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Depositordc.contributorSwain, Peter
Funderdc.contributor.otherBBSRC - Biotechnology and Biological Sciences Research Councilen_UK
Funderdc.contributor.otherEPSRC - Engineering and Physical Sciences Research Councilen_UK
Funderdc.contributor.otherMRC - Medical Research Councilen_UK
Funderdc.contributor.otherWellcome Trusten_UK
Funderdc.contributor.otherCIHR - Canadian Institutes of Health Researchen_UK
Data Creatordc.creatorSwain, Peter
Data Creatordc.creatorStevenson, Keiran
Data Creatordc.creatorLeary, Allen
Data Creatordc.creatorMontano-Gutierrez, Luis Fernando
Data Creatordc.creatorClark, Ivan
Data Creatordc.creatorVogel, Jackie
Data Creatordc.creatorPilizota, Teuta
Date Accessioneddc.date.accessioned2016-05-12T11:47:25Z
Date Availabledc.date.available2016-05-12T11:47:25Z
Citationdc.identifier.citationSwain, Peter; Stevenson, Keiran; Leary, Allen; Montano-Gutierrez, Luis Fernando; Clark, Ivan; Vogel, Jackie; Pilizota, Teuta. (2016). Inferring time derivatives, including cell growth rates, using Gaussian processes, [dataset]. University of Edinburgh. School of Biological Sciences. http://dx.doi.org/10.7488/ds/1405.en
Persistent Identifierdc.identifier.urihttp://hdl.handle.net/10283/2004
Persistent Identifierdc.identifier.urihttp://dx.doi.org/10.7488/ds/1405
Dataset Description (abstract)dc.description.abstractOften the time-derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population’s growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time-derivatives as a function of time from time-series data. Our approach is based on established properties of Gaussian processes and therefore applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, allows interpolation with the corresponding error estimation, and can be applied to any number of experimental replicates. As illustrations, we infer growth rate from measurements of the optical density of populations of microbial cells and estimate the rate of in vitro assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies in a single yeast cell. Being accessible through both a GUI and from scripts, our algorithm should have broad application across the sciences.en_UK
Dataset Description (TOC)dc.description.tableofcontentsData from Figures 2 and 3, see readme.txten_UK
Languagedc.language.isoengen_UK
Publisherdc.publisherUniversity of Edinburgh. School of Biological Sciencesen_UK
Relation (Is Referenced By)dc.relation.isreferencedbyhttp://dx.doi.org/10.1038/ncomms13766
Relation (Is Referenced By)dc.relation.isreferencedbySwain, P, Stevenson, K, Leary, A, Montano-Gutierrez, LF, Clark, IBN, Vogel, J & Pilizota, T 2016, 'Inferring time-derivatives including cell growth rates using Gaussian processes' Nature Communications. DOI: 10.1038/ncomms13766
Subjectdc.subjecttime-derivativeen_UK
Subjectdc.subjectgrowth rateen_UK
Subjectdc.subjectGaussian processen_UK
Subject Classificationdc.subject.classificationBiological Sciencesen_UK
Titledc.titleInferring time derivatives, including cell growth rates, using Gaussian processesen_UK
Typedc.typedataseten_UK

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