Data from: The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition - Wiggins, Laura (Zenodo, 2023)
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 feature selection to identify variables that provide the greatest discrimination for the analysis in question. Using ensemble classification for accurate prediction of cellular phenotype and clustering algorithms for the characterisation of heterogeneous subsets, we validate and prove adaptability using different cell types and experimental conditions.
ROI files can be opened with ImageJ. Image .tif files can be opened with any imaging software including ImageJ. Feature tables are provided as comma separated files and can be opened with Excel, for example. Funding provided by: Biotechnology and Biological Sciences Research Council Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100000268 Award Number: BB/S507416/1
Keywords: cc-by-4.0; cell biology; machine learning; cancer; Open-source; software; phenotyping; automation; image analysis