  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.
