I am currently running the scvi-tools to perform batch correction and dimensionality reduction of the dataset consisting of multiple patients. I am not sure about the complexity of the model that has been chosen for dimensionality reduction.
Here is an example of the code I am currently running:
sc.pp.highly_variable_genes(adata_da_HVG, n_top_genes=5000, subset=True)
model_da = scvi.model.SCVI(adata_da_HVG)
I wonder if there is a way to visualize the model complexity to look at bias-variance tradeoff for the particular dataset. I would like to check if I would need more layers to perform the dimensionality reduction more efficiently.