scVelo-based analyis of pancreatic endocrine data

Notebook runs scVelo’s dynamical model on pancreatic endocrine dataset.

Library imports

import numpy as np

import scanpy as sc
import scvelo as scv

from rgv_tools import DATA_DIR

/home/icb/weixu.wang/miniconda3/envs/regvelo_test/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_csv from `anndata` is deprecated. Import anndata.io.read_csv instead.
  warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/regvelo_test/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_excel from `anndata` is deprecated. Import anndata.io.read_excel instead.
  warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/regvelo_test/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_hdf from `anndata` is deprecated. Import anndata.io.read_hdf instead.
  warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/regvelo_test/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_loom from `anndata` is deprecated. Import anndata.io.read_loom instead.
  warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/regvelo_test/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_mtx from `anndata` is deprecated. Import anndata.io.read_mtx instead.
  warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/regvelo_test/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_text from `anndata` is deprecated. Import anndata.io.read_text instead.
  warnings.warn(msg, FutureWarning)
/home/icb/weixu.wang/miniconda3/envs/regvelo_test/lib/python3.10/site-packages/anndata/utils.py:429: FutureWarning: Importing read_umi_tools from `anndata` is deprecated. Import anndata.io.read_umi_tools instead.
  warnings.warn(msg, FutureWarning)

Constants

DATASET = "pancreatic_endocrinogenesis"

SAVE_DATA = True
if SAVE_DATA:
    (DATA_DIR / DATASET / "processed").mkdir(parents=True, exist_ok=True)

Data loading

adata = sc.read_h5ad(DATA_DIR / DATASET / "processed" / "adata_preprocessed.h5ad")

Velocity pipeline

scv.tl.recover_dynamics(adata, fit_scaling=False, var_names=adata.var_names)
adata.var["fit_scaling"] = 1.0

recovering dynamics (using 1/112 cores)

    finished (0:03:41) --> added 
    'fit_pars', fitted parameters for splicing dynamics (adata.var)

scv.tl.velocity(adata, mode="dynamical", min_likelihood=-np.inf, min_r2=None)

computing velocities
    finished (0:00:03) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)

Save dataset

if SAVE_DATA:
    adata.write_h5ad(DATA_DIR / DATASET / "processed" / "adata_run_scvelo.h5ad")