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Description
Components needed for RNA velocity analysis and visualization inclusion into open pipelines:
- QC:
function:
scv.utils.show_proportions(adata)
- just to see if data has sufficient unspliced counts
-
Preprocessing and fit:
functions:
scv.pp.filter_and_normalize(adata, min_counts=20, min_counts_u=10)
scv.pp.moments(adata)
scv.tl.recover_dynamics(adata) -
QC2: Gene likelihood check:
- this is manual so far...
- some function on: adata.var[‘fit_likelihood’]
- assessment (distribution plot, table for certain cutoffs)
- potentially look at phase portraits of potential velocity genes
- set adata.var['velocity_genes’] as mask on adata.var[‘fit_likelihood’]
- Velocity calculation step 2:
function:
scv.tl.velocity(adata, mode=‘dynamical’)
- other modes possible, but this is probably best
- Velo visualization calculation:
functions:
scv.tl.velocity_embedding(adata, basis=‘umap’)
- other bases possible, like diffmap, phate, etc)
- Velocity visualization plotting:
function(s):
scv.pl.velocity_embedding(adata, basis=‘umap’, color=, arrow_size=2, arrow_length=5, alpha=0.2, size=20)
- also other plots like velocity_embedding_grid or velocity_embedding_stream
- should combine into 1 visualization component ideally
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