Normalization and noise reduction for single cell RNA-seq experiments

Bo Ding; Lina Zheng; Yun Zhu; Nan Li; Haiyang Jia; Rizi Ai; Andre Wildberg; Wei Wang

doi:10.1093/bioinformatics/btv122

Normalization and noise reduction for single cell RNA-seq experiments

Bioinformatics. 2015 Jul 1;31(13):2225-7. doi: 10.1093/bioinformatics/btv122. Epub 2015 Feb 24.

Authors

Bo Ding¹, Lina Zheng¹, Yun Zhu¹, Nan Li¹, Haiyang Jia², Rizi Ai¹, Andre Wildberg¹, Wei Wang²

Affiliations

¹ Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.
² Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093, USA, College of Computer Science and Technology, Jilin University, Changchun 130012, China and Department of Cellular and Molecular Medicine, University of California, La Jolla, CA 92093, USA.

Abstract

A major roadblock towards accurate interpretation of single cell RNA-seq data is large technical noise resulted from small amount of input materials. The existing methods mainly aim to find differentially expressed genes rather than directly de-noise the single cell data. We present here a powerful but simple method to remove technical noise and explicitly compute the true gene expression levels based on spike-in ERCC molecules.

Availability and implementation: The software is implemented by R and the download version is available at http://wanglab.ucsd.edu/star/GRM.

Contact: wei-wang@ucsd.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Gene Expression Profiling / methods*
High-Throughput Nucleotide Sequencing / methods*
Humans
Sequence Analysis, RNA / methods*
Signal-To-Noise Ratio
Single-Cell Analysis / methods*
Software*

Grants and funding

U01 MH098977/MH/NIMH NIH HHS/United States

Abstract

Publication types

MeSH terms

Grants and funding

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