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Exploring single-cell data with deep multitasking neural networks
Author(s) -
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
Publication year - 2019
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-019-0576-7
Subject(s) - computer science , artificial intelligence , human multitasking , scalability , cluster analysis , artificial neural network , deep learning , leverage (statistics) , pattern recognition (psychology) , data mining , visualization , sample (material) , representation (politics) , deep neural networks , machine learning , database , biology , chemistry , chromatography , neuroscience , politics , political science , law
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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