Glimma: Interactive graphics for gene expression analysis

Shian Su, Charity W. Law, Casey Ah-Cann, Marie Liesse Asselin-Labat, Marnie E. Blewitt, Matthew E. Ritchie

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Motivation: Summary graphics for RNA-sequencing and microarray gene expression analyses may contain upwards of tens of thousands of points. Details about certain genes or samples of interest are easily obscured in such dense summary displays. Incorporating interactivity into summary plots would enable additional information to be displayed on demand and facilitate intuitive data exploration. Results: The open-source Glimma package creates interactive graphics for exploring gene expression analysis with a few simple R commands. It extends popular plots found in the limma package, such as multi-dimensional scaling plots and mean-difference plots, to allow individual data points to be queried and additional annotation information to be displayed upon hovering or selecting particular points. It also offers links between plots so that more information can be revealed on demand. Glimma is widely applicable, supporting data analyses from a number of well-established Bioconductor workflows (limma, edgeR and DESeq2) and uses D3/JavaScript to produce HTML pages with interactive displays that enable more effective data exploration by end-users. Results from Glimma can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. Availability and Implementation: The Glimma R package is available from http://bioconductor.org/packages/Glimma/.

Original languageEnglish (US)
Pages (from-to)2050-2052
Number of pages3
JournalBioinformatics
Volume33
Issue number13
DOIs
StatePublished - Jul 1 2017

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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