visnormsc

a graphical user interface to normalize single-cell RNA sequencing data

Lijun Tang, Nan Zhou*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Single-cell RNA sequencing (RNA-seq) allows the analysis of gene expression with high resolution. The intrinsic defects of this promising technology imports technical noise into the single-cell RNA-seq data, increasing the difficulty of accurate downstream inference. Normalization is a crucial step in single-cell RNA-seq data pre-processing. SCnorm is an accurate and efficient method that can be used for this purpose. An R implementation of this method is currently available. On one hand, the R package possesses many excellent features from R. On the other hand, R programming ability is required, which prevents the biologists who lack the skills from learning to use it quickly. To make this method more user-friendly, we developed a graphical user interface, visnormsc, for normalization of single-cell RNA-seq data. It is implemented in Python and is freely available at https://github.com/solo7773/visnormsc. Although visnormsc is based on the existing method, it contributes to this field by offering a user-friendly alternative. The out-of-the-box and cross-platform features make visnormsc easy to learn and to use. It is expected to serve biologists by simplifying single-cell RNA-seq normalization.

Original languageEnglish
Pages (from-to)636-640
Number of pages5
JournalInterdisciplinary Sciences: Computational Life Sciences
Volume10
Issue number3
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • Normalization
  • Quantile regression
  • RNA
  • Single-cell

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