Developing user-friendly data analysis tools
It was in the second half of my PhD that I learned about data analysis apps. I was trying to parse through GWAS results from a maize metabolomic screen, which was made significantly easier when I started using an R Shiny app that was developed for this exact purpose. Later, when trying to a apply a new coexpression analysis method, using Mutual Rank instead of Pearson Correlation Coefficient, I realized that a reactive and customizable R Shiny app would be more efficient than running a short scripts with different paramters every timeI wanted to check a different gene or a different expression data set. As a bonus, R Shiny