Single-cell RNA-seq denoising using a deep count autoencoder

Eraslan G, Simon LM, Mircea M, Mueller NS, Theis FJ (2019)

Publication Type: Journal article

Publication year: 2019

Journal

Nature Communications Nature Publishing Group: Nature Communications

Book Volume: 10

Article Number: 390

Journal Issue: 1

DOI: 10.1038/s41467-018-07931-2

Abstract

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a negative binomial noise model with or without zero-inflation, and nonlinear gene-gene dependencies are captured. Our method scales linearly with the number of cells and can, therefore, be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE)

How to cite

APA:

Eraslan, G., Simon, L.M., Mircea, M., Mueller, N.S., & Theis, F.J. (2019). Single-cell RNA-seq denoising using a deep count autoencoder. Nature Communications, 10(1). https://doi.org/10.1038/s41467-018-07931-2

MLA:

Eraslan, Goekcen, et al. "Single-cell RNA-seq denoising using a deep count autoencoder." Nature Communications 10.1 (2019).

BibTeX: Download