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Nat Methods. 2019 Nov;16(11):1139-1145. doi: 10.1038/s41592-019-0576-7. Epub 2019 Oct 7.

Exploring single-cell data with deep multitasking neural networks.

Author information

1
Department of Computer Science, Yale University, New Haven, CT, USA.
2
Department of Genetics, Yale University, New Haven, CT, USA.
3
School of Medicine, Yale University, New Haven, CT, USA.
4
Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.
5
Department of Mathematics and Statistics, Utah State University, Logan, UT, USA.
6
Department of Rheumatology, Yale University, New Haven, CT, USA.
7
Department of Neurovirology, NIMHANS, Bangalore, India.
8
Department of Microbial Pathogenesis, Yale University, New Haven, CT, USA.
9
Department of Mathematics and Statistics, Université de Montréal, Montréal, Quebec, Canada.
10
Mila - Quebec Artificial Intelligence Institute, Montréal, Quebec, Canada.
11
Department of Computer Science, Yale University, New Haven, CT, USA. smita.krishnaswamy@yale.edu.
12
Department of Genetics, Yale University, New Haven, CT, USA. smita.krishnaswamy@yale.edu.

Abstract

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.

PMID:
31591579
DOI:
10.1038/s41592-019-0576-7

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