An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells.

ABSTRACT:

Motivation

Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms (for instance in gene expression, eclosion, egg-laying, and feeding) tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets, and are also limited by their use of P-values in detecting oscillations.

Results

We introduce a new method, ODeGP (Oscillation Detection using Gaussian Processes), which combines Gaussian Process regression and Bayesian inference to incorporate measurement errors, non-uniformly sampled data, and a recently developed non-stationary k

SUBMITTER: Sahay S 

PROVIDER: S-EPMC10576164 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature