Fast, scalable and accurate differential expression analysis for single cells

ABSTRACT

Analysis of single-cell RNA-seq data is challenging due to technical variability, high noise levels and massive sample sizes. Here, we describe a normalization technique that substantially reduces technical variability and improves the quality of downstream analyses. We also introduce a nonparametric method for detecting differentially expressed genes that scales to > 1,000 cells and is both more accurate and ~10 times faster than existing parametric approaches.