scVelo from Seurat object

Hello,

Apologies if my questions sound trivial but I am completely new to python, and I am a biologist before being an occasional R user (so far… but hopefully will be able to play with python soon as well!). I would be interested in running scVelo on my data but I am not quite sure that I am doing the right things, and I am now feeling a little bit stuck.

• I have run velocyto on my output from 10x (all cells classified as such by cellranger), which has successfully generated a loom file.
• In parrallel, I have processed my data using Seurat, and I now have a filtered object, with only the group of cells I am interested in, which also contains clustering information, PCA and UMAP coordinates.

My first question is: is this the correct approach, or should velocyto be run on filtered data based on the cell barcodes that I am interested in only (ie filtered cells obtained at the end of my seurat analysis), or perhaps it doesn’t matter and this will be filtered within scVelo?

My second question is: how should I import the dimension reductions (PCA and UMAP), clusters information, and additional metadata (like sample name), into my scVelo object (adata)?

Any help would be much appreciated!

[VolkerBergen]

Hi @Mevelo, as @ziyangliu93 said you’d definitely need to normalize your counts. These preprocessing steps are always needed

scv.pp.filter_and_normalize(adata, min_shared_counts=30, n_top_genes=2000)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)

In fact, if not done by the user already, norm. and moments are computed automatically before velocity estimation. So even if the user forgets about it, it will be processed correctly.

[VolkerBergen]

scv.read understands the following formats: h5ad, loom, xlsx, csv, tsv, tab, data, txt, mtx, mtx.gz, soft.gz. rds is R-specific and needs to be converted to one of these, e.g. loom.

Also see here if you run into Seurat/Loom issues.