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Learning representations for image-based profiling of perturbations

View ORCID ProfileNikita Moshkov, Michael Bornholdt, Santiago Benoit, Matthew Smith, Claire McQuin, View ORCID ProfileAllen Goodman, Rebecca A. Senft, Yu Han, Mehrtash Babadi, View ORCID ProfilePeter Horvath, View ORCID ProfileBeth A. Cimini, View ORCID ProfileAnne E. Carpenter, View ORCID ProfileShantanu Singh, View ORCID ProfileJuan C. Caicedo
doi: https://doi.org/10.1101/2022.08.12.503783
Nikita Moshkov
1Biological Research Centre, Szeged, Hungary
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Michael Bornholdt
2Broad Institute of MIT and Harvard
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Santiago Benoit
3Carnegie Mellon University
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Matthew Smith
4Harvard College
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Claire McQuin
2Broad Institute of MIT and Harvard
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Allen Goodman
5Meta
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Rebecca A. Senft
2Broad Institute of MIT and Harvard
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Yu Han
2Broad Institute of MIT and Harvard
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Mehrtash Babadi
2Broad Institute of MIT and Harvard
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Peter Horvath
1Biological Research Centre, Szeged, Hungary
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Beth A. Cimini
2Broad Institute of MIT and Harvard
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Anne E. Carpenter
2Broad Institute of MIT and Harvard
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Shantanu Singh
2Broad Institute of MIT and Harvard
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Juan C. Caicedo
2Broad Institute of MIT and Harvard
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  • For correspondence: jcaicedo@broad.mit.edu
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Abstract

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data that highlight phenotypic outcomes. Here, we present an optimized strategy for learning representations of treatment effects from high-throughput imaging data, which follows a causal framework for interpreting results and guiding performance improvements. We use weakly supervised learning (WSL) for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with Cell Painting images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a WSL model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN-1. We conducted a comprehensive evaluation of our strategy on three publicly available Cell Painting datasets, discovering that representations obtained by the Cell Painting CNN-1 can improve performance in downstream analysis for biological matching up to 30% with respect to classical features, while also being more computationally efficient.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • - Revised introduction and abstract - Revised results section: added experiments with weak, median and strong treatments, revised figures. - Revised discussion section. - Added link to resources (model) and supplementary table. - Revised Methods section.

  • https://doi.org/10.5281/zenodo.7114558

  • https://github.com/cytomining/DeepProfiler

  • https://github.com/broadinstitute/DeepProfilerExperiments

  • https://cytomining.github.io/DeepProfiler-handbook/index.html

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 03, 2022.
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Learning representations for image-based profiling of perturbations
Nikita Moshkov, Michael Bornholdt, Santiago Benoit, Matthew Smith, Claire McQuin, Allen Goodman, Rebecca A. Senft, Yu Han, Mehrtash Babadi, Peter Horvath, Beth A. Cimini, Anne E. Carpenter, Shantanu Singh, Juan C. Caicedo
bioRxiv 2022.08.12.503783; doi: https://doi.org/10.1101/2022.08.12.503783
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Learning representations for image-based profiling of perturbations
Nikita Moshkov, Michael Bornholdt, Santiago Benoit, Matthew Smith, Claire McQuin, Allen Goodman, Rebecca A. Senft, Yu Han, Mehrtash Babadi, Peter Horvath, Beth A. Cimini, Anne E. Carpenter, Shantanu Singh, Juan C. Caicedo
bioRxiv 2022.08.12.503783; doi: https://doi.org/10.1101/2022.08.12.503783

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