Great tool, thanks for all your efforts…couple questions below.
I am integrating ~500K cells from > 40 donors, and I am interested in parameter tuning for the model. I’m new to neural networks, but it seems like the output (clustering, markers, UMAP) would be primarily affected by the number of HVGs, number of layers, and final dimensions. I was wondering what your thoughts were on toggling these three inputs for model refinement. My understanding is increasing layers should tease out more hidden interactions, while increasing dimensions is essentially allocating more space to store variability/patterns?
Does your model automatically consider variance from sequencing depth or should users specify number of UMIs as a continuous covariate?
Thanks for your help.