Robust identification of temporal biomarkers in longitudinal omics studies.

ABSTRACT:

Motivation

Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject.

Results

In this work, we developed OmicsLonDA, a statistical method th

SUBMITTER: Metwally AA 

PROVIDER: S-EPMC9344853 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature