Calico Life Sciences, LLC, South San Francisco, California 94080, USA.
Genome Res. 2021 Oct;31(10):1781-1793. doi: 10.1101/gr.268581.120. Epub 2021 Feb 24.
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.
注释细胞身份是单细胞基因组学实验分析中的常见瓶颈。在这里，我们提出了 scNym，这是一种半监督、对抗性神经网络，它可以学习将细胞身份注释从一个实验转移到另一个实验。scNym 利用了标记数据集和新的未标记数据集的信息，学习了丰富的细胞身份表示，从而实现了有效的注释转移。我们表明，尽管存在生物学和技术差异，scNym 仍能有效地在实验之间进行注释转移，其性能优于现有方法。我们还表明，scNym 模型可以从多个训练和目标数据集综合信息以提高性能。我们表明，除了高精度外，scNym 模型还可以通过显著性方法进行校准和解释。
