Wiggins, Laura
2023-02-24
<p>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.</p>
<p>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.</p><p>Funding provided by: Biotechnology and Biological Sciences Research Council<br>Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100000268<br>Award Number: BB/S507416/1</p>
https://doi.org/10.5281/zenodo.7674584
oai:zenodo.org:7674584
Zenodo
https://www.researchsquare.com/article/rs-971415/v1
https://doi.org/10.5281/zenodo.7620171
https://cellphegui.shinyapps.io/app_to_host/
https://doi.org/10.5061/dryad.4xgxd25f0
https://zenodo.org/communities/dryad
https://doi.org/10.5281/zenodo.7674583
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cell biology
machine learning
cancer
Open-source
software
phenotyping
automation
image analysis
Data from: The CellPhe toolkit for cell phenotyping using time-lapse imaging and pattern recognition
info:eu-repo/semantics/other