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Biological and biomedical researchers often do not have training in the application of advanced statistical techniques, though modern experiments typically involve production of high dimensional datasets, leading to analysis challenges for these researchers. R and its package repositories (CRAN and Bioconductor) have enabled researchers without formal training in these advanced techniques to apply new statistical methods to their datasets.
Proper statistical analyses require that these researchers be capable of importing potentially large datasets, assess quality of data, perform ordination and inference, and again assess diagnositics for the statistical analysis itself. The entire analysis is difficult or impossible to automate. Rmarkdown workflows allow for detailed instruction on proper use of statistical methods and R packages throughout an analysis.
I will describe the Bioconductor channel of Rmarkdown workflows published on F1000Research, and how these workflows allow for rapid dissemination of new statistical methods to researchers, and provide a venue for on-demand training in biological and biomedical data science.
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