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Normalization for Single-Cell RNA-Seq Data Analysis.

Rhonda Bacher1

  • 1Department of Biostatistics, University of Florida, Gainesville, FL, USA. rbacher@ufl.edu.

Methods in Molecular Biology (Clifton, N.J.)
|February 14, 2019
PubMed
Summary
This summary is machine-generated.

We present SCnorm, a robust normalization method for single-cell RNA sequencing data, implemented in R and available via Bioconductor. This approach includes diagnostic tools to assess normalization quality for improved data analysis.

Keywords:
Gene expressionHigh-throughput sequencingNormalizationRead countSingle-cell RNA-seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNA-seq) generates high-dimensional data.
  • Normalization is crucial for accurate analysis of scRNA-seq data.
  • Existing methods may not adequately address the complexities of scRNA-seq data.

Purpose of the Study:

  • To introduce SCnorm, a novel and robust normalization method for scRNA-seq data.
  • To provide an R package implementation of SCnorm within the Bioconductor framework.
  • To offer diagnostic tools for evaluating normalization performance.

Main Methods:

  • Development of the SCnorm algorithm for normalizing scRNA-seq data.
  • Implementation of SCnorm in the R programming language.
  • Integration into the Bioconductor package management system.
  • Inclusion of visualization functions for assessing normalization quality.

Main Results:

  • SCnorm provides a robust approach to normalize scRNA-seq data.
  • The SCnorm package includes user-friendly diagnostic tools.
  • The methodology is applicable to various scRNA-seq experimental designs.

Conclusions:

  • SCnorm offers a reliable solution for scRNA-seq data normalization.
  • The accompanying diagnostic functions aid in method selection and validation.
  • This chapter serves as a guide to implementing and utilizing SCnorm.