Reproducible image-based profiling with Pycytominer
/ Authors
Erik Serrano, S. Chandrasekaran, Dave Bunten, Kenneth I. Brewer, Jenna Tomkinson, Roshan Kern, Michael Bornholdt, Stephen J Fleming, Ruifan Pei, John Arevalo
and 18 more authors
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, H. Abbasi, A. Goodman, Juan C. Caicedo, Anne E Carpenter, B. Cimini, Shantanu Singh, Gregory P. Way
/ Abstract
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 Pycytominer’s usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries.
Journal: ArXiv