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A high throughput machine-learning driven analysis of Ca2+ spatio-temporal maps.
Cell Calcium ( IF 4.3 ) Pub Date : 2020-07-28 , DOI: 10.1016/j.ceca.2020.102260
Wesley A Leigh 1 , Guillermo Del Valle 1 , Sharif Amit Kamran 2 , Bernard T Drumm 3 , Alireza Tavakkoli 2 , Kenton M Sanders 1 , Salah A Baker 1
Affiliation  

High-resolution Ca2+ imaging to study cellular Ca2+ behaviors has led to the creation of large datasets with a profound need for standardized and accurate analysis. To analyze these datasets, spatio-temporal maps (STMaps) that allow for 2D visualization of Ca2+ signals as a function of time and space are often used. Methods of STMap analysis rely on a highly arduous process of user defined segmentation and event-based data retrieval. These methods are often time consuming, lack accuracy, and are extremely variable between users. We designed a novel automated machine-learning based plugin for the analysis of Ca2+ STMaps (STMapAuto). The plugin includes optimized tools for Ca2+ signal preprocessing, automated segmentation, and automated extraction of key Ca2+ event information such as duration, spatial spread, frequency, propagation angle, and intensity in a variety of cell types including the Interstitial cells of Cajal (ICC). The plugin is fully implemented in Fiji and able to accurately detect and expeditiously quantify Ca2+ transient parameters from ICC. The plugin’s speed of analysis of large-datasets was 197-fold faster than the commonly used single pixel-line method of analysis. The automated machine-learning based plugin described dramatically reduces opportunities for user error and provides a consistent method to allow high-throughput analysis of STMap datasets.



中文翻译:


Ca2+ 时空图的高通量机器学习驱动分析。



用于研究细胞 Ca 2+行为的高分辨率 Ca 2+成像导致了大型数据集的创建,迫切需要标准化和准确的分析。为了分析这些数据集,经常使用时空图 (STMap),它可以将 Ca 2+信号作为时间和空间的函数进行二维可视化。 STMap 分析方法依赖于用户定义的分段和基于事件的数据检索的高度艰巨的过程。这些方法通常非常耗时、缺乏准确性,并且在用户之间差异很大。我们设计了一种新颖的基于自动化机器学习的插件,用于分析 Ca 2+ STMaps (STMapAuto)。该插件包括用于 Ca 2+信号预处理、自动分割和自动提取关键 Ca 2+事件信息(例如各种细胞类型(包括间质细胞)的持续时间、空间分布、频率、传播角度和强度)的优化工具。卡哈尔(国际刑事法院)。该插件已在斐济全面实施,能够准确检测并快速量化 ICC 的 Ca 2+瞬态参数。该插件分析大型数据集的速度比常用的单像素行分析方法快 197 倍。所描述的基于自动化机器学习的插件极大地减少了用户出错的机会,并提供了一致的方法来允许对 STMap 数据集进行高通量分析。

更新日期:2020-08-11
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