Expectations for UMAP of embeddings

Hi,

interesting tool (and the use of the single-cell experiment class is much appreciated). We have single-cell RNA-Seq data from multiple samples at various conditions. This far, I've seen the most sensible results by creating pseudo-bulks per cell type, sample and condition and fitting a linear model ~ sample + Condition using edgeR.

So, I was very curious when I saw your tool. This far, however, I'm not sure I understand the output.
I ran:
```
fit <- lemur(sce, design = ~ sampleID + Treatment, n_embedding = 20)

set.seed(100)
fit <- runUMAP(fit, dimred = "embedding", n_neighbors = 15, min_dist = 0.25, name = "UMAP_embedding", BPPARAM = mcparam)

```
to get an overview of the embeddings. I would expect at least some separation based on the conditions (since for some of them the pseudo-bulk results are quite strong, and we can even appreciate them in a UMAP of PCA loadings). But I see a big blob of cells, and some very small individual groups. But no "Treatment-shifts".

Is this what you would expect?

Btw. is there a way to limit the memory of the lemur function call? It is very fast but super memory intesive. Ideally, I don't always need to run it on a HPC.

Best,
M