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

TL;DR

This review comprehensively discusses statistical and computational models, algorithms, and tools for analyzing complex longitudinal omics data, emphasizing their applications, limitations, and emerging topics in the field.

Contribution

It categorizes and evaluates state-of-the-art approaches for longitudinal omics data analysis, providing a guideline for researchers to select robust strategies.

Findings

Highlights the use of linear mixed models and their extensions.

Discusses challenges like high-dimensionality and non-Gaussianity.

Explores emerging topics such as data integration and network modeling.

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.