Journal Article

Normalizing single-cell RNA sequencing data: challenges and opportunities

Risso, Davide, Scialdone, Antonio, Dudoit, Sandrine, Vallejos, Catalina A, Marioni, John C

2017

Overview

This Perspective examines single-cell RNA-seq data challenges and the need for normalization methods designed specifically for single-cell data in order to remove technical biases. Single-cell transcriptomics is becoming an important component of the molecular biologist's toolkit. A critical step when analyzing data generated using this technology is normalization. However, normalization is typically performed using methods developed for bulk RNA sequencing or even microarray data, and the suitability of these methods for single-cell transcriptomics has not been assessed. We here discuss commonly used normalization approaches and illustrate how these can produce misleading results. Finally, we present alternative approaches and provide recommendations for single-cell RNA sequencing users.

• Publisher: Nature Publishing Group US,Nature Publishing Group
• Subject: 38 / 38/91 / 45/91 / 631/114/2397 / 631/114/2415 / 631/1647/2017 / 631/208/199 / 631/208/514/1949 / Algorithms / Bioinformatics / Biological Microscopy / Biological Techniques / Biomedical Engineering/Biotechnology / Cells / Data analysis / Data Interpretation, Statistical / Data processing / DNA microarrays / Gene sequencing / Genetic research / High-Throughput Nucleotide Sequencing - methods / High-Throughput Nucleotide Sequencing - standards / Life Sciences / Methods / Molecular biology / Normalizing / perspective / Proteomics / Reference Values / Ribonucleic acid / RNA / RNA - genetics / RNA sequencing / Sequence Analysis, RNA - standards / Single-Cell Analysis - standards / Technology utilization / Transcriptome - genetics