scVAE: variational auto-encoders for single-cell gene expression data

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Abstract
Motivation: Models for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter choices. With deep generative models one can work directly with count data, make likelihood-based model comparison, learn a latent representation of the cells and capture more of the variability in different cell populations. Results: We propose a novel method based on variational auto-encoders (VAEs) for analysis of single-cell RNA sequencing (scRNA-seq) data. It avoids data preprocessing by using raw count data as input and can robustly estimate the expected gene expression levels and a latent representation for each cell. We tested several count likelihood functions and a variant of the VAE that has a priori clustering in the latent space. We show for several scRNA-seq datasets that our method outperforms recently proposed scRNA-seq methods in clustering cells and that the resulting clusters reflect cell types. Availability and implementation: Our method, called scVAE, is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https://github.com/scvae/scvae. Supplementary information: Supplementary data are available at Bioinformatics online.
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Funding Information
  • Lundbeck Foundation (R190 2014-3904)
  • Novo Nordisk Foundation (NNF18CC0034900)
  • Novo Nordisk Foundation Center for Basic Metabolic Research (NNF16OC0021496)
  • Danish Ministry of Higher Education and Science [Elite Research Travel Grant 2018]

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