Reproducible image-based profiling with Pycytominer
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 applicati...
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| Published in | Nature methods Vol. 22; no. 4; pp. 677 - 680 |
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| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.04.2025
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1548-7091 1548-7105 1548-7105 |
| DOI | 10.1038/s41592-025-02611-8 |
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| 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.
Pycytominer is a user-friendly, open-source Python package that carries out key bioinformatics steps in image-based profiling. |
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| AbstractList | 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. 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.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. 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. Pycytominer is a user-friendly, open-source Python package that carries out key bioinformatics steps in image-based profiling. 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.Pycytominer is a user-friendly, open-source Python package that carries out key bioinformatics steps in image-based profiling. |
| Author | Chandrasekaran, Srinivas Niranj Kalinin, Alexandr A. Tsang, Hillary Senft, Rebecca Fleming, Stephen J. Serrano, Erik Taylor, Stephen J. Brewer, Kenneth I. Jamali, Nasim Tomkinson, Jenna Becker, Tim Kern, Roshan Pei, Ruifan Caicedo, Juan C. Cimini, Beth A. Bunne, Charlotte Adeboye, Adeniyi Carpenter, Anne E. Rubinetti, Vincent Tromans-Coia, Callum Way, Gregory P. Abbasi, Hamdah Shafqat Singh, Shantanu Goodman, Allen Arevalo, John Bornholdt, Michael Weisbart, Erin Bunten, Dave |
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| References | 2611_CR1 J Schindelin (2611_CR2) 2012; 9 C Wolff (2611_CR17) 2024 2611_CR9 JL Dahlin (2611_CR19) 2023; 14 2611_CR13 2611_CR14 2611_CR11 2611_CR12 2611_CR20 K Schorpp (2611_CR8) 2023; 9 D Krentzel (2611_CR6) 2023; 33 JC Caicedo (2611_CR5) 2017; 14 SN Chandrasekaran (2611_CR16) 2024; 21 GP Way (2611_CR10) 2022; 13 2611_CR18 J Arevalo (2611_CR15) 2024; 15 DR Stirling (2611_CR3) 2021; 22 C Scheeder (2611_CR4) 2018; 10 F Vincent (2611_CR7) 2022; 21 38045474 - ArXiv. 2024 Jul 2:arXiv:2311.13417v2. |
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| Title | Reproducible image-based profiling with Pycytominer |
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