Is there any recommendation for tuning the parameters in the
I have been modifying the parameters until the clusters make more biological sense (such as immune cells far from adipocytes and adipocyte subpopulations closer in the UMAP). Does this sound reasonable? Should I simply stick to the default?
Are you seeing a lot of variance of hyperparameters? What sorts of things are you currently changing? We don’t go too far from the defaults, even if we do.
I messed with the number of hidden layers and dimensionality of the latent space (1-4 and 10-40). I modified these values because I expected different adipocyte types to be closely clustered, while other cell types far apart. Moreover, with higher values, brown adipocytes from different studies cluster together. But when I use values closer to the default some brown adipocytes cluster with white adipocytes.
1-4 would be much too small. If I changed defaults I might try
n_layers=2, n_latent=30, gene_likelihood="nb"
Thank you for the suggestion Adam. Could you explain the reasoning for these choices? Especially the gene_likehood. If I understood it correctly, nb should do better with overdispersed data, which should be my case due to very different cell types coming from different studies?