Abstract: Computer-implemented methods of classifying analyte data are provided. Methods of interest include applying a regression model to determine a relationship between an initial set of analyte features and a cluster criterion, generating a sparse set from at most a portion of the initial set of the analyte features based on the relationship, generating a classification model based on the sparse set, and applying the classification model to classify the analyte data into the clusters. Systems and non-transitory computer-readable storage media configured to carry out the subject methods are also provided.

Abstract: Aspects of the present disclosure include methods for calculating one or more cell parameters of cells in a sample. Methods according to certain embodiments include irradiating a sample comprising cells in a flow stream with frequency-modulated beams of light, measuring light from the cells in the sample, generating image data of the cells from the measured light and calculating one or more cell parameters based on the generated image data. Systems and integrated circuit devices (e.g., a field programmable gate array) for practicing the subject methods are also described. Non-transitory computer readable storage media are also provided.

Abstract: Aspects of the present disclosure include methods for assessing morphology of cell mitochondria (e.g., for use to determine viability of cells of a sample). Methods according to certain embodiments include measuring light from cells in a sample having fluorescently-labeled mitochondria, generating images of the cell mitochondria from the measured light, calculating an image parameter from the generated images of the cell mitochondria and assessing morphology of the cell mitochondria based on the calculated image parameter. Systems and integrated circuit devices (e.g., a field programmable gate array) for practicing the subject methods are also described. Non-transitory computer readable storage media are also provided.