Correlation benchmark on cell cycle

Notebook benchmarks GRN inference using correlation on cell cycling dataset

Library imports

import pandas as pd

import anndata as ad

from rgv_tools import DATA_DIR
from rgv_tools.benchmarking import get_grn_auroc_cc

/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 = "cell_cycle"

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

Data loading

adata = ad.io.read_h5ad(DATA_DIR / DATASET / "processed" / "adata_processed.h5ad")
adata

AnnData object with n_obs × n_vars = 1146 × 395
    obs: 'phase', 'fucci_time', 'initial_size_unspliced', 'initial_size_spliced', 'initial_size', 'n_counts'
    var: 'ensum_id', 'gene_count_corr', 'means', 'dispersions', 'dispersions_norm', 'highly_variable', 'velocity_gamma', 'velocity_qreg_ratio', 'velocity_r2', 'velocity_genes'
    uns: 'log1p', 'neighbors', 'pca', 'umap', 'velocity_params'
    obsm: 'X_pca', 'X_umap'
    varm: 'PCs', 'true_skeleton'
    layers: 'Ms', 'Mu', 'spliced', 'total', 'unspliced', 'velocity'
    obsp: 'connectivities', 'distances'

GRN pipeline

grn_estimate = adata.to_df(layer="Ms").corr().abs().values

grn_correlation = [get_grn_auroc_cc(ground_truth=adata.varm["true_skeleton"].toarray(), estimated=grn_estimate.T)]

if SAVE_DATA:
    pd.DataFrame({"grn": grn_correlation}).to_parquet(
        path=DATA_DIR / DATASET / "results" / "correlation_correlation.parquet"
    )