计算机科学
深度学习
模式识别(心理学)
深层神经网络
监督学习
计算生物学
作者
Jacob C. Kimmel,David R. Kelley
出处
期刊:Genome Research
[Cold Spring Harbor Laboratory Press]
日期:2021-02-24
卷期号:31 (10): 1781-1793
被引量:12
标识
DOI:10.1101/gr.268581.120
摘要
Annotating cell identities is a common bottleneck in the analysis of single-cell genomics experiments. Here, we present scNym, a semisupervised, adversarial neural network that learns to transfer cell identity annotations from one experiment to another. scNym takes advantage of information in both labeled data sets and new, unlabeled data sets to learn rich representations of cell identity that enable effective annotation transfer. We show that scNym effectively transfers annotations across experiments despite biological and technical differences, achieving performance superior to existing methods. We also show that scNym models can synthesize information from multiple training and target data sets to improve performance. We show that in addition to high accuracy, scNym models are well calibrated and interpretable with saliency methods.
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