{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/identifying-stochastic-oscillations-in-single","title":"Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes","arxiv_id":"1608.06476","date":"2017-05-25","proceeding":null,"authors":[],"abstract":"Multiple biological processes are driven by oscillatory gene expression at\ndifferent time scales. Pulsatile dynamics are thought to be widespread, and\nsingle-cell live imaging of gene expression has lead to a surge of dynamic,\npossibly oscillatory, data for different gene networks. However, the regulation\nof gene expression at the level of an individual cell involves reactions\nbetween finite numbers of molecules, and this can result in inherent randomness\nin expression dynamics, which blurs the boundaries between aperiodic\nfluctuations and noisy oscillators. Thus, there is an acute need for an\nobjective statistical method for classifying whether an experimentally derived\nnoisy time series is periodic. Here we present a new data analysis method that\ncombines mechanistic stochastic modelling with the powerful methods of\nnon-parametric regression with Gaussian processes. Our method can distinguish\noscillatory gene expression from random fluctuations of non-oscillatory\nexpression in single-cell time series, despite peak-to-peak variability in\nperiod and amplitude of single-cell oscillations. We show that our method\noutperforms the Lomb-Scargle periodogram in successfully classifying cells as\noscillatory or non-oscillatory in data simulated from a simple genetic\noscillator model and in experimental data. Analysis of bioluminescent live cell\nimaging shows a significantly greater number of oscillatory cells when\nluciferase is driven by a {\\it Hes1} promoter (10/19), which has previously\nbeen reported to oscillate, than the constitutive MoMuLV 5' LTR (MMLV) promoter\n(0/25). The method can be applied to data from any gene network to both\nquantify the proportion of oscillating cells within a population and to measure\nthe period and quality of oscillations. It is publicly available as a MATLAB\npackage.","url_abs":"http://arxiv.org/abs/1608.06476v3","url_pdf":"http://arxiv.org/pdf/1608.06476v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"identifying-stochastic-oscillations-in-single","repo_url":"https://github.com/ManchesterBioinference/GPosc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"gaussian-processes","task_name":"Gaussian Processes"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}