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arXiv · 2311.13417

Reproducible image-based profiling with Pycytominer

Erik Serrano·Srinivas Niranj Chandrasekaran·Dave Bunten·Kenneth I. Brewer·Jenna Tomkinson·Roshan Kern·Michael Bornholdt·Stephen Fleming·Ruifan Pei·John Arevalo·Hillary Tsang·Vincent Rubinetti·Callum Tromans-Coia·Tim Becker·Erin Weisbart·Charlotte Bunne·Alexandr A. Kalinin·Rebecca Senft·Stephen J. Taylor·Nasim Jamali·Adeniyi Adeboye·Hamdah Shafqat Abbasi·Allen Goodman·Juan C. Caicedo·Anne E. Carpenter·Beth A. Cimini·Shantanu Singh·Gregory P. Way
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Abstract

Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as image-based profiling. We demonstrate Pycytominers usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries.

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Erik Serrano, Srinivas Niranj Chandrasekaran, Dave Bunten, Kenneth I. Brewer, Jenna Tomkinson, Roshan Kern, Michael Bornholdt, Stephen Fleming, Ruifan Pei, John Arevalo, Hillary Tsang, Vincent Rubinetti, Callum Tromans-Coia, Tim Becker, Erin Weisbart, Charlotte Bunne, Alexandr A. Kalinin, Rebecca Senft, Stephen J. Taylor, Nasim Jamali, Adeniyi Adeboye, Hamdah Shafqat Abbasi, Allen Goodman, Juan C. Caicedo, Anne E. Carpenter, Beth A. Cimini, Shantanu Singh, Gregory P. Way. 2023-11-22. Reproducible image-based profiling with Pycytominer. https://doi.org/10.1038/s41592-025-02611-8

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