x, y = scv.utils.get_cell_transitions(adata,
basis='PHATE_seurat',
starting_cell=1000)
ax = scv.pl.velocity_graph(adata,
c='lightgrey',
edge_width=.05,
show=False,
basis="PHATE_seurat")
ax = scv.pl.scatter(adata,
x=x,
y=y,
s=120,
c='ascending',
cmap='gnuplot',
ax=ax,
basis='PHATE_seurat')
adata.uns['neighbors']['distances'] = adata.obsp['distances']
adata.uns['neighbors']['connectivities'] = adata.obsp['connectivities']
scv.tl.paga(adata, groups='seurat_clusters')
df = scv.get_df(adata, 'paga/transitions_confidence', precision=2).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')
scv.pl.paga(adata,
basis='umap_seurat',
size=50,
alpha=.1,
min_edge_width=2,
node_size_scale=1.5,
dpi=800)
def export_text_data(velocity_data, args):
driver_genes = scvelo.get_df(velocity_data, "rank_dynamical_genes/names")
driver_genes.to_csv(args.output + "putative_driver_genes.tsv", sep="\t")
scv.pl.velocity_embedding_stream(adata,
basis="umap",
color='latent_time',
dpi=800,
color_map='gnuplot')
scv.pl.velocity_embedding_stream(adata,
basis="umap",
color='root_cells',
dpi=300,
min_mass=0,
density=1,
color_map='gnuplot')
scv.tl.paga(adata, groups='velocity_clusters')
df = scv.get_df(adata, 'paga/transitions_confidence', precision=2).T
df.style.background_gradient(cmap='Blues').format('{:.2g}')
scv.pl.paga(adata,
basis='umap',
size=50,
alpha=.1,
min_edge_width=2,
node_size_scale=1.5,
dpi=800)
scv.pl.velocity(adata, "Msn", dpi=800)
scv.tl.rank_velocity_genes(adata, min_corr=.3)
df = scv.DataFrame(adata.uns['rank_velocity_genes']['names'])
df.head()
dpi=300,
figsize=(10, 10),
color=clusters,
save='{}.png'.format(key),
title=key)
## Top drivers.
top_genes = samples['HT29_EV'].var['fit_likelihood'].sort_values(
ascending=False).index
scv.pl.scatter(samples['HT29_EV'],
basis=top_genes[:15],
ncols=5,
frameon=False)
## Top likelyhood genes.
outdir = 'results/trajectory/velocity_dynamical/velocity_genes_dynamical'
if not os.path.exists(outdir):
os.makedirs(outdir)
scv.settings.figdir = outdir
for key, value in samples.items():
scv.tl.rank_dynamical_genes(value, groupby=clusters)
df = scv.get_df(value, 'rank_dynamical_genes/names')
df.to_csv("{}/{}.tsv".format(outdir, key),
sep='\t',
header=True,
index=False)