Learning representations for image-based profiling of perturbations.
Nat Commun
; 15(1): 1594, 2024 Feb 21.
Article
en En
| MEDLINE
| ID: mdl-38383513
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.
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Banco de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
Idioma:
En
Año:
2024
Tipo del documento:
Article