Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to technical factors, including the number of molecules detected in each cell, which can confound biological heterogeneity with technical effects. To address this, we present a modeling framework for the normalization and variance stabilization of molecular count data from scRNA-seq experiments. We propose that the Pearson residuals from "regularized negative binomial regression," where cellular sequencing depth is utilized as a covariate in a generalized linear model, successfully remove the influence of technical characteristics from downstream analyses while preserving biological heterogeneity. Importantly, we show that an…

• NY
  New York State Department of Health
• CI
  California Institute of Technology
• NY
  New York Genome Center
• DF
  Deutsche Forschungsgemeinschaft

  Award: 328558384
• NI
  National Institutes of Health

  Award: 1OT2OD026673-01