Contrastive learning for robust cell annotation and representation from single-cell transcriptomics

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In this study, we present a novel deep learning approach using contrastive learning and a carefully designed loss function for learning an generalizable embedding space from scRNA-Seq data. We call this model CELLULAR: CELLUlar contrastive Learning for Annotation and Representation.

Recommended citation: Andrekson, Leo et al. (2024) “Contrastive learning for robust cell annotation and representation from single-cell transcriptomics.” bioRxiv.