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

By Hyunghoon Cho, Bonnie Berger, Jian Peng. 2018

50 citations recorded by the source corpus.

SUMMARY Visualization algorithms are fundamental tools for interpreting single-cell data. However, standard methods, such as t-stochastic neighbor embedding (t-SNE), are not scalable to datasets with millions of cells and the resulting visualizations cannot be generalized to analyze new datasets. Here we introduce net-SNE, a generalizable visualization approach that trains a neural network to learn a mapping function from high-dimensional single-cell gene-expression profiles to a low-dimensional visualization. We benchmark net-SNE on 13 different datasets, and show that it achieves visualization quality and clustering accuracy comparable with t-SNE. Additionally we show that the mapping function learned by net-SNE can accurately position entire new subtypes of cells from previously unseen datasets and can also be used to reduce the runtime of visualizing 1.3 million cells by 36-fold (from 1.5 days to an hour). Our work provides a framework for bootstrapping single-cell analysis from existing datasets.

Subfields: Biophysics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. J. Snyder-Cappione
2. Anna C. Belkina
3. Blaz Zupan
4. Pavlin Gregor Policar
5. Martin Stražar

In Molecular Biology

1. J. Snyder-Cappione
2. Josef Spidlen
3. Anna C. Belkina
4. Blaz Zupan
5. Pavlin Gregor Policar
6. Martin Stražar

Citations in the corpus

• Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, methodology): “Several attempts to successfully apply t-SNE-like methods to massive datasets have been recently reported including afore-noted HSNE 7,17,35 , LargeVis 8 , and net-SNE 36 .”