I do not know what did you mean by ‘’ I’d add “Cells” to all of the measurement modules you’re using.’’. I have only Hoechst staining and CD markers staining for phenotype detection.
You have a module in your pipeline that takes your “Nuclei” objects and expands them out a bit to create objects you called “Cells”, but in your downstream measurement modules you are only measuring the shape, size, and intensity in your CD channels of the “Nuclei” but not the “Cells”- my suggestion would be to measure BOTH the shape, size, intensity of “Nuclei” and “Cells”, as it may give the classifier enough extra information to be more accurate at categorizing the PBMCs into subtypes.
You can also consider adding additional types of measurements, such as the “MeasureCorrelation” and “MeasureGranularity” modules.
I’d also double check your segmentation results- if the segmentation isn’t great it might explain a bad classification. You may want to consider adding a SaveImages module to your pipeline to save the outlines of your segmented Nuclei; if you enable the “Record path information” box you’ll be able to see the outlines in Analyst while you’re classifying the cells, which’ll give you a hint as to whether there are segmentation issues or not.
Even if you do all of this though, if your segmentation isn’t good enough or if the differences just aren’t clear enough there’s no guarantee that you will be able to train a classifier that gets very high accuracy on all of your 4 subpopulations. That’s why I mentioned in my last post that you should attempt to figure out if there are certain classes that are being identified particularly poorly and then trying to think about if there’s a marker or something you could add to your experiment to help enhance your ability to detect them next time.