Deep Generative Autoencoder For Low-Dimensional Embeding Extraction From Single-Cell Rnaseq Data

Single-cell RNA sequencing (scRNAseq) can reveal biological diversity at the cellular level that are unexplored by bulk RNA sequencing (RNAseq), but they suffer from the excessive zero expression counts and the limitation of the scalability in practice. Here, we propose a non-linear generative autoencoder based method, scSVA, relying on an integration of variational autoencoder and dropout imputations. Specifically, scSVA automatically identifies the dropouts and recovery these values only to avoid introducing new biases. Then, scSVA utilizes stochastic optimization and deep neural network to extract the low-dimensional embedding from gene expression levels. We illustrate the benefits of scSVA through in-depth real analyses of six published scRNAseq data sets. scSVA is up to 1.3 times more powerful cell clustering accuracy than existing approaches. The high power of scSVA allows us to identify new cell types that reveal new biology from scRNAseq data that otherwise cannot be revealed by existing approaches.