CosinorPy: a python package for cosinor-based rhythmometry

Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages...

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Vydáno v:	BMC bioinformatics Ročník 21; číslo 1; s. 1 - 12
Hlavní autor:	Moskon, Miha
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 29.10.2020 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Analysis and modelling of complex systems Applications software Bioinformatics Biological analysis Biological research Biomedical and Life Sciences Chronobiology Circadian analysis Circadian rhythm Circadian rhythms Computational biology Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Computer programs Cosinor Data analysis Design of experiments Life Sciences Methods Microarrays Python Python (Programming language) Regression Regression analysis Regression models Rhythmicity analysis Rhythms Software Software packages Statistical analysis Time series Slovenia Rhythmicity analysis Regression Circadian analysis Cosinor Python
ISSN:	1471-2105, 1471-2105
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Abstract	Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats. Results We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. Conclusion CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.
AbstractList	Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats. Results We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. Conclusion CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from Keywords: Cosinor, Rhythmicity analysis, Circadian analysis, Regression, Python Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats. We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy. CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1. Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats. Results We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. Conclusion CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1. Abstract Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats. Results We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. Conclusion CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1. Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.BACKGROUNDEven though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats.We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats.RESULTSWe present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats.CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.CONCLUSIONCosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy . CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1. Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric regression based on cosinor still has several advantages over these methods and is still widely used. Different software packages for cosinor-based rhythmometry exist, but lack certain functionalities and require data in different, non-unified input formats. Results We present CosinorPy, a Python implementation of cosinor-based methods for rhythmicity detection and analysis. CosinorPy merges and extends the functionalities of existing cosinor packages. It supports the analysis of rhythmic data using single- or multi-component cosinor models, automatic selection of the best model, population-mean cosinor regression, and differential rhythmicity assessment. Moreover, it implements functions that can be used in a design of experiments, a synthetic data generator, and import and export of data in different formats. Conclusion CosinorPy is an easy-to-use Python package for straightforward detection and analysis of rhythmicity requiring minimal statistical knowledge, and produces publication-ready figures. Its code, examples, and documentation are available to download from https://github.com/mmoskon/CosinorPy. CosinorPy can be installed manually or by using pip, the package manager for Python packages. The implementation reported in this paper corresponds to the software release v1.1.
ArticleNumber	485
Audience	Academic
Author	Moškon, Miha
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Cites_doi	10.1371/journal.pcbi.1004094 10.1073/pnas.1619320114 10.1073/pnas.1909557116 10.1073/pnas.1408886111 10.1177/0748730417728663 10.1177/0748730410379711 10.1093/bioinformatics/btz834 10.1007/978-1-62703-637-5_19 10.1080/09291010600903692 10.1016/j.jsmc.2015.08.007 10.1093/bioinformatics/btz730 10.3389/fphys.2019.00682 10.1177/0748730418789536 10.1177/0748730418813785 10.1177/0748730414553029 10.15280/jlm.2019.9.1.1 10.1097/ALN.0000000000000596 10.1126/scitranslmed.aat8806 10.1186/1742-4682-11-16
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Keywords	Rhythmicity analysis Regression Circadian analysis Cosinor Python
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References	R Zhang (3830_CR2) 2014; 111 ME Hughes (3830_CR20) 2017; 32 R Parsons (3830_CR23) 2020; 36 R Refinetti (3830_CR14) 2007; 38 3830_CR12 TS Andreani (3830_CR3) 2015; 10 C Bingham (3830_CR19) 1982; 9 AL Hutchison (3830_CR8) 2015; 11 PF Thaben (3830_CR10) 2014; 29 MD Ruben (3830_CR13) 2019; 116 G Cornelissen (3830_CR15) 2014; 11 3830_CR21 J Brainard (3830_CR4) 2015; 122 Y Xie (3830_CR5) 2019; 10 RC Anafi (3830_CR11) 2017; 114 A Seifalian (3830_CR6) 2019; 9 3830_CR1 3830_CR16 3830_CR17 3830_CR18 AL Hutchison (3830_CR9) 2018; 33 JM Singer (3830_CR22) 2019; 34 ME Hughes (3830_CR7) 2010; 25
References_xml	– volume: 11 start-page: e1004094 issue: 3 year: 2015 ident: 3830_CR8 publication-title: PLoS Comput Biol doi: 10.1371/journal.pcbi.1004094 – volume: 114 start-page: 5312 issue: 20 year: 2017 ident: 3830_CR11 publication-title: Proc Nat Acad Sci doi: 10.1073/pnas.1619320114 – volume: 116 start-page: 20953 issue: 42 year: 2019 ident: 3830_CR13 publication-title: Proc Nat Acad Sci doi: 10.1073/pnas.1909557116 – volume: 111 start-page: 16219 issue: 45 year: 2014 ident: 3830_CR2 publication-title: Proc Nat Acad Sci doi: 10.1073/pnas.1408886111 – volume: 32 start-page: 380 issue: 5 year: 2017 ident: 3830_CR20 publication-title: J Biol Rhythms doi: 10.1177/0748730417728663 – volume: 25 start-page: 372 issue: 5 year: 2010 ident: 3830_CR7 publication-title: J Biol Rhythms doi: 10.1177/0748730410379711 – ident: 3830_CR18 doi: 10.1093/bioinformatics/btz834 – ident: 3830_CR1 doi: 10.1007/978-1-62703-637-5_19 – volume: 9 start-page: 397 issue: 4 year: 1982 ident: 3830_CR19 publication-title: Chronobiologia. – volume: 38 start-page: 275 issue: 4 year: 2007 ident: 3830_CR14 publication-title: Biol Rhythm Res doi: 10.1080/09291010600903692 – volume: 10 start-page: 413 issue: 4 year: 2015 ident: 3830_CR3 publication-title: Sleep Med Clin doi: 10.1016/j.jsmc.2015.08.007 – volume: 36 start-page: 1208 issue: 4 year: 2020 ident: 3830_CR23 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btz730 – volume: 10 start-page: 682 year: 2019 ident: 3830_CR5 publication-title: Frontin Physiol doi: 10.3389/fphys.2019.00682 – ident: 3830_CR17 – volume: 33 start-page: 339 issue: 4 year: 2018 ident: 3830_CR9 publication-title: J Biol Rhythms doi: 10.1177/0748730418789536 – volume: 34 start-page: 5 issue: 1 year: 2019 ident: 3830_CR22 publication-title: J Biol Rhythms doi: 10.1177/0748730418813785 – ident: 3830_CR16 – ident: 3830_CR21 – volume: 29 start-page: 391 issue: 6 year: 2014 ident: 3830_CR10 publication-title: J Biol Rhythms doi: 10.1177/0748730414553029 – volume: 9 start-page: 1 issue: 1 year: 2019 ident: 3830_CR6 publication-title: J Lifestyle Med doi: 10.15280/jlm.2019.9.1.1 – volume: 122 start-page: 1170 issue: 5 year: 2015 ident: 3830_CR4 publication-title: Anesthesiol doi: 10.1097/ALN.0000000000000596 – ident: 3830_CR12 doi: 10.1126/scitranslmed.aat8806 – volume: 11 start-page: 16 issue: 1 year: 2014 ident: 3830_CR15 publication-title: Theoret Biol Med Modell doi: 10.1186/1742-4682-11-16
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Snippet	Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical... Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical trigonometric... Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years, classical... Abstract Background Even though several computational methods for rhythmicity detection and analysis of biological data have been proposed in recent years,...
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SubjectTerms	Algorithms Analysis and modelling of complex systems Applications software Bioinformatics Biological analysis Biological research Biomedical and Life Sciences Chronobiology Circadian analysis Circadian rhythm Circadian rhythms Computational biology Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Computer programs Cosinor Data analysis Design of experiments Life Sciences Methods Microarrays Python Python (Programming language) Regression Regression analysis Regression models Rhythmicity analysis Rhythms Software Software packages Statistical analysis Time series
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Title	CosinorPy: a python package for cosinor-based rhythmometry
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