I took a look at your pipeline and I have a few suggestions that I think will help. I’d also recommend working through our tutorials on YouTube, such as this workshop - while the problem is different, it will help you learn principles of how CellProfiler works: CellProfiler Workshop - YouTube
If I understand your data correctly, the Test and Control images are two different images that each contain the same channels. I think you may need to change how you import these images into CellProfiler in order to set up your experiment correctly.
In the NamesAndTypes module, you are configuring an “ImageSet” to contain both a Test and a Control image. Essentially, you’re telling CellProfiler that the Test image and the Control image were acquired at the same imaging position/site. I’d recommend using the “All images” mode instead.
Since your images are RGB, I’d also recommend importing them in “Color” mode and then using the module “ColorToGray” to split the individual channels to separate images that can be processed individually in CellProfiler.
More information on configuring images for analysis is available in our help, available by clicking on the
? buttons in CellProfiler or online here:
Once you’ve imported your images, a rough outline of what you’ll probably want to try to do is:
- segment Nuclei w/ IdentifyPrimaryObjects as objects using the Blue channel
- EnhanceOrSuppressFeatures w/ the Red channel
- segment Speckles w/ IdentifyPrimaryObjects w/ the Red channel
- relate the Nuclei (parents) to the Speckles (children). Be sure that each of these objects have unique names
- make desired measurements
You’ll run the same pipeline on both test and control images. The output Image.csv file will have a Count_Speckles (or whatever you named your Speckle objects in IdentifyPrimaryObjects) column that contains the # of speckles per image. The Nuclei.csv file will have a Children_Speckles_Count column with the number of Speckles per Nuclei object.
I hope this is helpful! Let us know how it goes.