symbol Note: Versions of QuPath

While the vast majority of this workflow should apply to the later variants of QuPath 0.4.x, as much as possible we will be making use of new features introduced with 0.5.0 rc1 and rc2.

If you find that any functionality is missing from your version, it is likely you are using an earlier version of QuPath than this guide was intended for. While it is important not to switch software versions mid-analysis, if you are exploring whether or not to use QuPath for your multiplex analyses, there are quite a few new features included with the 0.5.0 release candidates that make them worth exploring.

The following is a collaboration with Johanna Dela-Cruz, intended to make breaking into multiplex (multiple channel) image analysis just a little bit easier. The information presented will consist of a short video produced by Johanna and hosted on her YouTube channel, paired with a text description including example code and finer details written by me.
The project was created according to the steps shown in the official documentation (create folder, drag and drop images).

For another live example of performing multiplex analysis with QuPath, see Sara McAdle's presentation on YouTube from the 2023 From Samples to Knowledge , held in San Diego at the La Jolla Institute for Immunology.

A Zipped file containing a sample project you can use to test and follow along can be found in the same place as the scripting demo. In the “Multiplex demo” zip file, you will find two folders, one containing the images and a second containing the project along with the full scripts for the analyses shown (in Automate->Project scripts…)

Image of project tab in QuPath

Image of project tab in QuPath

This guide is certainly not the only way to run a multiplex analysis - and it contains many opinions that not everyone might agree with or might not apply to your specific project - but hopefully it includes enough suggestions and good practices to get a few people started. Validation is always important - testing different settings on your full data set, finding one that gives the results you want, and then calling that your analysis is the sort of thing that leads to retractions in the future. Not how good science works - in fact, running through your full data set repeatedly is more like a retrospective study, as the images have already been collected and you can run a multitude of analyses on them until you obtain your desired results.