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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.13417 |
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| _version_ | 1866911030051864576 |
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| author | Serrano, Erik Chandrasekaran, Srinivas Niranj Bunten, Dave Brewer, Kenneth I. Tomkinson, Jenna Kern, Roshan Bornholdt, Michael Fleming, Stephen Pei, Ruifan Arevalo, John Tsang, Hillary Rubinetti, Vincent Tromans-Coia, Callum Becker, Tim Weisbart, Erin Bunne, Charlotte Kalinin, Alexandr A. Senft, Rebecca Taylor, Stephen J. Jamali, Nasim Adeboye, Adeniyi Abbasi, Hamdah Shafqat Goodman, Allen Caicedo, Juan C. Carpenter, Anne E. Cimini, Beth A. Singh, Shantanu Way, Gregory P. |
| author_facet | Serrano, Erik Chandrasekaran, Srinivas Niranj Bunten, Dave Brewer, Kenneth I. Tomkinson, Jenna Kern, Roshan Bornholdt, Michael Fleming, Stephen Pei, Ruifan Arevalo, John Tsang, Hillary Rubinetti, Vincent Tromans-Coia, Callum Becker, Tim Weisbart, Erin Bunne, Charlotte Kalinin, Alexandr A. Senft, Rebecca Taylor, Stephen J. Jamali, Nasim Adeboye, Adeniyi Abbasi, Hamdah Shafqat Goodman, Allen Caicedo, Juan C. Carpenter, Anne E. Cimini, Beth A. Singh, Shantanu Way, Gregory P. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_13417 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reproducible image-based profiling with Pycytominer Serrano, Erik Chandrasekaran, Srinivas Niranj Bunten, Dave Brewer, Kenneth I. Tomkinson, Jenna Kern, Roshan Bornholdt, Michael Fleming, Stephen Pei, Ruifan Arevalo, John Tsang, Hillary Rubinetti, Vincent Tromans-Coia, Callum Becker, Tim Weisbart, Erin Bunne, Charlotte Kalinin, Alexandr A. Senft, Rebecca Taylor, Stephen J. Jamali, Nasim Adeboye, Adeniyi Abbasi, Hamdah Shafqat Goodman, Allen Caicedo, Juan C. Carpenter, Anne E. Cimini, Beth A. Singh, Shantanu Way, Gregory P. Quantitative Methods 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. |
| title | Reproducible image-based profiling with Pycytominer |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2311.13417 |