A few responses:
The data files are saved out as .csv files. These are similar to a spreadsheet and can be opened in text editors, spreadsheet programs like Excel or Numbers, or imported into a Google spreadsheet. There are also various free online CSV viewers if you search “CSV viewer”.
When I run your pipeline and open the “MyExpt_Cells.csv” file, there is a column named “Children_GFP_Count” that has a count of the number of GFP objects per each cell.
A few notes about your pipeline:
- your nuclear segmentation thresholding looks great, well done!
- you may want to choose to “discard objects outside the diameter range” and set different boundaries for the sizes in order to eliminate very small dots from being included as nuclei
- your image for detecting cytoplasm is challenging to use to detect cells because the staining is very dark in the interior of the cytoplasm. One thing that can help to detect dim cells like this is to either use the “Log transform before thresholding?” option or to use the ImageMath module to raise the original image by a factor of 0.5. Doing so can help increase the pixel intensity of the dimmest values:
- Finally, at least to my eyes, your IdentifyPrimaryObjects module for detecting GFP appears to be detecting a lot of very dim objects as true signal. I’d recommend trying out the RobustBackground method for this segmentation, since you have a very dark background with a few dispersed bright spots:
This tutorial covers a lot of what I discussed above regarding optimizing segmentation settings and the underlying theory. Highly recommended! CellProfiler Workshop - YouTube