| """Cross-dataset consistency analysis.""" |
|
|
| from __future__ import annotations |
|
|
| import numpy as np |
| import pandas as pd |
| from anndata import AnnData |
| from scipy import stats |
|
|
| from .._constants import GAMMA |
| from .._utils import get_layer, require_layers |
|
|
|
|
| def cross_dataset_consistency( |
| adatas_dict: dict[str, AnnData], |
| case_insensitive: bool = True, |
| ) -> pd.DataFrame: |
| """Compare per-gene median gamma across multiple datasets. |
| |
| For each pair of datasets, computes Spearman and Pearson correlations |
| of per-gene median gamma on their shared gene set. |
| |
| Parameters |
| ---------- |
| adatas_dict |
| Dictionary mapping dataset name to analyzed AnnData |
| (each must have ``gamma`` layer). |
| case_insensitive |
| Match gene symbols case-insensitively (default True). Useful |
| for cross-species comparisons (mouse Titlecase vs human UPPER). |
| |
| Returns |
| ------- |
| DataFrame with columns: ``dataset_a``, ``dataset_b``, ``n_shared_genes``, |
| ``spearman_r``, ``pearson_r``. |
| """ |
| names = sorted(adatas_dict.keys()) |
| |
| medians = {} |
| for name in names: |
| adata = adatas_dict[name] |
| require_layers(adata, GAMMA) |
| gamma = get_layer(adata, GAMMA) |
| medians[name] = pd.Series( |
| np.median(gamma, axis=0), index=adata.var_names |
| ) |
|
|
| records = [] |
| for i, name_a in enumerate(names): |
| for name_b in names[i + 1:]: |
| if case_insensitive: |
| |
| map_a = {} |
| for g in medians[name_a].index: |
| if isinstance(g, str): |
| map_a[g.upper()] = g |
| map_b = {} |
| for g in medians[name_b].index: |
| if isinstance(g, str): |
| map_b[g.upper()] = g |
| shared_upper = set(map_a.keys()) & set(map_b.keys()) |
| n_shared = len(shared_upper) |
|
|
| if n_shared < 3: |
| sp_r = pe_r = np.nan |
| else: |
| ga = np.array([medians[name_a][map_a[u]] for u in shared_upper], dtype=float) |
| gb = np.array([medians[name_b][map_b[u]] for u in shared_upper], dtype=float) |
| valid = np.isfinite(ga) & np.isfinite(gb) |
| ga, gb = ga[valid], gb[valid] |
| n_shared = len(ga) |
|
|
| if len(ga) < 3: |
| sp_r = pe_r = np.nan |
| else: |
| sp_r, _ = stats.spearmanr(ga, gb) |
| pe_r, _ = stats.pearsonr(ga, gb) |
| else: |
| shared = medians[name_a].index.intersection(medians[name_b].index) |
| n_shared = len(shared) |
|
|
| if n_shared < 3: |
| sp_r = pe_r = np.nan |
| else: |
| ga = medians[name_a][shared].values.astype(float) |
| gb = medians[name_b][shared].values.astype(float) |
| valid = np.isfinite(ga) & np.isfinite(gb) |
| ga, gb = ga[valid], gb[valid] |
|
|
| if len(ga) < 3: |
| sp_r = pe_r = np.nan |
| else: |
| sp_r, _ = stats.spearmanr(ga, gb) |
| pe_r, _ = stats.pearsonr(ga, gb) |
|
|
| records.append({ |
| "dataset_a": name_a, |
| "dataset_b": name_b, |
| "n_shared_genes": n_shared, |
| "spearman_r": float(sp_r) if not np.isnan(sp_r) else np.nan, |
| "pearson_r": float(pe_r) if not np.isnan(pe_r) else np.nan, |
| }) |
|
|
| return pd.DataFrame(records) |
|
|
