We consider cell line classification using multivariate time series data obtained from Electric Cell-substrate Impedance Sensing (ECIS) technology. The ECIS device, 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. We greatly broaden the scope of ECIS by considering simultaneous tracking of multiple cell lines in a search for new methods that can help mitigate the current reproducibility crisis in the biological literature. Our approach capitalizes on the multi-frequency data ECIS provides, which have been underutilized in previous studies . We consider classification of fifteen different mammalian cell lines, construct a dictionary of 29 different characteristic features, and obtain 95\% accuracy out-of-sample . Our preliminary findings provide a baseline for future large-scale studies in this field.
We consider cell line classification using multivariate time series data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS device, 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, which may help to mitigate the current crisis of reproducibility 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. Our analysis also makes use of simultaneous multi-frequency ECIS data, where previous studies have focused on only one frequency. In classification tests on fifteen mammalian cell lines, we obtain very high out-of-sample accuracy. These preliminary findings provide a baseline for future large-scale studies in this field.
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Electric Cell-substrate Impedance Sensing
electric cell-substrate impedance sensing
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We greatly broaden the scope of ECIS by considering simultaneous tracking of
However, we show that if applied to data from
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in a search for new methods that can help
, ECIS can be used to classify unknown or potentially mislabeled cells, which may help to
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reproducibility crisis
crisis of reproducibility
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Our approach capitalizes on the
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. Our analysis also makes use of simultaneous
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data ECIS provides, which have been underutilized in previous studies . We consider classification of fifteen different
ECIS data, where previous studies have focused on only one frequency. In classification tests on fifteen
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construct a dictionary of 29 different characteristic features, and obtain 95\% accuracy
we obtain very high
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accuracy. These