scVelo application on murine neural crest data

scVelo application on murine neural crest data#

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 = "mouse_neural_crest"

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

Velocity pipeline#

for i in range(1, 5):
    adata = sc.read_h5ad(DATA_DIR / DATASET / "processed" / f"adata_stage{i}_processed_velo_all_regulons.h5ad")

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

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

    if SAVE_DATA:
        adata.write_h5ad(DATA_DIR / DATASET / "processed" / f"adata_run_stage_{i}_scvelo_all_regulons.h5ad")

recovering dynamics (using 1/128 cores)

    finished (0:03:14) --> added 
    'fit_pars', fitted parameters for splicing dynamics (adata.var)
computing velocities
    finished (0:00:01) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
recovering dynamics (using 1/128 cores)

    finished (0:03:51) --> added 
    'fit_pars', fitted parameters for splicing dynamics (adata.var)
computing velocities
    finished (0:00:02) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
recovering dynamics (using 1/128 cores)

    finished (0:04:23) --> added 
    'fit_pars', fitted parameters for splicing dynamics (adata.var)
computing velocities
    finished (0:00:02) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
recovering dynamics (using 1/128 cores)

    finished (0:06:36) --> added 
    'fit_pars', fitted parameters for splicing dynamics (adata.var)
computing velocities
    finished (0:00:04) --> added 
    'velocity', velocity vectors for each individual cell (adata.layers)
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