The article introduces CytoSummaryNet, a Deep Sets-based approach that uses self-supervised contrastive learning in a multiple-instance learning framework to improve image-based cell profiling. The key insights are:
Typical cell profiling methods represent a sample by averaging across cells, failing to capture the heterogeneity within cell populations. CytoSummaryNet addresses this by learning a representation that preserves the diversity of single-cell features.
CytoSummaryNet achieves a 30-68% improvement in mean average precision for mechanism of action prediction compared to average profiling on a public dataset.
Interpretability analysis suggests the model achieves this by downweighting small mitotic cells or those with debris, and prioritizing large uncrowded cells.
The approach requires only perturbation labels for training, which are readily available in all cell profiling datasets, making it an easy-to-apply method for aggregating single-cell feature data.
CytoSummaryNet offers a straightforward post-processing step for single-cell profiles that can significantly boost retrieval performance on image-based profiling datasets.
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by van Dijk,R.,... 於 www.biorxiv.org 11-16-2023
https://www.biorxiv.org/content/10.1101/2023.11.14.567038v2深入探究