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<p>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.</p>

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Reproducible image-based profiling with Pycytominer

  • 期刊论文

  • 发表日期：

    2025年3月3日

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  • Erik Serrano,
  • Srinivas Niranj Chandrasekaran,
  • Dave Bunten,
  • Kenneth I. Brewer,
  • Jenna Tomkinson,
  • Roshan Kern,
  • Michael Bornholdt,
  • Stephen J. 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

摘要

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.