File size: 4,137 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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | """Correlation of estimated gamma with published mRNA half-lives."""
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 correlate_with_halflives(
adata: AnnData,
halflives_df: pd.DataFrame,
gene_col: str = "gene_symbol",
halflife_col: str = "half_life_hours",
min_gamma_fraction: float = 0.1,
case_insensitive: bool = True,
) -> dict:
"""Correlate per-gene median gamma with published mRNA half-lives.
Expects a negative correlation: high gamma (fast degradation) should
correspond to short half-lives.
Parameters
----------
adata
Annotated data matrix with ``gamma`` layer.
halflives_df
DataFrame with gene symbols and half-life measurements.
gene_col
Column name for gene symbols in ``halflives_df``.
halflife_col
Column name for half-life values in ``halflives_df``.
min_gamma_fraction
Minimum fraction of cells with gamma > 0 for a gene to be
included in the correlation (default 0.1). Genes with too
few unspliced reads produce unreliable gamma estimates.
case_insensitive
Match gene symbols case-insensitively (default True). Useful
for cross-species comparisons (mouse Titlecase vs human UPPER).
Returns
-------
dict with keys: ``spearman_r``, ``spearman_p``, ``pearson_r``,
``pearson_p``, ``n_genes``, ``n_genes_unfiltered``, ``matched_genes``.
"""
require_layers(adata, GAMMA)
gamma = get_layer(adata, GAMMA)
# Filter genes: require minimum fraction of cells with non-zero gamma
nonzero_frac = (gamma > 0).mean(axis=0)
gene_mask = nonzero_frac >= min_gamma_fraction
median_gamma = np.median(gamma, axis=0)
gene_names = adata.var_names.tolist()
gamma_series = pd.Series(median_gamma, index=gene_names)
mask_series = pd.Series(gene_mask, index=gene_names)
hl_series = halflives_df.set_index(gene_col)[halflife_col]
if case_insensitive:
# Build uppercase-to-original mapping, match via uppercase
gamma_upper = {g.upper(): g for g in gene_names}
hl_upper = {}
for g in hl_series.index:
if isinstance(g, str):
hl_upper[g.upper()] = g
shared_upper = set(gamma_upper.keys()) & set(hl_upper.keys())
# Map back to original names
shared_all = pd.Index([gamma_upper[u] for u in shared_upper])
# Rebuild hl_series indexed by adata gene names
hl_remap = {gamma_upper[u]: hl_series[hl_upper[u]] for u in shared_upper}
hl_series = pd.Series(hl_remap)
else:
shared_all = gamma_series.index.intersection(hl_series.index)
# Apply gene quality filter
shared = shared_all[mask_series[shared_all].values]
n_unfiltered = len(shared_all)
if len(shared) < 3:
return {
"spearman_r": np.nan,
"spearman_p": np.nan,
"pearson_r": np.nan,
"pearson_p": np.nan,
"n_genes": len(shared),
"n_genes_unfiltered": n_unfiltered,
"matched_genes": shared.tolist(),
}
g = gamma_series[shared].values.astype(float)
h = hl_series[shared].values.astype(float)
# Remove NaN/Inf
valid = np.isfinite(g) & np.isfinite(h) & (g > 0) & (h > 0)
g, h = g[valid], h[valid]
if len(g) < 3:
return {
"spearman_r": np.nan,
"spearman_p": np.nan,
"pearson_r": np.nan,
"pearson_p": np.nan,
"n_genes": 0,
"n_genes_unfiltered": n_unfiltered,
"matched_genes": [],
}
sp_r, sp_p = stats.spearmanr(g, h)
pe_r, pe_p = stats.pearsonr(np.log1p(g), np.log1p(h))
return {
"spearman_r": float(sp_r),
"spearman_p": float(sp_p),
"pearson_r": float(pe_r),
"pearson_p": float(pe_p),
"n_genes": int(valid.sum()),
"n_genes_unfiltered": n_unfiltered,
"matched_genes": shared[valid].tolist(),
}
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