Glimma: interactive graphics for gene expression analysis

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/devel/bioc/html/Glimma.html.

• Publication year

  2017

• Venue

  bioRxiv

• Publication date

  2017-01-05

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1101/096107PMID 28203714PMCID 5870845
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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