Maciej Dobrzyński, Marc-Antoine Jacques, Olivier Pertz
{"title":"Mining single-cell time-series datasets with Time Course Inspector.","authors":"Maciej Dobrzyński, Marc-Antoine Jacques, Olivier Pertz","doi":"10.1093/bioinformatics/btz846","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>Thanks to recent advances in live cell imaging of biosensors, microscopy experiments can generate thousands of single-cell time-series. To identify sub-populations with distinct temporal behaviours that correspond to different cell fates, we developed Time Course Inspector (TCI)-a unique tool written in R/Shiny to combine time-series analysis with clustering. With TCI it is convenient to inspect time-series, plot different data views and remove outliers. TCI facilitates interactive exploration of various hierarchical clustering and cluster validation methods. We showcase TCI by analysing a single-cell signalling time-series dataset acquired using a fluorescent biosensor.</p><p><strong>Availability and implementation: </strong>https://github.com/pertzlab/shiny-timecourse-inspector.</p><p><strong>Supplementary information: </strong>Supplementary data are available at Bioinformatics online.</p>","PeriodicalId":90576,"journal":{"name":"Journal of bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1093/bioinformatics/btz846","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btz846","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
Summary: Thanks to recent advances in live cell imaging of biosensors, microscopy experiments can generate thousands of single-cell time-series. To identify sub-populations with distinct temporal behaviours that correspond to different cell fates, we developed Time Course Inspector (TCI)-a unique tool written in R/Shiny to combine time-series analysis with clustering. With TCI it is convenient to inspect time-series, plot different data views and remove outliers. TCI facilitates interactive exploration of various hierarchical clustering and cluster validation methods. We showcase TCI by analysing a single-cell signalling time-series dataset acquired using a fluorescent biosensor.
Availability and implementation: https://github.com/pertzlab/shiny-timecourse-inspector.
Supplementary information: Supplementary data are available at Bioinformatics online.