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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Vydáno v:	Genome Biology Ročník 20; číslo 1; s. 296
Hlavní autoři:	Hafemeister, Christoph, Satija, Rahul
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 23.12.2019 Springer Nature B.V BMC
Témata:	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
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Shrnutí:	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 .
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Undefined-1 ObjectType-Feature-3 content type line 23
ISSN:	1474-760X 1474-7596 1474-760X
DOI:	10.1186/s13059-019-1874-1