Dealing mit with multiple conditions and details on model parameters

[Thapeachydude]

Good morning,

I'd like to try your tool, I don't have much experience with scVI and mainly work with the single-cell experiment class in Bioconductor, so it would be grea to have your insights on how to best run it.

1). Multiple conditions: The data set I have is not unlike the MIX-Seq one, but I'm dealing with multiple conditions, not two. Would you run them all together or rather separately pairwise (control vs treatment 1, then control vs treatment 2 etc)?

2). Raw or normalized counts: Does the tool require raw or normalized counts? (assuming raw, but wanted to make sure).

3). Model parameters: How do parameters such as the number of latent layers, n_hidden and batch size affect the output? I tried running my dataset using the parameters below (using all conditions vs dmso). The model finished, but looking at the salient UMAP it was one big blob.

## Initialize raw counts
adata.raw = adata
adata.layers["counts"] = adata.X.toarray() # keep raw counts for scdef

model = ContrastiveVI(
    adata,
    n_batch = 0, # no batch correction
    n_layers = 1, 
    n_hidden = 128,
    n_salient_latent=10,
    n_background_latent=10,
    use_observed_lib_size=False
)

background_indices = np.where(adata.obs["Treatment"] == "DMSO")[0]
target_indices = np.where(adata.obs["Treatment"] != "DMSO")[0]

model.train(
    check_val_every_n_epoch = 1,
    train_size = 0.8, # 0 to 1
    background_indices = background_indices,
    target_indices = target_indices,
    use_gpu = True,
    early_stopping = True,
    max_epochs = 1000,
    batch_size = 512
)

## Convert things back into R to continue with SCE class
reducedDim(sce, "salient") <- scd$obsm[["salient_rep"]] # assign as dimensional reduction to sce
set.seed(100)
sce <- runUMAP(sce, dimred = "salient", n_neighbors = 5, name = paste0("UMAP_salient"), BPPARAM = mcparam) # assuming euclidean distance works as a metric

Many thanks : )