The R Shiny package lets users interact with plots in R.
During data analysis, we often have to choose parameters based on a data set's properties.
For this, it is helpful to be able to interactively assess aspects of the data.
The Seurat package has a great example of this.
As discussed in this Seurat tutorial, a user has to decided how many principle components to provide for cell clustering, which assigns cells to clusters using UMAP. Cells that get assigned to the same cluster are then interpreted as being the same cell type, and the numbers of cells per cluster is interpreted as biologically meaningful depending on the experimental setup.

In this task, I'm going to create an example Markdown showing how we can make a Seurat analysis interactive, using an R Shiny tutorial written by someone else as a starting point.

I'm doing this work on a local clone, set up with the follow remotes:

• Ann's fork on bitbucket: https://bitbucket.org/aloraine/r-shiny-tutorial-binf3121
• Ann's fork on github: https://github.com/aloraine/R-Shiny-Tutorial-BINF3121
• Brandon's original repo: https://github.com/brandonyph/R-Shiny-Tutorial.git

The original repository by brandonyph uses "main" as the main branch. I forked his repo in github to create aloraine/R-Shiny-Tutorial-BINF3121, also with "main" as the main branch.

Because I like the Bitbucket interface better than the github interfact, I then imported my github fork (which was identical to Brandon's at that point) to bitbucket, creating another repository there: git@bitbucket.org:aloraine/r-shiny-tutorial-binf3121. I set up IGBF-3223 as the main branch there for now. I'm doing this so that I can avoid cluttering branch main with lots of commits that Brandon might not want to have should I ever want to submit PR's to his original repository. (I noticed his work has a few typos I might want to contribute fixes for, by way of thanking him for making his work available to all.)