SCnorm: Robust Normalization of Single-cell RNA-seq Data

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2017 Apr 19

PMID 28418000

Citations 132

Authors

Rhonda Bacher

Li-Fang Chu

Ning Leng

Audrey P Gasch

James A Thomson

Ron M Stewart

Michael Newton

Christina Kendziorski

Affiliations

Soon will be listed here.

Abstract

The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.

Citing Articles

Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution.

Lu S, Yang J, Yan L, Liu J, Wang J, Jain R Nat Commun. 2025; 16(1):1246.

PMID: 39893178 PMC: 11787294. DOI: 10.1038/s41467-025-56623-1.

Single-cell RNA sequencing algorithms underestimate changes in transcriptional noise compared to single-molecule RNA imaging.

Khetan N, Zuckerman B, Calia G, Chen X, Garcia Arceo X, Weinberger L Cell Rep Methods. 2024; 4(12):100933.

PMID: 39662473 PMC: 11704610. DOI: 10.1016/j.crmeth.2024.100933.

Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise.

Khetan N, Zuckerman B, Calia G, Chen X, Garcia Arceo X, Weinberger L bioRxiv. 2024; .

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Single-cell omics: experimental workflow, data analyses and applications.

Sun F, Li H, Sun D, Fu S, Gu L, Shao X Sci China Life Sci. 2024; 68(1):5-102.

PMID: 39060615 DOI: 10.1007/s11427-023-2561-0.

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications.

Ctortecka C, Clark N, Boyle B, Seth A, Mani D, Udeshi N Nat Commun. 2024; 15(1):5707.

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