bioRxivGenomicsPreprint
Fast, scalable and accurate differential expression analysis for single cells
Debarka Sengupta, Nirmala Arul Rayan, Michelle Lim, Bing Lim, Shyam Prabhakar
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.
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