Computational workflows for RNA sequencing data often include differential expression and functional interpretation analyses, and a number of specialized and established tools exist for performing these tasks, each generating a set of intermediate processed data and results.
In order to simplify the process of interpretation from large and complex datasets, it is critical that all the analytic components are integrated together in a framework that streamlines the generation of novel hypotheses, thus simplifying the process and reducing the time required to extract knowledge from the data at hand.
The workflow in the GeneTonic package
Researchers at the University Medical Center in Mainz, Germany (@FedeBioinfo and @AnnekathrinLudt) developed a solution to this with the GeneTonic package (https://bioconductor.org/packages/GeneTonic/), in which they enable the consolidation of the individual input data and results (expression matrix, DE results, enrichment analysis) in a framework that combines interactivity and reproducibility. While interactivity makes the exploration accessible and user-friendly to a broad spectrum of researchers, GeneTonic further allows for reproducibility, by means of tailored HTML reports automatically created via RMarkdown.
This approach can prove very efficient in effectively generating insight from increasingly complex datasets, addressing the long-standing bottleneck of in-depth analysis, visualization, and interpretation of transcriptomic data, with a broad spectrum of application scenarios.