How do I consider hashtags in the Anndata object and my analyses? I have 7 different hashtags that identify 7 different tissues. I want to see these different tissues in my UMAP plots and then do differential analyses.
Here is the AnnData object I have that I created from a Seurat object and then loaded into scvi-tools (anndata.read). how can I be sure that the HTOs are labeled according to the tissue (I can see that they are in a Seurat object).
Assuming you’ve run through the standard scvi-tools/totalVI/Scanpy workflow then you should be able to
sc.pl.umap(adata, color="HTO_classification")
However, if you’d just like the joint RNA + protein latent space, the simplest thing to do is to save it as a csv, load it in R and then add it to the seurat object using for example
# latent is a dataframe from scvi-tools
pbmc[['totalvi']] <- CreateDimReducObject(embeddings = latent, key = "totalvi_", assay = DefaultAssay(pbmc))
And then you can just use R.
Regarding differential expression, basic Seurat or Scanpy is a good place to start. If you feel that protein background is challenging the effect sizes, then it would be beneficial to use totalVI DE.
Thank you, this was helpful. A followup question…
After setting up a AnnData object and running TotalVI on my data to get denoised protein values how can I visualize single UMAPs with each tissue (HTO) separately. I can visualize all of the tissues together on the UMAP as such:
sc.pl.umap(
adata,
color=[“leiden_totalVI”, “batch”,“HTO_maxID”],
frameon=False,
ncols=3,
)
But how can I create multiple UMAPs with each tissue highlighted?