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bioRxiv · 10.1101/289223

Neural Data Visualization for Scalable and Generalizable Single Cell Analysis

Cho, H.·Berger, B.·Peng, J.
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Abstract

Single-cell RNA sequencing is becoming effective and accessible as emerging technologies push its scale to millions of cells and beyond. Visualizing the landscape of single cell expression has been a fundamental tool in single cell analysis. However, standard methods for visualization, such as t-stochastic neighbor embedding (t-SNE), not only lack scalability to data sets with millions of cells, but also are unable to generalize to new cells, an important ability for transferring knowledge across fast-accumulating data sets. We introduce net-SNE, which trains a neural network to learn a high quality visualization of single cells that newly generalizes to unseen data. While matching the visualization quality of t-SNE on 14 benchmark data sets of varying sizes, from hundreds to 1.3 million cells, net-SNE also effectively positions previously unseen cells, even when an entire subtype is missing from the initial data set or when the new cells are from a different sequencing experiment. Furthermore, given a \"reference\" visualization, net-SNE can vastly reduce the computational burden of visualizing millions of single cells from multiple days to just a few minutes of runtime. Our work provides a general framework for newly bootstrapping single cell analysis from existing data sets.

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Cho, H., Berger, B., Peng, J.. 2018-03-27. Neural Data Visualization for Scalable and Generalizable Single Cell Analysis. https://doi.org/10.1101/289223

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