期刊
NATURE METHODS
卷 16, 期 11, 页码 1139-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41592-019-0576-7
关键词
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资金
- Indo-U.S. Vaccine Action Program
- National Institute of Allergy and Infectious Diseases of the NIH [AI089992]
- Chan-Zuckerberg Initiative [182702]
- IVADO (L'institut de valorisation des donnees)
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.
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