WIlsON: Web-based Interactive Omics VisualizatioN

High throughput (HT) screens in the omics field are typically analyzed by automated pipelines that generate static visualizations and comprehensive spreadsheet data for scientists. However, exploratory and hypothesis driven data analysis are key aspects for the understanding of biological systems, which benefit considerably from customized and dynamic visualization.

WIlsON was implemented in R Shiny as an interactive workbench for analysis and visualization of multi-omics data. It is based on an adapted version of the SummarizedExperiment class, ensuring compatibility with a wide range of datatypes.

Features:

Compatibility: Any flatfile table/matrix or SummarizedExperiment dataset can easily be converted to be compatible. It can originate from any analysis (e.g. RNA-Seq, ChIP-Seq, Mass Spectrometry, Microarray) that results in numeric data (e.g. count, score, log2foldchange, z-score, p-value) attributed to a feature (e.g. gene, transcript, probe, protein).

Hosting Felxibility: A new server can be set up using either R, R Studio, or by downloading a prepackaged Docker image (this works on both, Windows and Linux based systems). This is not a complicated process (one command in case of Docker) and multiple servers can run independently on the same machine. Each server can optionally be started with the desired (e.g. random) url to restrict access to the desired user base. Each server can also host multiple datasets which are introduced by simply copying the respective flatfile into the right folder.

Ease-of-use: The typical workflow consists of five steps: 1) select the desired dataset, 2) filter for features of interest based on categorical (annotation) or numerical values (e.g. transcripts, genes, proteins, probes), 3) Select plot type (bar/box/violin, scatter, heatmap), 4) Adjust plot parameters, 5) Render/download result. An extensive manual and tutorials exist.

Tracking/Logging/Transfer: The underlying R code of a produced plot can be downloaded for local reproducation or storage. It is also possible to create a new Docker image of a WIlsON server including a number of preinstalled datasets for simple transferral of processed data.

Availability:

Manuscript: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty711/5078467
Demo server:  http://loosolab.mpi-bn.mpg.de
Docker container: https://hub.docker.com/r/loosolab/wilson
GitHub: https://github.molgen.mpg.de/loosolab/wilson-apps
CRAN: https://cran.r-project.org/web/packages/wilson/