Classifying Contaminated Cell Cultures using Time Series Features
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We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. We perform feature-based classification, extracting interpretable features from the ECIS time courses. We can achieve high classification accuracy using only two features, which depend on the cell line under examination. Initial results also show the existence of experimental variation between plates and suggest types of features that may prove more robust to such variation. Our paper is the first to perform a broad examination of ECIS time course features in the context of detecting contamination, and to describe and suggest possibilities for ameliorating plate-to-plate variation.
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