Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data.

ABSTRACT:

Background

Standard preprocessing of single-cell RNA-seq UMI data includes normalization by sequencing depth to remove this technical variability, and nonlinear transformation to stabilize the variance across genes with different expression levels. Instead, two recent papers propose to use statistical count models for these tasks: Hafemeister and Satija (Genome Biol 20:296, 2019) recommend using Pearson residuals from negative binomial regression, while Townes et al. (Genome Biol 20:295, 2019) recommend fitting a generalized PCA model. Here, we investigate the connection between these approaches theoretically and empirically, and compare their effects on downstream processing.

Results

We show that the model of Hafemeister and Satija produces noisy parameter estimates because

SUBMITTER: Lause J 

PROVIDER: S-EPMC8419999 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature