I have two questions:
I’m doing a co-localization analysis for two proteins that show up as tiny granules in a fly neuron’s axons (and in the cell body but we’re ignoring that for the moment) and I’m having trouble identifying the objects. However, when I use the morph:close operation it greatly enhances object recognition. Now, since the operation can inflate the actual size of the object I’m wondering if I shouldn’t use it. Then again, since it’s co-localization and I don’t care about the size or shape of the objects does it really matter since its a proportion of object x that is overlapping with object y? In other words, the expansion in pixels occurs in both sets of neurons so it shouldn’t affect the relative comparison. Is my reasoning correct?
When analyzing nuclei, I’m still getting these dumbbell shapes consisting of two nuclei touching one another. The de-clumping options (de-clumping by intensity, shape or lapcalian of gaussian) seem to work rarely. If I increase the threshold to a point where the objects do de-clump, I’ve usually lost identification of the majority of the other nuclei. Is there a better way to de-clump? Or a way to filter these dumbbell shapes out?