scVelo-based analyis of zebrafish data

Notebook runs scVelo’s dynamical model on zebrafish 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 = "zebrafish"

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 1/128 cores)
WARNING: Unable to create progress bar. Consider installing `tqdm` as `pip install tqdm` and `ipywidgets` as `pip install ipywidgets`,
or disable the progress bar using `show_progress_bar=False`.
    finished (0:03:58) --> 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 (0:00:00) --> 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")