Cell morphology-based machine learning models for human cell state classification.
Cell morphology-based machine learning models for human cell state classification.
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DOI：
10.1038/s41540-021-00180-y
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发表时间：
2021-05-26
影响因子：
4
通讯作者：
Bleris L
中科院分区：
文献类型：
--
作者：
Li Y;Nowak CM;Pham U;Nguyen K;Bleris L
在这里，我们实现和访问机器学习体系结构，以确定使用专门的正向(FSC)和侧向(SSC)散射流式细胞术信息来区分健康细胞和凋亡细胞的模型。为了生成训练数据，结直肠癌HCT116细胞受到miR-34a处理，然后使用传统的Annexin V/PI染色分析进行分类。凋亡细胞被定义为Annexin V阳性细胞，包括早期和晚期的凋亡细胞、坏死细胞以及其他死亡或死亡的细胞。除了荧光信号外，我们还从FSC和SSC参数中收集了细胞大小和粒度信息。这两个参数都细分为面积、高度和宽度，因此总共提供了六个数字特征，用于通知和训练我们的模型。对逻辑回归、随机森林、k近邻、多层感知器和支持向量机的集合进行了训练，并测试了仅使用上述六个数字特征预测小区状态的分类性能。在1046个候选模型中，选择了一个多层感知器，当应用于标准化数据时，其活精度为0.91，活召回率为0.93，活f值为0.92，ROC曲线下的活面积为0.97。我们讨论并强调分类器性能上的差异，并将结果与前向和侧向散射门控的标准实践进行比较，这些门控通常执行以基于大小和/或复杂性来选择单元。我们证明，我们的模型，一个随时可用的模块，用于任何基于流式细胞术的分析，可以提供自动、可靠和无染色的健康和凋亡细胞分类，仅使用大小和粒度信息。
Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information.
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影响因子：
3.7
作者：
Li, Yi;Nowak, Chance M.;Bleris, Leonidas
通讯作者：
Bleris, Leonidas
影响因子：
4.7
作者：
Guinn, Michael;Bleris, Leonidas
通讯作者：
Bleris, Leonidas
影响因子：
3.7
作者：
Fischer, Dennis;Buchbinder, Joern H.;Flassig, Robert J.
通讯作者：
Flassig, Robert J.
DOI：
10.1073/pnas.1507168112
发表时间：
2015-10-13
影响因子：
11.1
作者：
Kang, Taek;Moore, Richard;Bleris, Leonidas
通讯作者：
Bleris, Leonidas
影响因子：
38.3
作者：
Miyagi, Atsushi;Chipot, Christophe;Scheuring, Simon
通讯作者：
Scheuring, Simon