Web-Based Interactive Omics Visualization

Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data. This package utilizes the 'shiny' and 'plotly' frameworks to provide a user friendly dashboard for interactive plotting.


Buildkite: Build status



High-throughput (HT) studies of complex biological systems generate a massive amount of so called omics data. The results are typically summarized using spreadsheet like data formats. Visualization of this data is a key aspect of both, the analysis and the understanding of biological systems under investigation. While users have many visualization methods and tools to choose from, the challenge is to properly handle these tools and create clear, meaningful, and integrated visualizations based on pre-processed datasets.


The WIlsON R package employs the R Shiny and Plotly web-based frameworks using a client-server based approach comprising a range of interface and plotting modules. These can be joined to allow a user to select a meaningful combination of parameters for the creation of various plot types (e.g. bar, box, line, scatter, heat). The modular setup of elements assures a concise code base and simplifies maintenance. An app thus created can be mounted on an R Shiny Server or inside R Studio. Data must be supplied server-side using a custom tab-delimited format derived from the SummarizedExperiment format (Clarion) and can principally originate from any analysis (e.g. RNA-Seq, ChIP-Seq, Mass Spectrometry, Microarray) that results in numeric data (e.g. count, score, log2foldchange, zscore, pvalue) attributed to a feature (e.g. gene, transcript, probe, protein).


The WIlsON R package includes a toolbox of R Shiny modules that can be used to construct a wide array of web-interfaces for plotting feature-based data.


All components of the WIlsON R package have been implemented in an integrated web application that is available for download from the Github repository wilson-apps and can be tested on our official demonstration server.

Usage instructions can be found in the extensive documentation.

Get a Docker container here.

Please make sure to check our other projects at loosolab.

Organization and Philosophy

Visualizations are organized hierarchically as Shiny modules, such that larger visualizations are built from small, general, and reusable components.


The module source code is made available as an R package and can be installed locally with

install_github("loosolab/wilson", host="github.molgen.mpg.de/api/v3")

CLARION input format

CLARION: generiC fiLe formAt foR quantItative cOmparsions of high throughput screeNs

CLARION is a data format especially developed to be used with WIlsON, which relies on a tab-delimited table with a metadata header to describe the following columns. It is based on the Summarized Experiment format and supports all types of data which can be reduced to features and their annotation (e.g. genes, transcripts, proteins, probes) with assigned numerical values (e.g. count, score, log2foldchange, z-score, p-value). Most result tables derived from RNA-Seq, ChIP/ATAC-Seq, Proteomics, Microarrays, and many other analyses can thus be easily reformatted to become compatible without having to modify the code of WIlsON for each specific experiment.

Please check the following link for details considering the CLARION format.

How to cite

Schultheis H, Kuenne C, Preussner J, Wiegandt R, Fust A, Bentsen M, Looso M. WIlsON: Webbased Interactive Omics VisualizatioN. (2018), doi: https://doi.org/10.1093/bioinformatics/bty711


This project is licensed under the MIT license.


wilson 2.0.2

  • fixed CRAN check Note/ Error

wilson 2.0.1

  • tests added

wilson 2.0.0


  • clarion class:

    • easier data-format validation by providing several checks
    • simplified module usage (only forward clarion object) for top-level modules (e.g. filter & plot)
    • provide functions for frequent tasks (e.g. get_name, is_delimited, etc.)
  • geneView:

    • group columns by one or more factors
  • pca:

    • color & shape grouping by selected factor(s)
  • scatterPlot:

    • add name to hovertext if available (only interactive)


  • improved notifications (closable, more)
  • overall code quality improvements via usage of packages goodpractice and lintr
  • removed deprecated colorPicker

wilson 1.0.0

first public release

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.