Journal article
scVAE: Variational auto-encoders for single-cell gene expression data
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.
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 data sets that our method outperforms recently proposed scRNA-seq methods in clustering cells and that the resulting clusters reflect cell types. 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 data are available at Bioinformatics online.
Language: | English |
---|---|
Publisher: | Oxford University Press |
Year: | 2020 |
Pages: | 4415-4422 |
ISSN: | 14602059 , 02667061 , 13674803 and 13674811 |
Types: | Journal article |
DOI: | 10.1093/bioinformatics/btaa293 |
ORCIDs: | 0000-0003-3036-3219 , 0000-0002-0352-4323 , 0000-0003-0207-4831 , Winther, Ole and Vording, Maximillian Fornitz |