Computational biology and genomics
For a while, heatmap.2() from the gplots package was my function of choice for creating heatmaps in R. Then I discovered the superheat package, which attracted me because of the side plots. However, shortly afterwards I discovered pheatmap and I have been mainly using it for all my heatmaps (except when I need to interact…
I discovered the superheat package a few days ago and the adjacent plots feature was really attractive. The superheat #rstats package for making heatmaps looks super! I like the adjacent plots https://t.co/KhyA6K6Ap3 — Dave Tang (@davetang31) February 3, 2017
Once upon a time, I made my graphs using Excel because it was the only software that I was aware of for making graphs. Now one can do amazing things with Excel and produce fairly good looking graphs, but after looking at some examples of R graphs, I wanted to learn a bit more about…
Update 15th May 2018: I recommend using the pheatmap package for creating heatmaps. Heatmaps are great for visualising large tables of data; they are definitely popular in many transcriptome papers. Here are the basic commands for making your own heatmap: The colour schemes from the function display.brewer.all() Returning the values used for the heatmap Taken…