Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
Department of Automation, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China.
Nat Methods. 2019 Apr;16(4):311-314. doi: 10.1038/s41592-019-0353-7. Epub 2019 Mar 18.
Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.
近年来,大规模单细胞 RNA 测序技术的进步使得在异质组织中对表型不同的细胞状态进行精细描述成为可能。我们提出了 scScope,这是一种基于深度学习的可扩展方法,可以从数百万个嘈杂的单细胞基因表达谱中准确、快速地识别细胞类型组成。