Speaker
Description
The principle of scientific accountability is more than reproducibility of data analysis: each result – figure, table or p-value must be unequivocally and easily tracked to the original data from which it originates. In many bioinformatics workflows this kind of accountability is difficult to maintain, particularly in interactive web applications where exploratory analyses are performed through graphical interfaces.
In this talk, I present an approach to improving scientific accountability in interactive analysis tools using a framework for building bioinformatics applications with Shiny modules, R package {bioshmods}. The framework automatically records analysis steps performed in the application and generates a corresponding R Markdown document that reproduces the complete workflow. The resulting apps can create a streamlined analysis as an R markdown file, producing publication quality results and a transparent, auditable and accountable analysis path.
The use of {bioshmods} enables developers to build interactive tools that preserve transparency and traceability while maintaining the usability benefits of graphical interfaces. I will demonstrate the framework using two applications build with {bioshmods}: {seaPiper}, for visualization of RNA-seq pipeline results, and {tmodUI}, a gene set enrichment analysis tool.
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | bioinformatics,reproducibility,shiny,workflow automation,r markdown |
|---|---|
| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |
| Interested in serving as reviewer? | january.weiner@bih-charite.de |