SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency

Abstract

The advance of single-cell RNA-sequencing (scRNA-seq) sheds light on cell-specific transcriptomic studies of cell developments, complex diseases, and cancers. Nevertheless, scRNA-seq techniques suffer from “dropout” events, and imputation tools are proposed to address the sparsity. Here, rather than imputation, we propose a tool, SMURF, to embed cells and genes into their latent space vectors utilizing matrix factorization with a mixture of Poisson-Gamma divergent as objective while preserving self-consistency. As for wet lab dataset evaluation, SMURF exhibited feasible cell subpopulation discovery efficacy with the latent vectors on all the eight-cell line mixtures. Furthermore, SMURF can embed the cell latent vectors into a 1D-oval and recover the time course of the cell cycle. SMURF can also serve as an imputation tool, the in silico data assessment shows that SMURF paraded the most robust gene expression recovery power with low root mean square error and high Pearson correlation. Moreover, SMURF recovers the gene distribution for the WM989 Drop-seq data. SMURF is available at https://github.com/deepomicslab/SMURF.