Exploring Single-Cell Data with Deep Multitasking Neural Networks

Abstract

Handling the vast amounts of single-cell RNA-sequencing and CyTOF data, which are now being generated in patient cohorts, presents a computational challenge due to the noise, complexity, sparsity and batch effects present. Here, we propose a unified deep neural network-based approach to automatically process and extract structure from these massive datasets. Our unsupervised architecture, called SAUCIE (Sparse Autoencoder for Unsupervised Clustering, Imputation, and Embedding), simultaneously performs several key tasks for single-cell data analysis including 1) clustering, 2) batch correction, 3) visualization, and 4) denoising/imputation. SAUCIE is trained to recreate its own input after reducing its dimensionality in a 2-D embedding layer which can be used to visualize the data. Additionally, it uses two novel regularizations: (1) an information dimension regularization to penalize entropy as computed on normalized activation values of the layer, and thereby encourage binary-like encodings that are amenable to clustering and (2) a Maximal Mean Discrepancy penalty to correct batch effects. Thus SAUCIE has a single architecture that denoises, batch-corrects, visualizes and clusters data using a unified 1 . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a

Advancing systems immunology through data-driven statistical analysis

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Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks

“…Notably, Amodio et al (2017), concurrently with this work, introduced an autoencoder-based approach for jointly performing batch effect correction and visualization, which finds a parametric embedding like net-SNE. Because they optimize a different objective function, however, the behavior of their embedding is fundamentally different from that of t-SNE (and thus net-SNE).…”

Enhancing scientific discoveries in molecular biology with deep generative models

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