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Sahay S, Adhikari S, Hormoz S, Chakrabarti S. An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells. Bioinformatics 2023; 39:btad602. [PMID: 37769241 PMCID: PMC10576164 DOI: 10.1093/bioinformatics/btad602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 09/21/2023] [Accepted: 09/27/2023] [Indexed: 09/30/2023] Open
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 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's 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. AVAILABILITY AND IMPLEMENTATION ODeGP is available at https://github.com/Shaonlab/ODeGP.
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Affiliation(s)
- Shabnam Sahay
- Department of Computer Science, Indian Institute of Technology Bombay, Mumbai, Maharashtra 400076, India
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, Karnataka 560065, India
| | - Shishir Adhikari
- Department of Systems Biology, Harvard Medical School, Boston, MA 02215, United States
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, United States
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA 02215, United States
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, United States
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
| | - Shaon Chakrabarti
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore, Karnataka 560065, India
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Sahay S, Adhikari S, Hormoz S, Chakrabarti S. An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.21.533651. [PMID: 36993318 PMCID: PMC10055182 DOI: 10.1101/2023.03.21.533651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets 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. Here we introduce a new method, ODeGP ( O scillation De tection using G aussian P rocesses), which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene, thus highlighting our method’s ability to discover unexpected patterns. In its current implementation, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.
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Affiliation(s)
- Shabnam Sahay
- Department of Computer Science, Indian Institute of Technology Bombay
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore
| | - Shishir Adhikari
- Department of Systems Biology, Harvard Medical School, Boston
- Department of Data Science, Dana-Farber Cancer Institute, Boston
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston
- Department of Data Science, Dana-Farber Cancer Institute, Boston
- Broad Institute of MIT and Harvard, Cambridge
| | - Shaon Chakrabarti
- Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Bangalore
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Balagué N, Hristovski R, Almarcha M, Garcia-Retortillo S, Ivanov PC. Network Physiology of Exercise: Beyond Molecular and Omics Perspectives. SPORTS MEDICINE - OPEN 2022; 8:119. [PMID: 36138329 PMCID: PMC9500136 DOI: 10.1186/s40798-022-00512-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/27/2022] [Indexed: 11/17/2022]
Abstract
Molecular Exercise Physiology and Omics approaches represent an important step toward synthesis and integration, the original essence of Physiology. Despite the significant progress they have introduced in Exercise Physiology (EP), some of their theoretical and methodological assumptions are still limiting the understanding of the complexity of sport-related phenomena. Based on general principles of biological evolution and supported by complex network science, this paper aims to contrast theoretical and methodological aspects of molecular and network-based approaches to EP. After explaining the main EP challenges and why sport-related phenomena cannot be understood if reduced to the molecular level, the paper proposes some methodological research advances related to the type of studied variables and measures, the data acquisition techniques, the type of data analysis and the assumed relations among physiological levels. Inspired by Network Physiology, Network Physiology of Exercise provides a new paradigm and formalism to quantify cross-communication among diverse systems across levels and time scales to improve our understanding of exercise-related phenomena and opens new horizons for exercise testing in health and disease.
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Affiliation(s)
- Natàlia Balagué
- Complex Systems in Sport Research Group, Institut Nacional d'Educació Fisica de Catalunya (INEFC), University of Barcelona (UB), Barcelona, Spain.
| | - Robert Hristovski
- Complex Systems in Sport Research Group, Faculty of Physical Education, Sport and Health, Ss. Cyril and Methodius University, 1000, Skopje, Republic of Macedonia
| | - Maricarmen Almarcha
- Complex Systems in Sport Research Group, Institut Nacional d'Educació Fisica de Catalunya (INEFC), University of Barcelona (UB), Barcelona, Spain
| | - Sergi Garcia-Retortillo
- Complex Systems in Sport Research Group, Institut Nacional d'Educació Fisica de Catalunya (INEFC), University of Barcelona (UB), Barcelona, Spain
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA, 02215, USA
- Department of Health and Exercise Science, Wake Forest University, Winston-Salem, NC, 21709, USA
| | - Plamen Ch Ivanov
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA, 02215, USA.
- Harvard Medical School and Division of Sleep Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, 1113, Sofia, Bulgaria.
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Oh VKS, Li RW. Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data. Genes (Basel) 2021; 12:352. [PMID: 33673721 PMCID: PMC7997275 DOI: 10.3390/genes12030352] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/19/2021] [Accepted: 02/22/2021] [Indexed: 02/06/2023] Open
Abstract
Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.
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Affiliation(s)
- Vera-Khlara S. Oh
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
- Department of Computer Science and Statistics, College of Natural Sciences, Jeju National University, Jeju City 63243, Korea
| | - Robert W. Li
- Animal Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville, MD 20705, USA;
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Cao M, Zhou W, Breidt FJ, Peers G. Large scale maximum average power multiple inference on time‐course count data with application to RNA‐seq analysis. Biometrics 2019; 76:9-22. [DOI: 10.1111/biom.13144] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 08/28/2019] [Indexed: 11/30/2022]
Affiliation(s)
- Meng Cao
- Department of Statistics Colorado State University Fort Collins Colorado
| | - Wen Zhou
- Department of Statistics Colorado State University Fort Collins Colorado
| | - F. Jay Breidt
- Department of Statistics Colorado State University Fort Collins Colorado
| | - Graham Peers
- Department of Biology Colorado State University Fort Collins Colorado
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Viitaniemi HM, Verhagen I, Visser ME, Honkela A, van Oers K, Husby A. Seasonal Variation in Genome-Wide DNA Methylation Patterns and the Onset of Seasonal Timing of Reproduction in Great Tits. Genome Biol Evol 2019; 11:970-983. [PMID: 30840074 PMCID: PMC6447391 DOI: 10.1093/gbe/evz044] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/05/2019] [Indexed: 02/06/2023] Open
Abstract
In seasonal environments, timing of reproduction is a trait with important fitness consequences, but we know little about the molecular mechanisms that underlie the variation in this trait. Recently, several studies put forward DNA methylation as a mechanism regulating seasonal timing of reproduction in both plants and animals. To understand the involvement of DNA methylation in seasonal timing of reproduction, it is necessary to examine within-individual temporal changes in DNA methylation, but such studies are very rare. Here, we use a temporal sampling approach to examine changes in DNA methylation throughout the breeding season in female great tits (Parus major) that were artificially selected for early timing of breeding. These females were housed in climate-controlled aviaries and subjected to two contrasting temperature treatments. Reduced representation bisulfite sequencing on red blood cell derived DNA showed genome-wide temporal changes in more than 40,000 out of the 522,643 CpG sites examined. Although most of these changes were relatively small (mean within-individual change of 6%), the sites that showed a temporal and treatment-specific response in DNA methylation are candidate sites of interest for future studies trying to understand the link between DNA methylation patterns and timing of reproduction.
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Affiliation(s)
- Heidi M Viitaniemi
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Finland
| | - Irene Verhagen
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Marcel E Visser
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Antti Honkela
- Helsinki Institute for Information Technology HIIT, Department of Mathematics and Statistics, University of Helsinki, Finland
- Department of Public Health, University of Helsinki, Finland
| | - Kees van Oers
- Department of Animal Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
| | - Arild Husby
- Organismal and Evolutionary Biology Research Programme, University of Helsinki, Finland
- Department of Ecology and Genetics, EBC, Uppsala University, Sweden
- Centre for Biodiversity Dynamics, NTNU, Trondheim, Norway
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