利用 Time Course Inspector 挖掘单细胞时间序列数据集。

Maciej Dobrzyński, Marc-Antoine Jacques, Olivier Pertz
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引用次数: 13

摘要

摘要:由于生物传感器活细胞成像技术的最新进展,显微镜实验可以生成成千上万的单细胞时间序列。为了识别与不同细胞命运相对应的具有不同时间行为的亚群,我们开发了时间历程检查器(TCI)--一种用 R/Shiny 编写的独特工具,将时间序列分析与聚类相结合。利用 TCI,可以方便地检查时间序列、绘制不同的数据视图并移除异常值。TCI 可促进对各种分层聚类和聚类验证方法的交互式探索。我们通过分析使用荧光生物传感器获取的单细胞信号时间序列数据集来展示 TCI。可用性和实现:https://github.com/pertzlab/shiny-timecourse-inspector.Supplementary 信息:补充数据可在 Bioinformatics online 上获取。
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Mining single-cell time-series datasets with Time Course Inspector.

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.

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