Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning.

ABSTRACT: Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30-68% in mean average precision compared to average profiling on a public dataset. CytoSummaryNet uses self-supervised contrastive learning in a multiple-instance learning framework, providing an easier-to-apply method for aggregating single-cell feature data than previously published strategies. Interpretability analysis suggests that the model achieves this improvement by downweighting smal

SUBMITTER: van Dijk R 

PROVIDER: S-EPMC11611260 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature