The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition.

ABSTRACT: With phenotypic heterogeneity in whole cell populations widely recognised, the demand for quantitative and temporal analysis approaches to characterise single cell morphology and dynamics has increased. We present CellPhe, a pattern recognition toolkit for the unbiased characterisation of cellular phenotypes within time-lapse videos. CellPhe imports tracking information from multiple segmentation and tracking algorithms to provide automated cell phenotyping from different imaging modalities, including fluorescence. To maximise data quality for downstream analysis, our toolkit includes automated recognition and removal of erroneous cell boundaries induced by inaccurate tracking and segmentation. We provide an extensive list of features extracted from individual cell time series, with custom

SUBMITTER: Wiggins L 

PROVIDER: S-EPMC10070448 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature