Learning representations for image-based profiling of perturbations

Moshkov, Nikita and Bornholdt, Michael and Benoit, Santiago and Smith, Matthew and Mcquin, Claire and Goodman, Allen and Senft, Rebecca A. and Han, Yu and Babadi, Mehrtash and Horváth, Péter and Cimini, Beth A. and Carpenter, Anne E. and Singh, Shantanu and Caicedo, Juan C. (2024) Learning representations for image-based profiling of perturbations. NATURE COMMUNICATIONS, 15 (1). ISSN 2041-1723

Preview

Text MoshkovN_Learning.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview

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. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning 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 images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient. Assessing cell phenotypes in image-based assays requires solid computational methods for transforming images into quantitative data. Here, the authors present a strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation.

Actions (login required)

Edit Item