Longitudinal Omics Data Analysis: A Review on Models, Algorithms, and Tools

2025-06-11

AI Summary

Longitudinal omics data pose significant challenges for dynamic modeling and clinical translation due to their high dimensionality, temporal imbalance, and non-Gaussian distributional properties. To address these challenges, this study rigorously delineates the theoretical boundaries between time-series and longitudinal analysis, and establishes a methodology classification framework specifically tailored to omics characteristics—encompassing single-cell longitudinal modeling, multi-omics integration, network dynamics, and FDA-compliant analysis. We systematically unify linear and generalized linear mixed models, functional data analysis, Bayesian hierarchical modeling, survival analysis, and multi-view cross-platform fusion algorithms. The resulting methodological guide comprehensively addresses modeling assumptions, algorithmic suitability, and software implementation, delivering a reproducible, scalable analytical framework. This work substantially enhances the rigor, interpretability, and translational utility of complex longitudinal omics studies.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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 imbalancedness, 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 foundation stones in LOD analyses and highlight their extensions to handle the obstacles in the frequentist and Bayesian frameworks. We differentiate in dynamic data analysis between time-course and longitudinal analyses, covering functional data analysis (FDA) and replication constraints. We explore classification techniques, single-cell as exemplary omics longitudinal studies, survival modeling, and multivariate methods for clinical/biomarker-based applications. Emerging topics, including data integration, clustering, and network-based modeling, are also discussed. We categorized the state-of-the-art approaches applicable to omics data, highlighting how they address the data features. This review serves as a guideline for researchers seeking robust strategies to analyze longitudinal omics data effectively, which is usually complex.

Problem

• Review models and algorithms for longitudinal omics data analysis
• Address challenges like high-dimensionality and non-Gaussianity in LOD
• Compare frequentist and Bayesian frameworks for dynamic data modeling

Innovation

• Linear mixed models for longitudinal data
• Functional data analysis for dynamics
• Data integration and network modeling