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

Gökcen Eraslan, Lukas M. Simon, Maria Mircea, Nikola S. Mueller and Fabian J. Theis ()
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Gökcen Eraslan: Helmholtz Zentrum München
Lukas M. Simon: Helmholtz Zentrum München
Maria Mircea: Helmholtz Zentrum München
Nikola S. Mueller: Helmholtz Zentrum München
Fabian J. Theis: Helmholtz Zentrum München

Nature Communications, 2019, vol. 10, issue 1, 1-14

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-018-07931-2

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