Reproducible image-based profiling with Pycytominer

被引：0

作者：

Serrano, Erik ^{[1
]}

Chandrasekaran, Srinivas Niranj ^{[2
]}

Bunten, Dave ^{[1
]}

Brewer, Kenneth I.

Tomkinson, Jenna ^{[1
]}

Kern, Roshan ^{[1
,3
]}

Bornholdt, Michael ^{[2
]}

Fleming, Stephen J. ^{[4
]}

Pei, Ruifan ^{[2
]}

Arevalo, John ^{[2
]}

Tsang, Hillary ^{[2
]}

Rubinetti, Vincent ^{[1
]}

Tromans-Coia, Callum ^{[2
]}

Becker, Tim ^{[2
]}

Weisbart, Erin ^{[2
]}

Bunne, Charlotte ^{[2
]}

Kalinin, Alexandr A. ^{[2
]}

Senft, Rebecca ^{[2
]}

Taylor, Stephen J. ^{[1
]}

Jamali, Nasim ^{[2
]}

Adeboye, Adeniyi ^{[2
]}

Abbasi, Hamdah Shafqat ^{[2
]}

Goodman, Allen ^{[2
,5
]}

Caicedo, Juan C. ^{[2
,6
]}

Carpenter, Anne E. ^{[2
]}

Cimini, Beth A. ^{[2
]}

Singh, Shantanu ^{[2
]}

Way, Gregory P. ^{[1
]}

机构：

[1] Univ Colorado, Sch Med, Dept Biomed Informat, Aurora, CO 80045 USA

[2] Broad Inst MIT & Harvard, Imaging Platform, Cambridge, MA 02138 USA

[3] Case Western Reserve Univ, Cleveland, OH USA

[4] Broad Inst MIT & Harvard, Data Sci Platform, Cambridge, MA USA

[5] Genentech gRED, South San Francisco, CA USA

[6] Univ Wisconsin, Mor, Madison, WI USA

来源：

NATURE METHODS | 2025年 / 22卷 / 04期

关键词：

D O I：

10.1038/s41592-025-02611-8

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

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.

引用

页数：13

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