Dear JC,
Concerning statistical testing, I am in favour of techniques used in transcriptomics analyses. In the recent paper advertised in this forum "Establishing High Dimensional Immune Signatures from Peripheral Blood via Mass Cytometry in a Discovery Cohort of Stage IV Melanoma Patients", you will see a lot of heatmaps representing percentages. These representations are more comprehensive (or compact) than a bunch of boxplots. The usual goal of an analysis is to compare groups of samples (cancer vs control in your case) for each cell population that was detected (or identified or searched). This could be achieved using t-test or Wilcoxon tests. Such tests will take into account the variability within the group of sample. In order to cope with multiple testing, False Discovery Rate estimation (or correction) must be carried out.
We usually use MeV program to carry out all the stages of such analysis once the populations or clusters of cells have been found and percentages have been extracted. IMHO, the post-analysis turns to be the same whatever the populations came from a CyTOF or classical cytometry experiment, a computational method for finding clusters of cells or a classical manual gating. The MeV software is a graphical data analysis tool. We have just set up a web site for the French community
http://impact.marseille.inserm.fr/. The web site is in French but the dias are in English. You will find the standard pipeline we use and some explanations of the rationale of steps in a recent presentation I gave in a French congress.
* rationale of the pipeline (briefly, we apply a log transform to the percentages before centring)
http://impact.marseille.inserm.fr/tutos ... njeaud.pdf* step by step slide show
http://impact.marseille.inserm.fr/tutos ... ercent.pdf* short video demonstrating the various steps on a real dataset from a classical cytometry experiment
http://impact.marseille.inserm.fr/tutos ... erview.mp4 (comments are in French, but I guess you will see how easy it is to carry out the analysis)
* download the software at
https://sourcesup.renater.fr/frs/?group_id=2569 (Windows 64 bits including Java).
If you have questions, feel free to contact me.
Apart from this very own view, I think there are brilliant statisticians at Mayo Clinic that could help you.
Concerning Citrus pointed out by Brian, if you look under the hood, you will find hierarchical clustering and a statistical method (SAM) taking into account multiple testing. Both of them have been exploited a lot at the age of DNA chip.
Brian, thanks for the reference concerning cluster stability.
Hope my answer will help you,
Samuel