Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning

Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles. scScope uses a recurrent network to remove batch effects and iteratively impute zero values in scRNA-seq data.