RT Journal Article
SR Electronic
T1 Exploring Single-Cell Data with Deep Multitasking Neural Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 237065
DO 10.1101/237065
A1 Amodio, Matthew
A1 van Dijk, David
A1 Srinivasan, Krishnan
A1 Chen, William S
A1 Mohsen, Hussein
A1 Moon, Kevin R
A1 Campbell, Allison
A1 Zhao, Yujiao
A1 Wang, Xiaomei
A1 Venkataswamy, Manjunatha
A1 Desai, Anita
A1 V., Ravi
A1 Kumar, Priti
A1 Montgomery, Ruth
A1 Wolf, Guy
A1 Krishnaswamy, Smita
YR 2019
UL http://biorxiv.org/content/early/2019/01/03/237065.abstract
AB Biomedical researchers are generating high-throughput, high-dimensional single-cell data at a staggering rate. As costs of data generation decrease, experimental design is moving towards measurement of many different single-cell samples in the same dataset. These samples can correspond to different patients, conditions, or treatments. While scalability of methods to datasets of these sizes is a challenge on its own, dealing with large-scale experimental design presents a whole new set of problems, including batch effects and sample comparison issues. Currently, there are no computational tools that can both handle large amounts of data in a scalable manner (many cells) and at the same time deal with many samples (many patients or conditions). Moreover, data analysis currently involves the use of different tools that each operate on their own data representation, not guaranteeing a synchronized analysis pipeline. For instance, data visualization methods can be disjoint and mismatched with the clustering method. For this purpose, we present SAUCIE, a deep neural network that leverages the high degree of parallelization and scalability offered by neural networks, as well as the deep representation of data that can be learned by them to perform many single-cell data analysis tasks, all on a unified representation. A well-known limitation of neural networks is their interpretability. Our key contribution here are newly formulated regularizations (penalties) that render features learned in hidden layers of the neural network interpretable. When large multi-patient datasets are fed into SAUCIE, the various hidden layers contain denoised and batch-corrected data, a low dimensional visualization, unsupervised clustering, as well as other information that can be used to explore the data. We show this capability by analyzing a newly generated 180-sample dataset consisting of T cells from dengue patients in India, measured with mass cytometry. We show that SAUCIE, for the first time, can batch correct and process this 11-million cell data to identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue on the basis of single-cell measurements.