CellPyAbility: automated image analysis for high-throughput dose-response screening

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ID: 320647
2026
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Abstract
Abstract Summary Nuclei counting provides a low-cost, metabolic-independent alternative to ATP- or tetrazolium-based cell viability assays. However, the fragmentation of image processing, normalization, and statistical modeling across multiple software platforms hinders high-throughput adoption. We present CellPyAbility, a Python-based suite that automates image processing, dose-response fitting, and synergy analysis. It converts unedited whole-well images into publication-ready graphics in under one minute per 96-well plate on standard desktop hardware. Availability and Implementation CellPyAbility is open-source (MIT License) and available as a Python package via PyPI and Bioconda, or as a code-free application for macOS and Windows. Source code and documentation are available at https://github.com/bindralab/cellpyability and on Zenodo at https://doi.org/10.5281/zenodo.20693745. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7168034541 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors James L. Elia, Sam Friedman, Ranjit S. Bindra
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag513
URL
https://doi.org/10.1093/bioinformatics/btag513
Keywords Keywords not found

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