{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T20:48:59Z","timestamp":1775162939402,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001843","name":"SERB","doi-asserted-by":"publisher","award":["SPR\/2021\/000486"],"award-info":[{"award-number":["SPR\/2021\/000486"]}],"id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]},{"name":"intramural funds from National Center for Biological Sciences\u2013Tata Institute of Fundamental Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 kernel to improve detection of oscillations. By using Bayes factors, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses, thus providing an advantage over P-values. Using synthetic datasets, we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary symmetric oscillations. Next, by analyzing existing qPCR datasets, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak and noisy oscillations. Finally, we generate new qPCR data on mouse embryonic stem cells. Surprisingly, we discover using ODeGP that increasing cell-density results in rapid generation of oscillations in the Bmal1 gene, thus highlighting our method\u2019s ability to discover unexpected and new patterns. In its current implementation, ODeGP is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>ODeGP is available at https:\/\/github.com\/Shaonlab\/ODeGP.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad602","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T16:32:28Z","timestamp":1695918748000},"source":"Crossref","is-referenced-by-count":3,"title":["An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells"],"prefix":"10.1093","volume":"39","author":[{"given":"Shabnam","family":"Sahay","sequence":"first","affiliation":[{"name":"Department of Computer Science, Indian Institute of Technology Bombay , Mumbai, Maharashtra 400076, India"},{"name":"Simons Centre for the Study of Living Machines, National Centre for Biological Sciences , Bangalore, Karnataka 560065, India"}]},{"given":"Shishir","family":"Adhikari","sequence":"additional","affiliation":[{"name":"Department of Systems Biology, Harvard Medical School , Boston, MA 02215, United States"},{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA 02215, United States"}]},{"given":"Sahand","family":"Hormoz","sequence":"additional","affiliation":[{"name":"Department of Systems Biology, Harvard Medical School , Boston, MA 02215, United States"},{"name":"Department of Data Science, Dana-Farber Cancer Institute , Boston, MA 02215, United States"},{"name":"Broad Institute of MIT and Harvard , Cambridge, MA 02142, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7429-5927","authenticated-orcid":false,"given":"Shaon","family":"Chakrabarti","sequence":"additional","affiliation":[{"name":"Simons Centre for the Study of Living Machines, National Centre for Biological Sciences , Bangalore, Karnataka 560065, India"}]}],"member":"286","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"2023101406150009800_btad602-B1","doi-asserted-by":"crossref","first-page":"i8","DOI":"10.1093\/bioinformatics\/btw243","article-title":"What time is it? 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