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

We propose a novel variational auto-encoder-based method 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 show for several scRNA-seq data sets that our method outperforms recently proposed scRNA-seq methods in clustering cells. Our software tool scVAE has support for several count likelihood functions and a variant of the variational auto-encoder has a priori clustering in the latent space.