My name is Dorien Pastoors, I'm a PhD student at Erasmus MC in Rotterdam. During my PhD I'm working on gene regulation in leukemia.
Gating in of flow cytometry data in FlowJO and visualisation with ggplot/R
While FlowJO is great for making and inspecting manual gating, its layout editor can be frustrating at times. However, doing the gating in Flowjo and the rest with ggplot is actually not very difficult at all as I hope to show you here!
In order to conviently be able to plot flow cytometry data with R, we would like to be able to use gating created in external software, such as Flowjo workspaces, and combine this with all the plotting options from ggplot.
Using this, you can:
Make multi-graph overlays as shown on the right
Easily arrange and annotate your dotplots and show gates
The drawing that could have been a graph
When using drawings instead of graphs, almost always, you will loose information in the drawing that is contained in the data underlying (as simplification is often the point of using drawings in the first place). This tutorial is an example of this dilemma, where I first wanted to make an illustration showing the similarity between two proteins, but then decided this could, and should in fact be, a graph.
While there are probably a thousand ways to visualise protein alignments already out there, I could not find one that was simple enough for an introductory slide so I decided to make my own. I wanted to be able to see in one view you where two proteins diverge in sequence and how this relates to the location of their functional domains. While you can of course show a domainogram of two proteins and just say “they are most conserved within their functional domains” , or “domain X is not conserved but domain Y is”, you can also actually visualise this! And for me this made it actually also much more clear for myself.
Frequently, dose response data is collected in excel files from colorimetric plate readers. Getting them into Graphpad requires quite a lot of paste-transpose! This takes a lot of time, and the more you have to do it, the more room for error arises.
Using this script, you can:
Import data directly from an excel file in 96-well format with an excel plate layout
Using dplyr, it is easy to group your data, normalise, make heatmaps, and identify outliers
Calculate the IC50 using the drc package.