计算机科学
人工智能
人类多任务处理
可扩展性
聚类分析
人工神经网络
杠杆(统计)
数据挖掘
样品(材料)
模式识别(心理学)
机器学习
可视化
深度学习
深层神经网络
数据库
化学
色谱法
心理学
认知心理学
作者
Matthew Amodio,David van Dijk,K. Srinivasan,William S. Chen,Hussein Mohsen,Kevin R. Moon,Allison M. Campbell,Yujiao Zhao,Xiaomei Wang,Manjunatha M. Venkataswamy,Anita Desai,Vasanthapuram Ravi,Priti Kumar,Ruth R. Montgomery,Guy Wolf,Smita Krishnaswamy
出处
期刊:Nature Methods
[Springer Nature]
日期:2019-10-07
卷期号:16 (11): 1139-1145
被引量:211
标识
DOI:10.1038/s41592-019-0576-7
摘要
It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that combines parallelization and scalability offered by neural networks, with the deep representation of data that can be learned by them to perform many single-cell data analysis tasks. Our regularizations (penalties) render features learned in hidden layers of the neural network interpretable. On large, multi-patient datasets, SAUCIE’s various hidden layers contain denoised and batch-corrected data, a low-dimensional visualization and unsupervised clustering, as well as other information that can be used to explore the data. We analyze a 180-sample dataset consisting of 11 million T cells from dengue patients in India, measured with mass cytometry. SAUCIE can batch correct and identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue. SAUCIE, a deep learning platform to analyze single-cell data across samples and platforms, allows information to be obtained from the internal layers of the network, which provides additional mechanistic understanding that can be used to further tune data analysis.
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