I thought I would open a new thread for this question. I went through the PAGA tutorial (paul-15) and tried it on my data, with and without the denoising step. Adding that step seems to have a profound effect on how the data looks and I am not too sure 1) whether it makes sense 2) whether the parameters I am using are appropriate.
In the (paul-15) tutorial, you are using 4 neighbors at first:
sc.pp.neighbors(adata, n_neighbors=4, n_pcs=20)
This number appears rather small compared to the sort of default values usually used?
Then in the denoising step, the number neighbors increases to 10 for the diffusion map:
sc.pp.neighbors(adata, n_neighbors=10, use_rep='X_diffmap')
I am not very familiar with the concepts underlying these decisions and this renders the choice somewhat difficult. Of course I could just try every possible combination and pick the one that looks best but I’d prefer to understand a bit more the logic behind it. Would it be possible to obtain more ‘details’ about what should motivate this choice?