Article

Self-distillation contrastive learning enables clustering-free signature extraction and mapping to multimodal single-cell atlas of multimillion scale

2021-11-09

Abstract excerpt

Massively generated single-cell multi-omics datasets are revolutionizing biological studies of heterogenous tissues and organisms, which necessitate powerful computational methods to unleash the full potential of these tremendous data. Here, we present Concerto, stands for self-distillation contrastive learning of cell representations, a self-supervised representation learning framework optimized with asymmetric t...

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Literature Corpus work: 5165b199-f114-527b-adda-689535c42528
DOI: 10.21203/rs.3.rs-841909/v1

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