ABSTRACT: Longitudinal omics data (LOD) analysis is essential for understanding the dynamics of biological processes and disease progression over time. This review explores various statistical and computational approaches for analyzing such data, emphasizing their applications and limitations. The main characteristics of longitudinal data, such as imbalance, high-dimensionality, and non-Gaussianity are discussed for modeling and hypothesis testing. We discuss the properties of linear mixed models (LMM) and generalized linear mixed models (GLMM) as foundational tools in LOD analyses and highlight their extensions to handle the obstacles in the frequentist and Bayesian frameworks. We differentiate dynamic data analysis between time-course and longitudinal analyses, covering functional data analysis (F

SUBMITTER: Taheriyoun AR 

PROVIDER: S-EPMC12818117 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature