Normalizing single-cell RNA sequencing data: challenges and opportunities. Academic Article uri icon

Overview

abstract

  • 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.

publication date

  • May 15, 2017

Research

keywords

  • Algorithms
  • High-Throughput Nucleotide Sequencing
  • RNA
  • Sequence Analysis, RNA
  • Single-Cell Analysis
  • Transcriptome

Identity

PubMed Central ID

  • PMC5549838

Scopus Document Identifier

  • 85021816036

Digital Object Identifier (DOI)

  • 10.1038/nmeth.4292

PubMed ID

  • 28504683

Additional Document Info

volume

  • 14

issue

  • 6