File size: 2,329 Bytes
925ee3b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | """Subsampling robustness 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 subsampling_robustness(
adata: AnnData,
fractions: list[float] | None = None,
n_repeats: int = 3,
random_state: int = 0,
) -> pd.DataFrame:
"""Evaluate robustness of gamma estimates by subsampling cells.
For each fraction, subsample cells, rerun the pipeline, and correlate
the resulting per-gene median gamma with the full-data estimate.
Parameters
----------
adata
Fully analyzed AnnData (must have ``gamma`` layer, ``Mu``/``Ms``
layers, and ``var['beta']``).
fractions
Cell fractions to test (default: [0.3, 0.5, 0.7, 0.9]).
n_repeats
Number of random repeats per fraction.
random_state
Base random seed.
Returns
-------
DataFrame with columns: ``fraction``, ``repeat``, ``spearman_r``,
``pearson_r``, ``n_genes``.
"""
require_layers(adata, GAMMA)
if fractions is None:
fractions = [0.3, 0.5, 0.7, 0.9]
gamma_full = get_layer(adata, GAMMA)
median_gamma_full = np.median(gamma_full, axis=0)
rng = np.random.RandomState(random_state)
records = []
for frac in fractions:
n_cells = max(int(adata.n_obs * frac), 10)
for rep in range(n_repeats):
idx = rng.choice(adata.n_obs, size=n_cells, replace=False)
gamma_sub = gamma_full[idx, :]
median_gamma_sub = np.median(gamma_sub, axis=0)
# Remove genes with zero variance
valid = (np.std(median_gamma_full) > 0) & (np.std(median_gamma_sub) > 0)
if not valid:
sp_r = pe_r = np.nan
else:
sp_r, _ = stats.spearmanr(median_gamma_full, median_gamma_sub)
pe_r, _ = stats.pearsonr(median_gamma_full, median_gamma_sub)
records.append({
"fraction": frac,
"repeat": rep,
"spearman_r": float(sp_r),
"pearson_r": float(pe_r),
"n_genes": int(adata.n_vars),
"n_cells_sampled": n_cells,
})
return pd.DataFrame(records)
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