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 normalizati...

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Bibliographic Details
Published in:Genome Biology Vol. 20; no. 1; p. 296
Main Authors: Hafemeister, Christoph, Satija, Rahul
Format: Journal Article
Language:English
Published: London BioMed Central 23.12.2019
Springer Nature B.V
BMC
Subjects:
Animal Genetics and Genomics
binomial distribution
Bioinformatics
Biomedical and Life Sciences
data collection
Datasets
Evolutionary Biology
Gene expression
gene expression regulation
Generalized linear models
genes
Genomics
Human Genetics
Humans
Life Sciences
linear models
Method
Microbial Genetics and Genomics
Normalization
Plant Genetics and Genomics
Regression Analysis
Ribonucleic acid
RNA
sequence analysis
Sequence Analysis, RNA
Single-Cell Analysis
Single-cell RNA-seq
Software
variance
Single-cell RNA-seq
Normalization
ISSN:1474-760X, 1474-7596, 1474-760X
Online Access:Get full text
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Summary: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 unconstrained negative binomial model may overfit scRNA-seq data, and overcome this by pooling information across genes with similar abundances to obtain stable parameter estimates. Our procedure omits the need for heuristic steps including pseudocount addition or log-transformation and improves common downstream analytical tasks such as variable gene selection, dimensional reduction, and differential expression. Our approach can be applied to any UMI-based scRNA-seq dataset and is freely available as part of the R package sctransform , with a direct interface to our single-cell toolkit Seurat .
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1874-1