Change point detection for clustered expression data.

ABSTRACT:

Background

To detect changes in biological processes, samples are often studied at several time points. We examined expression data measured at different developmental stages, or more broadly, historical data. Hence, the main assumption of our proposed methodology was the independence between the examined samples over time. In addition, however, the examinations were clustered at each time point by measuring littermates from relatively few mother mice at each developmental stage. As each examination was lethal, we had an independent data structure over the entire history, but a dependent data structure at a particular time point. Over the course of these historical data, we wanted to identify abrupt changes in the parameter of interest - change points.

Results

In this study,

SUBMITTER: Sieg M 

PROVIDER: S-EPMC9261071 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature