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Semi-supervised adversarial neural networks for single-cell classification
2021
Genome Research
Annotating cell identities is a common bottleneck in the analysis of single-cell genomics experiments. Here, we present scNym, a semi-supervised, adversarial neural network that learns to transfer cell identity annotations from one experiment to another. scNym takes advantage of information in both labeled datasets and new, unlabeled datasets to learn rich representations of cell identity that enable effective annotation transfer. We show that scNym effectively transfers annotations across
doi:10.1101/gr.268581.120
pmid:33627475
pmcid:PMC8494222
fatcat:o22a5tajd5aitgcrs3zxgndlty