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Zheng C, Eckley I, Fearnhead P. Consistency of a range of penalised cost approaches for detecting multiple changepoints. Electron J Stat 2022. [DOI: 10.1214/22-ejs2048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Chao Zheng
- Department of Mathematics and Statistics Lancaster University
| | - Idris Eckley
- Department of Mathematics and Statistics Lancaster University
| | - Paul Fearnhead
- Department of Mathematics and Statistics Lancaster University
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Patin R, Etienne M, Lebarbier E, Chamaillé‐Jammes S, Benhamou S. Identifying stationary phases in multivariate time series for highlighting behavioural modes and home range settlements. J Anim Ecol 2019; 89:44-56. [DOI: 10.1111/1365-2656.13105] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 08/08/2019] [Indexed: 11/29/2022]
Affiliation(s)
- Rémi Patin
- Centre d'Écologie Fonctionnelle et Évolutive CNRS et Université de Montpellier Montpellier France
| | - Marie‐Pierre Etienne
- Institut de recherche mathématique de RennesUniversité de Rennes, AgroCampusOuest Rennes France
| | - Emilie Lebarbier
- Mathématiques et Informatique Appliquées Agroparistech Paris France
| | - Simon Chamaillé‐Jammes
- Centre d'Écologie Fonctionnelle et Évolutive CNRS et Université de Montpellier Montpellier France
- LTSER France Zone Atelier ‘Hwange’ Hwange National Park Dete Zimbabwe
- Department of Zoology & Entomology Mammal Research Institute University of Pretoria Pretoria South Africa
| | - Simon Benhamou
- Centre d'Écologie Fonctionnelle et Évolutive CNRS et Université de Montpellier Montpellier France
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Brault V, Ouadah S, Sansonnet L, Lévy-Leduc C. Nonparametric multiple change-point estimation for analyzing large Hi-C data matrices. J MULTIVARIATE ANAL 2018. [DOI: 10.1016/j.jmva.2017.12.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Machné R, Murray DB, Stadler PF. Similarity-Based Segmentation of Multi-Dimensional Signals. Sci Rep 2017; 7:12355. [PMID: 28955039 PMCID: PMC5617875 DOI: 10.1038/s41598-017-12401-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 08/30/2017] [Indexed: 11/25/2022] Open
Abstract
The segmentation of time series and genomic data is a common problem in computational biology. With increasingly complex measurement procedures individual data points are often not just numbers or simple vectors in which all components are of the same kind. Analysis methods that capitalize on slopes in a single real-valued data track or that make explicit use of the vectorial nature of the data are not applicable in such scenaria. We develop here a framework for segmentation in arbitrary data domains that only requires a minimal notion of similarity. Using unsupervised clustering of (a sample of) the input yields an approximate segmentation algorithm that is efficient enough for genome-wide applications. As a showcase application we segment a time-series of transcriptome sequencing data from budding yeast, in high temporal resolution over ca. 2.5 cycles of the short-period respiratory oscillation. The algorithm is used with a similarity measure focussing on periodic expression profiles across the metabolic cycle rather than coverage per time point.
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Affiliation(s)
- Rainer Machné
- Institute for Synthetic Microbiology, Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich Heine University Düsseldorf, Universitätsstraße 1, D-40225, Düsseldorf, Germany. .,Department of Theoretical Chemistry of the University of Vienna, Währingerstrasse 17, Vienna, A-1090, Austria.
| | - Douglas B Murray
- Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0017, Japan
| | - Peter F Stadler
- Bioinformatics Group, Department of Computer Science, Interdisciplinary Center for Bioinformatics, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Competence Center for Scalable Data Services and Solutions, and Leipzig Research Center for Civilization Diseases, University Leipzig, Härtelstrasse 16-18, D-04107, Leipzig, Germany. .,Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, D-04103, Leipzig, Germany. .,Fraunhofer Institute for Cell Therapy and Immunology, Perlickstrasse 1, D-04103, Leipzig, Germany. .,Department of Theoretical Chemistry of the University of Vienna, Währingerstrasse 17, Vienna, A-1090, Austria. .,Center for RNA in Technology and Health, Univ. Copenhagen, Grønneg ardsvej 3, Frederiksberg C, Denmark. .,Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA.
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Delatola EI, Lebarbier E, Mary-Huard T, Radvanyi F, Robin S, Wong J. SegCorr a statistical procedure for the detection of genomic regions of correlated expression. BMC Bioinformatics 2017; 18:333. [PMID: 28697800 PMCID: PMC5504623 DOI: 10.1186/s12859-017-1742-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2016] [Accepted: 06/26/2017] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation). RESULTS The identification of correlated regions requires segmenting the gene expression correlation matrix into regions of homogeneously correlated genes and assessing whether the observed local correlation is significantly higher than the background chromosomal correlation. A unified statistical framework is proposed to achieve these two tasks, where optimal segmentation is efficiently performed using dynamic programming algorithm, and detection of highly correlated regions is then achieved using an exact test procedure. We also propose a simple and efficient procedure to correct the expression signal for mechanisms already known to impact expression correlation. The performance and robustness of the proposed procedure, called SegCorr, are evaluated on simulated data. The procedure is illustrated on cancer data, where the signal is corrected for correlations caused by copy number variation. It permitted the detection of regions with high correlations linked to epigenetic marks like DNA methylation. CONCLUSIONS SegCorr is a novel method that performs correlation matrix segmentation and applies a test procedure in order to detect highly correlated regions in gene expression.
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Affiliation(s)
- Eleni Ioanna Delatola
- AgroParisTech UMR518, Paris, 75005, France.
- INRA UMR518, Paris, 75005, France.
- Institut Curie, PSL Research University, Cedex 05, Paris, 75248, France.
- CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, Paris, 75248, France.
| | - Emilie Lebarbier
- AgroParisTech UMR518, Paris, 75005, France
- INRA UMR518, Paris, 75005, France
| | - Tristan Mary-Huard
- AgroParisTech UMR518, Paris, 75005, France
- INRA UMR518, Paris, 75005, France
- INRA, UMR 0320 - UMR 8120 Genetique Quantitative et Evolution-Le Moulon, Gif-sur-Yvette, F-91190, France
| | - François Radvanyi
- Institut Curie, PSL Research University, Cedex 05, Paris, 75248, France
- CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, Paris, 75248, France
| | - Stéphane Robin
- AgroParisTech UMR518, Paris, 75005, France
- INRA UMR518, Paris, 75005, France
| | - Jennifer Wong
- Institut Curie, PSL Research University, Cedex 05, Paris, 75248, France
- CNRS UMR144, Equipe Labellisee par La Ligue Nationale contre le Cancer, Cedex 05, Paris, 75248, France
- Molecular Oncology Unit, Department of Biochemistry, Hospital Saint Louis, AP-HP, Cedex 10, Paris, 75475, France
- Université Paris Diderot, Sorbonne Paris Cité, CNRS UMR7212/INSERM U944, Cedex 10, Paris, 75475, France
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