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Article Abstract
Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Next, whether by deep learning or classical algorithms, image analysis pipelines commonly 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 key to image-based profiling. We demonstrate Pycytominer's usefulness in a machine-learning project to predict nuisance compounds that cause undesirable cell injuries.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12121495 | PMC |
| http://dx.doi.org/10.1038/s41592-025-02611-8 | DOI Listing |
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