Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

Abstract

Although single-cell transcriptome data are increasingly available, their interpretation remains a challenge. Here, the authors present a dimensionality reduction approach that preserves both the local and global neighbourhood structures in the data thus enhancing its interpretability.