DR-GEM enables self-supervised machine learning for single-cell embeddings and annotations
Nature
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Researchers introduced DR-GEM, a self-supervised machine learning method that improves single-cell and spatial genomics analysis by better detecting rare cell types and filtering noise. DR-GEM outperforms existing techniques in embedding reliability and uncovering hidden genetic and spatial features.
