For a proper understanding of the organization and regulation of gene expression, the computational analysis is an essential component of the scientific workflow, and this is particularly true in the fields of biostatistics and bioinformatics. Interactivity and reproducibility are two highly relevant features to consider when adopting or designing a tool, and often they can not be provided simultaneously.In this work, we address the issue of developing a framework that can provide interactive analysis, in order to allow experimentalists to fully exploit advanced software tools, as well as reproducibility as an internal validation of the analysis steps, by providing the underlying code and data in such a way that enables the re-creation of the results, and also constitutes a didactic tool for the life scientist.We illustrate this paradigm with the help of the R/Bioconductor package pcaExplorer, designed as a practical companion for interactive and reproducible exploratory data analysis for high dimensional data (e.g. RNA-seq), and highlight some of the features that are provided in the software.
Marini, F., & Binder, H. (2017). Development of Applications for Interactive and Reproducible Research: a Case Study. Genomics and Computational Biology, 3(1), 39. https://doi.org/10.18547/gcb.2017.vol3.iss1.e39