We present new methods for cell line classification using multivariate time series bioimpedance data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS technology, which monitors the attachment and spreading of mammalian cells in real time through the collection of electrical impedance data, has historically been used to study one cell line at a time. However, we show that if applied to data from multiple cell lines, ECIS can be used to classify unknown or potentially mislabeled cells, factors which have previously been associated with the reproducibility crisis in the biological literature. We assess a range of approaches to this new problem, testing different classification methods and deriving a dictionary of 29 features to characterize ECIS data. Most notably, our analysis enriches the current field by making use of simultaneous multi-frequency ECIS data, where previous studies have focused on only one frequency; using classification methods to distinguish multiple cell lines, rather than simple statistical tests that compare only two cell lines; and assessing a range of features derived from ECIS data based on their classification performance. In classification tests on fifteen mammalian cell lines, we obtain very high out-of-sample predictive accuracy. These preliminary findings provide a baseline for future large-scale studies in this field.

中文翻译：

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使用细胞-细胞基质阻抗传感（ECIS）进行细胞系分类。

我们提出了新的方法用于细胞系分类，该方法使用了从电细胞-基质阻抗感测（ECIS）技术获得的多元时间序列生物阻抗数据。ECIS技术通过收集电阻抗数据实时监测哺乳动物细胞的附着和扩散，过去一直用于一次研究一种细胞系。但是，我们表明，如果将ECIS应用于来自多个细胞系的数据，则可以用于对未知或潜在错误标记的细胞进行分类，这些因素以前与生物学文献中的再现性危机有关。我们评估了针对此新问题的多种方法，测试了不同的分类方法，并推导了29种特征字典来表征ECIS数据。最为显着地，我们的分析通过同时使用多频率ECIS数据来丰富了当前的领域，以前的研究仅集中在一个频率上。使用分类方法来区分多个细胞系，而不是仅比较两个细胞系的简单统计检验；并根据其分类性能评估从ECIS数据得出的一系列特征。在15种哺乳动物细胞系的分类测试中，我们获得了非常高的样本外预测准确性。这些初步发现为该领域未来的大规模研究提供了基线。并根据其分类性能评估从ECIS数据得出的一系列特征。在15种哺乳动物细胞系的分类测试中，我们获得了非常高的样本外预测准确性。这些初步发现为该领域未来的大规模研究提供了基线。并根据其分类性能评估从ECIS数据得出的一系列特征。在15种哺乳动物细胞系的分类测试中，我们获得了非常高的样本外预测准确性。这些初步发现为该领域未来的大规模研究提供了基线。