By Saket Choudhary, New York Genome Center

Abstract 

Single-cell RNA-sequencing (scRNA-seq) has emerged as a powerful technique to characterize cellular diversity at an unprecedented resolution, enabling the characterization of the molecular state of individual cells in any biological system or species. While unsupervised analysis of single-cell data can uncover heterogeneous cell types and states, the results can also be confounded by cell-to-cell variation arising from technical factors such as differences in sequencing depths. I will introduce a computational method based on generalized linear models that tackles the normalization problem using a data-driven approach. By analyzing over 50 scRNA-seq datasets spanning multiple technologies, biological systems, and sequencing depths, I will show that the degree of heterogeneity varies across datasets which necessitates a data-driven parameter learning approach.  Using extensive benchmarking, I also demonstrate how the method outperforms other tools at identifying differentially expressed genes.