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Microarray-specific preprocessing and analysis workflow helpers.
Reusable, dataset-agnostic logic for datasets declared with
data_type: microarray_log_expression or microarray_log_ratio in their manifest.
All dataset-specific parameters (platform, gene_symbol_column, collapse_method)
come from the manifest features block — not hardcoded here.
Manifest helpers (implemented)
-------------------------------
get_collapse_params Extract probe collapse parameters from a manifest.
check_probe_collapse_needed Simple predicate: does this manifest require collapse?
recommend_analysis_path Return ("A"|"B", reason_str) for a manifest.
Expression loading and alignment
---------------------------------
load_expression_matrix Load CSV/TSV as samples × features DataFrame.
harmonize_expression_and_metadata
Align expression rows to metadata sample IDs.
Data characterisation
---------------------
detect_log_scale Heuristic: is the matrix likely log-transformed?
Gene-level aggregation
----------------------
collapse_duplicate_genes Collapse duplicate gene-name columns in an already
gene-symbol-labelled matrix (not probe→gene mapping;
for that use decoupler_collapse_probes_to_genes).
Statistical analysis
--------------------
prepare_gene_level_statistics
Welch's t-test with BH FDR on pre-normalised data.
Outputs gene, statistic, pvalue, padj, mean_test,
mean_control, log2fc_like.
NOT for raw counts — do not use DESeq2 here.
DE statistical helpers (used by src/tools/rna.py)
--------------------------------------------------
run_welch_ttest Vectorized Welch's t-test; returns DESeq2-schema DataFrame.
run_limma Limma moderated t-test via rpy2; returns DESeq2-schema DataFrame.
"""
from __future__ import annotations
from typing import Any
import numpy as np
import pandas as pd
from src.workflows.metadata_validation import subset_and_require_group
def get_collapse_params(manifest: "dict | object") -> dict[str, Any]:
"""
Extract probe-to-gene collapse parameters from a manifest.
Reads from the new-schema 'feature_mapping' block if present; falls back
to the legacy 'features' block so old YAML manifests still work.
Parameters
----------
manifest:
A parsed manifest dict OR a DatasetManifest dataclass instance.
Returns
-------
dict with keys:
required (bool) — whether collapse is needed.
method (str) — "mean", "max", or "most_variable".
gene_symbol_column (str|None) — var column with gene symbols, or None.
multi_gene_policy (str) — "drop" or "first".
"""
# Support both DatasetManifest dataclass and raw dict
if hasattr(manifest, "feature_mapping"):
fm = manifest.feature_mapping or {}
else:
fm = manifest.get("feature_mapping") or manifest.get("features") or {}
return {
"required": bool(fm.get("requires_collapse") or fm.get("collapse_required", False)),
"method": fm.get("collapse_method", "mean"),
"gene_symbol_column": fm.get("gene_symbol_column"),
"multi_gene_policy": fm.get("multi_gene_policy", "drop"),
}
def check_probe_collapse_needed(manifest: dict) -> bool:
"""Return True if the manifest declares that probe collapse is required."""
return get_collapse_params(manifest)["required"]
def recommend_analysis_path(manifest: "dict | object") -> tuple[str, str]:
"""
Return (path, reason) where path is "A" or "B".
"A" = raw integer counts → DESeq2 pipeline.
"B" = pre-normalized/log → ttest or limma pipeline.
The path is read from the manifest; this function adds an agent-readable
reason string describing why and which tool to use.
"""
# Support DatasetManifest (uses .analysis_path property) and raw dicts
if hasattr(manifest, "analysis_path"):
path = str(manifest.analysis_path).upper()
data_type = getattr(manifest, "data_level", "unknown")
else:
path = str(manifest.get("analysis_path", "B")).upper()
data_type = manifest.get("data_level") or manifest.get("data_type", "unknown")
if path == "A":
reason = (
"raw integer counts — use decoupler_preprocess_data then "
"decoupler_differential_expression(method='deseq2')"
)
else:
reason = (
f"{data_type} (pre-normalized) — skip decoupler_preprocess_data; "
"use decoupler_differential_expression(method='ttest') or method='limma'"
)
return path, reason
# ---------------------------------------------------------------------------
# Expression loading and alignment
# ---------------------------------------------------------------------------
def load_expression_matrix(expression_path: str) -> dict[str, Any]:
"""
Load a gene expression matrix from a CSV or TSV file.
Expects samples as rows and features/genes as columns, with sample IDs
in the first column (used as the DataFrame index).
Parameters
----------
expression_path:
Local path or http(s)/ftp URL to a .csv, .tsv, or .txt file.
Separator is inferred from the file extension: tab for .tsv/.txt,
comma for .csv and all others.
Returns
-------
dict with keys:
dataframe (pd.DataFrame) — samples × features, numeric only.
n_samples (int)
n_features (int)
sample_id_sample (list[str]) — first 5 row index values.
feature_id_sample (list[str]) — first 5 column names.
warnings (list[str]) — non-fatal issues found during loading.
Raises
------
FileNotFoundError if expression_path does not exist.
"""
from pathlib import Path
is_url = expression_path.startswith(("http://", "https://", "ftp://"))
if not is_url and not Path(expression_path).exists():
raise FileNotFoundError(f"Expression file not found: {expression_path}")
# Infer separator from the extension (strip any URL query string first).
suffix = Path(expression_path.split("?", 1)[0]).suffix.lower()
sep = "\t" if suffix in (".tsv", ".txt") else ","
# pandas reads http(s)/ftp URLs natively; pass the raw string so the URL is
# not mangled by Path() (which collapses "https://" → "https:/").
df = pd.read_csv(expression_path, sep=sep, index_col=0)
warnings: list[str] = []
# Drop non-numeric columns silently with a warning
non_numeric = [c for c in df.columns if not pd.api.types.is_numeric_dtype(df[c])]
if non_numeric:
df = df.drop(columns=non_numeric)
warnings.append(
f"Dropped {len(non_numeric)} non-numeric column(s): "
f"{non_numeric[:5]}{'...' if len(non_numeric) > 5 else ''}"
)
n_samples, n_features = df.shape
# Orientation heuristic: far more rows than columns suggests transposition
if n_features > 0 and n_samples > n_features * 10 and n_samples > 50:
warnings.append(
f"Matrix has {n_samples} rows and {n_features} columns. "
"For microarray data, samples should be rows and genes columns. "
"This may be transposed — verify orientation before proceeding."
)
return {
"dataframe": df,
"n_samples": n_samples,
"n_features": n_features,
"sample_id_sample": list(df.index[:5].astype(str)),
"feature_id_sample": list(df.columns[:5].astype(str)),
"warnings": warnings,
}
def harmonize_expression_and_metadata(
expression_df: "pd.DataFrame",
metadata_df: "pd.DataFrame",
sample_id_column: str | None = None,
) -> dict[str, Any]:
"""
Align expression rows to metadata sample IDs.
Finds the intersection of sample IDs between the expression DataFrame index
and the metadata DataFrame (either its index or a specified column), drops
samples present in only one source, and returns both DataFrames sorted to
the same order.
Parameters
----------
expression_df:
Samples × features expression DataFrame.
metadata_df:
Sample metadata DataFrame.
sample_id_column:
Column in metadata_df whose values are sample IDs. If None, the
metadata_df index is used as the sample ID source.
Returns
-------
dict with keys:
expression_df (pd.DataFrame) — aligned expression.
metadata_df (pd.DataFrame) — aligned metadata.
n_aligned_samples (int)
n_expression_only (int) — samples in expression but not metadata.
n_metadata_only (int) — samples in metadata but not expression.
expression_only_samples (list[str])
metadata_only_samples (list[str])
warnings (list[str])
valid (bool) — True if ≥2 aligned samples remain.
"""
warnings: list[str] = []
# Resolve metadata index
if sample_id_column is not None:
if sample_id_column not in metadata_df.columns:
raise ValueError(
f"sample_id_column '{sample_id_column}' not found in metadata. "
f"Available columns: {list(metadata_df.columns)}"
)
meta_indexed = metadata_df.set_index(sample_id_column)
else:
meta_indexed = metadata_df
expr_ids = set(expression_df.index.astype(str))
meta_ids = set(meta_indexed.index.astype(str))
common = sorted(expr_ids & meta_ids)
expr_only = sorted(expr_ids - meta_ids)
meta_only = sorted(meta_ids - expr_ids)
if expr_only:
warnings.append(
f"{len(expr_only)} expression sample(s) have no metadata and will be dropped: "
f"{expr_only[:5]}{'...' if len(expr_only) > 5 else ''}"
)
if meta_only:
warnings.append(
f"{len(meta_only)} metadata sample(s) have no expression data and will be dropped: "
f"{meta_only[:5]}{'...' if len(meta_only) > 5 else ''}"
)
if not common:
warnings.append(
"No common samples found. "
"Check that sample IDs in the expression index match those in the metadata."
)
aligned_expr = expression_df.loc[
expression_df.index.astype(str).isin(common)
].sort_index()
aligned_meta = meta_indexed.loc[
meta_indexed.index.astype(str).isin(common)
].sort_index()
return {
"expression_df": aligned_expr,
"metadata_df": aligned_meta,
"n_aligned_samples": len(common),
"n_expression_only": len(expr_only),
"n_metadata_only": len(meta_only),
"expression_only_samples": expr_only[:20],
"metadata_only_samples": meta_only[:20],
"warnings": warnings,
"valid": len(common) >= 2,
}
# ---------------------------------------------------------------------------
# Data characterisation
# ---------------------------------------------------------------------------
def detect_log_scale(expression_df: "pd.DataFrame") -> dict[str, Any]:
"""
Heuristic check for whether an expression matrix is likely log-transformed.
Uses value range, integrality, and sign to classify the data. This is a
diagnostic helper, not a transformation. Always verify the result against
the dataset's documented processing.
Returns
-------
dict with keys:
likely_log_scale (bool) — True if heuristics suggest log scale.
likely_log2 (bool) — True if specifically log2 is likely.
has_negative_values (bool)
value_min (float)
value_max (float)
value_median (float)
value_mean (float)
fraction_integer (float) — fraction of values that are whole numbers.
diagnostic_notes (list[str]) — reasoning behind the classification.
warnings (list[str]) — caveats about heuristic reliability.
"""
flat = expression_df.values.flatten()
flat = flat[~(
(flat != flat) | # NaN
(flat == float("inf")) |
(flat == float("-inf"))
)]
if len(flat) == 0:
return {
"likely_log_scale": False,
"likely_log2": False,
"has_negative_values": False,
"value_min": None,
"value_max": None,
"value_median": None,
"value_mean": None,
"fraction_integer": None,
"diagnostic_notes": ["No finite numeric values found in the matrix."],
"warnings": ["Cannot determine scale: matrix contains no finite values."],
}
vmin = float(np.min(flat))
vmax = float(np.max(flat))
vmed = float(np.median(flat))
vmean = float(np.mean(flat))
frac_int = float(np.mean(flat == np.floor(flat)))
has_neg = bool(vmin < 0)
notes: list[str] = []
caveats: list[str] = []
likely_log = False
likely_log2 = False
if has_neg:
likely_log = True
notes.append(
f"Negative values present (min={vmin:.3f}) — consistent with "
"log-ratio microarray data centred near 0."
)
elif frac_int > 0.9 and vmax > 100:
likely_log = False
notes.append(
f"{frac_int:.0%} of values are integers and max={vmax:.0f} — "
"consistent with raw integer counts, not log-transformed."
)
elif vmax < 30 and frac_int < 0.1:
likely_log = True
likely_log2 = True
notes.append(
f"Max value {vmax:.2f} < 30, values are non-integer — "
"consistent with log2-normalised microarray expression "
"(log2 CPM or log2 intensity typically ranges 4–18)."
)
elif vmax < 50:
likely_log = True
notes.append(
f"Max value {vmax:.2f} — plausibly log-transformed, "
"but scale is ambiguous (could be log10 or natural log)."
)
else:
notes.append(
f"Max value {vmax:.2f} > 50 with non-integer values — "
"may be RPKM, TPM, or another non-log normalised form. "
"Verify against the dataset documentation."
)
caveats.append(
"This is a value-range heuristic. It cannot distinguish log2 from "
"log10 or natural log, and can be fooled by outliers or mixed data."
)
return {
"likely_log_scale": likely_log,
"likely_log2": likely_log2,
"has_negative_values": has_neg,
"value_min": round(vmin, 4),
"value_max": round(vmax, 4),
"value_median": round(vmed, 4),
"value_mean": round(vmean, 4),
"fraction_integer": round(frac_int, 4),
"diagnostic_notes": notes,
"warnings": caveats,
}
# ---------------------------------------------------------------------------
# Gene-level aggregation
# ---------------------------------------------------------------------------
def collapse_duplicate_genes(
expression_df: "pd.DataFrame",
method: str = "mean",
) -> dict[str, Any]:
"""
Collapse duplicate gene-name columns in a gene-symbol-labelled matrix.
This function is for matrices whose columns are already gene symbols with
some genes appearing more than once (e.g. after imperfect probe annotation).
It is NOT a probe-to-gene mapping step — for that, use
decoupler_collapse_probes_to_genes via the MCP tool layer.
If no duplicate column names are found, the DataFrame is returned unchanged.
Parameters
----------
expression_df:
Samples × genes DataFrame. Column names must be gene symbols.
method:
Aggregation method for duplicates.
"mean" — average expression across all duplicate columns.
"max" — keep the column with the highest mean expression.
"most_variable" — keep the column with the highest variance.
Returns
-------
dict with keys:
dataframe (pd.DataFrame) — samples × unique genes.
n_features_before (int)
n_features_after (int)
n_duplicated_genes (int) — gene names appearing >1 time.
duplicated_gene_sample (list[str]) — up to 5 examples.
method (str)
warnings (list[str])
"""
if method not in ("mean", "max", "most_variable"):
raise ValueError(
f"method must be 'mean', 'max', or 'most_variable', got '{method}'"
)
n_before = expression_df.shape[1]
col_counts = expression_df.columns.value_counts()
dup_genes = col_counts[col_counts > 1].index.tolist()
n_dup = len(dup_genes)
if n_dup == 0:
return {
"dataframe": expression_df,
"n_features_before": n_before,
"n_features_after": n_before,
"n_duplicated_genes": 0,
"duplicated_gene_sample": [],
"method": method,
"warnings": [
"No duplicate gene names found; DataFrame returned unchanged."
],
}
w = [
f"{n_dup} gene name(s) appear more than once; collapsing with method='{method}'.",
"This collapses duplicate column names only. If columns are still probe IDs, "
"use decoupler_collapse_probes_to_genes first.",
]
if method == "mean":
collapsed = expression_df.T.groupby(level=0).mean().T
elif method == "max":
# Per gene: keep the column position with the highest mean expression.
# Must use positional indexing (iloc) — label-based indexing on a
# DataFrame with duplicate column names returns ALL matching columns,
# causing a length mismatch when reassigning column names.
col_names = expression_df.columns.tolist()
means_arr = expression_df.mean(axis=0).values
best_pos: dict[str, int] = {}
for i, name in enumerate(col_names):
if name not in best_pos or means_arr[i] > means_arr[best_pos[name]]:
best_pos[name] = i
ordered = list(dict.fromkeys(col_names)) # unique, first-seen order
collapsed = expression_df.iloc[:, [best_pos[g] for g in ordered]].copy()
collapsed.columns = ordered
else: # most_variable
# Per gene: keep the column position with the highest variance.
# Variance (not mean) carries the differential signal.
col_names = expression_df.columns.tolist()
var_arr = expression_df.var(axis=0).values
best_pos = {}
for i, name in enumerate(col_names):
if name not in best_pos or var_arr[i] > var_arr[best_pos[name]]:
best_pos[name] = i
ordered = list(dict.fromkeys(col_names))
collapsed = expression_df.iloc[:, [best_pos[g] for g in ordered]].copy()
collapsed.columns = ordered
return {
"dataframe": collapsed,
"n_features_before": n_before,
"n_features_after": collapsed.shape[1],
"n_duplicated_genes": n_dup,
"duplicated_gene_sample": dup_genes[:5],
"method": method,
"warnings": w,
}
# ---------------------------------------------------------------------------
# Statistical analysis
# ---------------------------------------------------------------------------
def prepare_gene_level_statistics(
expression_df: "pd.DataFrame",
metadata_df: "pd.DataFrame",
group_column: str,
test_group: str,
control_group: str,
subset_query: str | None = None,
method: str = "welch_ttest",
) -> dict[str, Any]:
"""
Compute gene-level differential statistics for pre-normalised microarray data.
Uses Welch's t-test (unequal-variance) with Benjamini-Hochberg FDR correction.
Outputs one row per gene with statistic, pvalue, padj, group means, and a
log2fc_like column that is a true log2 fold-change only if the input matrix
is in log2 scale.
This function is for normalised microarray-like expression (log-intensity,
log-CPM, log-ratio). Do NOT use on raw integer counts — use DESeq2 for those.
Parameters
----------
expression_df:
Samples × genes DataFrame. Index must match metadata_df index.
metadata_df:
Sample metadata DataFrame. Index must match expression_df index.
group_column:
Column in metadata_df containing the group labels.
test_group:
Label of the foreground / test condition.
control_group:
Label of the reference / background condition.
subset_query:
Optional pandas query string applied to metadata_df before grouping,
e.g. ``"tissue == 'tumor'"``.
method:
Statistical method. Currently only "welch_ttest" is supported.
Returns
-------
dict with keys:
dataframe (pd.DataFrame) — genes × stats, sorted by padj.
Columns: statistic, pvalue, padj,
mean_test, mean_control, log2fc_like.
Index name: "gene".
n_genes (int)
n_test_samples (int)
n_control_samples (int)
method (str)
group_column, test_group, control_group, subset_query
significant_genes_05 (int) — genes with padj < 0.05
significant_genes_01 (int) — genes with padj < 0.01
warnings (list[str])
Raises
------
ValueError if group_column is missing, groups are not found, or either
group has fewer than 2 samples.
"""
import warnings as _w
from scipy import stats
from statsmodels.stats.multitest import multipletests
if method != "welch_ttest":
raise ValueError(f"method must be 'welch_ttest', got '{method}'")
run_warnings = [
"log2fc_like = mean(test) − mean(control). This equals log2 fold-change "
"only when input values are in log2 scale. Verify with detect_log_scale().",
"Welch's t-test assumes approximately normal distribution within each group. "
"For n < 5, treat p-values as approximate.",
"Genes with zero variance in either group are assigned statistic=0, pvalue=1.",
]
# Subset (optional) + require the group column (shared helper).
working_meta = subset_and_require_group(metadata_df, subset_query, group_column)
# ── Align expression to (subset) metadata ────────────────────────────
common_idx = expression_df.index.intersection(working_meta.index)
if len(common_idx) == 0:
raise ValueError(
"No common samples between expression index and metadata index "
"after subsetting. Check that indices are aligned."
)
aligned_expr = expression_df.loc[common_idx]
aligned_meta = working_meta.loc[common_idx]
# ── Build group masks ────────────────────────────────────────────────
available = aligned_meta[group_column].unique().tolist()
if test_group not in available:
raise ValueError(
f"test_group '{test_group}' not found in '{group_column}'. "
f"Available: {sorted(str(g) for g in available)}"
)
if control_group not in available:
raise ValueError(
f"control_group '{control_group}' not found in '{group_column}'. "
f"Available: {sorted(str(g) for g in available)}"
)
test_mask = aligned_meta[group_column] == test_group
ctrl_mask = aligned_meta[group_column] == control_group
n_test = int(test_mask.sum())
n_ctrl = int(ctrl_mask.sum())
if n_test < 2:
raise ValueError(
f"test_group '{test_group}' has only {n_test} sample(s) — "
"need at least 2 for Welch's t-test."
)
if n_ctrl < 2:
raise ValueError(
f"control_group '{control_group}' has only {n_ctrl} sample(s) — "
"need at least 2 for Welch's t-test."
)
if n_test < 5 or n_ctrl < 5:
run_warnings.append(
f"Small group sizes (test={n_test}, control={n_ctrl}). "
"Statistical power is limited; interpret results with caution."
)
# ── Vectorised Welch's t-test ────────────────────────────────────────
X_test = aligned_expr[test_mask].values # (n_test, n_genes)
X_ctrl = aligned_expr[ctrl_mask].values # (n_ctrl, n_genes)
with _w.catch_warnings():
_w.simplefilter("ignore", RuntimeWarning)
t_stats, p_vals = stats.ttest_ind(X_test, X_ctrl, axis=0, equal_var=False)
# Replace NaN / Inf from zero-variance genes before BH correction
t_stats = np.where(np.isfinite(t_stats), t_stats, 0.0)
p_vals = np.where(np.isfinite(p_vals), p_vals, 1.0)
_, padj, _, _ = multipletests(p_vals, method="fdr_bh")
mean_test = X_test.mean(axis=0)
mean_ctrl = X_ctrl.mean(axis=0)
log2fc_like = mean_test - mean_ctrl
genes = aligned_expr.columns.tolist()
result_df = pd.DataFrame(
{
"statistic": t_stats,
"pvalue": p_vals,
"padj": padj,
"mean_test": mean_test,
"mean_control": mean_ctrl,
"log2fc_like": log2fc_like,
},
index=genes,
)
result_df.index.name = "gene"
result_df = result_df.sort_values("padj")
return {
"dataframe": result_df,
"n_genes": len(genes),
"n_test_samples": n_test,
"n_control_samples": n_ctrl,
"method": method,
"group_column": group_column,
"test_group": test_group,
"control_group": control_group,
"subset_query": subset_query,
"significant_genes_05": int((padj < 0.05).sum()),
"significant_genes_01": int((padj < 0.01).sum()),
"warnings": run_warnings,
}
# ---------------------------------------------------------------------------
# Data type classification
# ---------------------------------------------------------------------------
def classify_expression_data_type(X_flat: "np.ndarray") -> "dict[str, Any]":
"""
Classify the expression data type from a flattened expression matrix.
Heuristics
----------
raw_counts: whole numbers, no negatives, max > 100.
log_expression: non-integer, all positive, max < 35 (log2 CPM/TPM scale).
log_ratio: has negatives, max < 15, median near 0 (two-color microarray).
unknown: none of the above.
Parameters
----------
X_flat:
1-D numpy array of all expression values (adata.X.flatten() or similar).
Returns
-------
dict with keys:
is_integer, has_negatives, value_min, value_max, value_mean, value_median,
likely_raw_counts, likely_log_expression, likely_log_ratio,
likely_log_transformed, data_type (str).
"""
is_integer = bool(np.all(X_flat == np.floor(X_flat)))
has_negatives = bool(np.any(X_flat < 0))
value_max = float(np.max(X_flat))
value_min = float(np.min(X_flat))
value_mean = float(np.mean(X_flat))
value_median = float(np.median(X_flat))
# Raw integer counts: whole numbers, no negatives, typically large values (>100)
likely_raw_counts = is_integer and not has_negatives and value_max > 100
# Single-channel log-expression (log2(CPM+1), log2(TPM+1), etc.):
# all positive, non-integer, upper range ~4–25
likely_log_expression = (not is_integer) and (not has_negatives) and (value_max < 35)
# Log-ratio microarray (two-color or quantile-normalised single-channel):
# centered near 0, has negatives, compact range (typically -5 to +5)
# mean(test) - mean(ctrl) on this scale IS a log2 fold-change directly
likely_log_ratio = has_negatives and (value_max < 15) and (abs(value_median) < 2)
# Combined flag — both sub-types go to Path B
likely_log_transformed = likely_log_expression or likely_log_ratio
if likely_raw_counts:
data_type = "raw_counts"
elif likely_log_ratio:
data_type = "log_ratio_microarray"
elif likely_log_expression:
data_type = "log_expression"
else:
data_type = "unknown"
return {
"is_integer": is_integer,
"has_negatives": has_negatives,
"value_min": round(value_min, 4),
"value_max": round(value_max, 4),
"value_mean": round(value_mean, 4),
"value_median": round(value_median, 4),
"likely_raw_counts": likely_raw_counts,
"likely_log_expression": likely_log_expression,
"likely_log_ratio": likely_log_ratio,
"likely_log_transformed": likely_log_transformed,
"data_type": data_type,
}
def detect_probe_like_features(
feature_names: "pd.Index | list[str]", sample_size: int = 50
) -> bool:
"""
Heuristic check for whether feature names look like probe IDs rather than gene symbols.
Checks the first sample_size feature names against known probe ID patterns:
- Affymetrix: starts with digits then underscore (e.g. "1553551_at")
- Illumina: ILMN_ prefix (e.g. "ILMN_1234567")
- Long numeric-only: 5+ digits (e.g. "3100001")
- Generic long probe: length > 12 with underscore (e.g. "A_23_P100001")
Returns True if more than 30% of the sampled features match any pattern.
Parameters
----------
feature_names:
Feature names to check (e.g. adata.var.index).
sample_size:
Number of features to sample from the start.
Returns
-------
bool — True if features look like probe IDs.
"""
sample = pd.Index(feature_names[: min(sample_size, len(feature_names))]).astype(str)
if len(sample) == 0:
return False
probe_like = (
sample.str.match(r"^\d+_")
| sample.str.match(r"^ILMN_\d")
| sample.str.match(r"^\d{5,}$")
| ((sample.str.len() > 12) & sample.str.contains("_"))
)
return bool(probe_like.sum() / len(sample) > 0.3)
# ---------------------------------------------------------------------------
# Statistical helpers for differential expression
# ---------------------------------------------------------------------------
def run_welch_ttest(
X_test: "np.ndarray", X_ctrl: "np.ndarray", genes: "list[str]"
) -> "pd.DataFrame":
"""
Welch's t-test vectorized across all genes with Benjamini-Hochberg FDR correction.
Assumes X is already in log-scale (log2 or similar), so mean(test) - mean(ctrl)
approximates log2 fold-change.
Genes with zero variance in either group return NaN from scipy; RuntimeWarnings are
suppressed and those genes are replaced with stat=0, pvalue=1 before BH correction
(treated as non-differentially-expressed).
Returns columns: log2FoldChange, stat, pvalue, padj — same schema as DESeq2 output.
"""
import warnings
from scipy import stats
from statsmodels.stats.multitest import multipletests
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
t_stats, p_vals = stats.ttest_ind(X_test, X_ctrl, axis=0, equal_var=False)
t_stats = np.where(np.isfinite(t_stats), t_stats, 0.0)
p_vals = np.where(np.isfinite(p_vals), p_vals, 1.0)
log2fc = np.mean(X_test, axis=0) - np.mean(X_ctrl, axis=0)
_, padj, _, _ = multipletests(p_vals, method="fdr_bh")
return pd.DataFrame(
{"log2FoldChange": log2fc, "stat": t_stats, "pvalue": p_vals, "padj": padj},
index=genes,
)
def run_limma(
X_test: "np.ndarray",
X_ctrl: "np.ndarray",
genes: "list[str]",
test_group: str,
control_group: str,
) -> "pd.DataFrame":
"""
Limma moderated t-test via Rscript subprocess — no rpy2 bridge.
After 4 failed rpy2 approaches (deprecated activate(), py2rpy conversion,
non-conformable arrays from round-trip, unknown conversion errors), we bypass
rpy2 entirely. The expression matrix is written to a temp CSV, limma runs
in a fresh Rscript process, and results are read back as CSV. Rscript is
installed via packages.txt (r-base) and has been independently verified to work.
Raises RuntimeError if Rscript/limma is unavailable — caller falls back to ttest.
Returns columns: log2FoldChange, stat, pvalue, padj — same schema as DESeq2.
"""
import subprocess
import tempfile
import os
n_test, n_ctrl = X_test.shape[0], X_ctrl.shape[0]
X_all = np.vstack([X_test, X_ctrl]).T.astype(np.float64) # genes × samples
with tempfile.TemporaryDirectory() as tmpdir:
expr_csv = os.path.join(tmpdir, "expr.csv")
result_csv = os.path.join(tmpdir, "result.csv")
# Write expression matrix: genes as rows, samples as columns
expr_df = pd.DataFrame(X_all, index=genes)
expr_df.to_csv(expr_csv, header=False)
r_script = f"""
suppressPackageStartupMessages({{
library(limma)
library(utils)
}})
expr <- as.matrix(read.csv("{expr_csv}", header=FALSE, row.names=1))
n_test <- {n_test}
n_ctrl <- {n_ctrl}
group <- factor(c(rep("test", n_test), rep("ctrl", n_ctrl)),
levels = c("ctrl", "test"))
design <- model.matrix(~ group)
fit <- lmFit(expr, design)
fit <- eBayes(fit)
result <- topTable(fit, coef = "grouptest",
number = nrow(expr),
sort.by = "none",
adjust.method = "BH")
write.csv(result, "{result_csv}", row.names = TRUE)
cat("OK\\n")
"""
proc = subprocess.run(
["Rscript", "--vanilla", "-"],
input=r_script,
capture_output=True,
text=True,
timeout=300,
)
if proc.returncode != 0:
raise RuntimeError(
f"Rscript/limma failed (exit {proc.returncode}):\n{proc.stderr.strip()}"
)
if not os.path.exists(result_csv):
raise RuntimeError(
f"Rscript ran but produced no output.\nstdout: {proc.stdout}\nstderr: {proc.stderr}"
)
top_df = pd.read_csv(result_csv, index_col=0)
top_df = top_df.rename(columns={
"logFC": "log2FoldChange",
"t": "stat",
"P.Value": "pvalue",
"adj.P.Val": "padj",
})
top_df.index = genes
return top_df[["log2FoldChange", "stat", "pvalue", "padj"]]
def run_limma_covariate(
X: "np.ndarray",
group_labels: "list[str]",
batch_labels: "list[str]",
genes: "list[str]",
test_group: str,
control_group: str,
) -> "pd.DataFrame":
"""
Limma with a batch covariate via Rscript — ``model.matrix(~ batch + group)``.
Mode-A early integration (ADR-0001 T8): when several cohorts are pooled into
one matrix, the per-cohort ``batch`` is modelled as a covariate so the group
effect is estimated *adjusting* for it. Group labels are recoded to ctrl/test
(control_group -> "ctrl", test_group -> "test") so the tested coefficient is
always "grouptest" regardless of the original label spelling; batch enters as
additional factor columns whose names do not matter (only the group
coefficient is read back).
Parameters
----------
X : samples x genes matrix (rows = samples, aligned with group/batch labels).
group_labels, batch_labels : per-sample labels, length == X.shape[0].
genes : gene ids, length == X.shape[1].
Returns columns: log2FoldChange, stat, pvalue, padj (same schema as run_limma).
Raises RuntimeError if Rscript/limma is unavailable or the design is rank-
deficient (e.g. batch perfectly confounded with group).
"""
import os
import subprocess
import tempfile
X = np.asarray(X, dtype=np.float64)
n = X.shape[0]
if not (len(group_labels) == len(batch_labels) == n):
raise ValueError(
f"group_labels ({len(group_labels)}) and batch_labels "
f"({len(batch_labels)}) must match X sample count ({n})"
)
coded = ["test" if str(g) == str(test_group) else "ctrl" for g in group_labels]
if len(set(coded)) < 2:
raise ValueError(
f"need both groups present; got only {set(group_labels)} for "
f"test='{test_group}' / control='{control_group}'"
)
expr = X.T # genes x samples
with tempfile.TemporaryDirectory() as tmpdir:
expr_csv = os.path.join(tmpdir, "expr.csv")
meta_csv = os.path.join(tmpdir, "meta.csv")
result_csv = os.path.join(tmpdir, "result.csv")
pd.DataFrame(expr, index=genes).to_csv(expr_csv, header=False)
pd.DataFrame(
{"group": coded, "batch": [str(b) for b in batch_labels]}
).to_csv(meta_csv, index=False)
r_script = f"""
suppressPackageStartupMessages({{
library(limma)
library(utils)
}})
expr <- as.matrix(read.csv("{expr_csv}", header=FALSE, row.names=1))
meta <- read.csv("{meta_csv}", colClasses = "character")
group <- factor(meta$group, levels = c("ctrl", "test"))
batch <- factor(meta$batch)
design <- model.matrix(~ batch + group)
if (qr(design)$rank < ncol(design)) {{
stop("design is rank-deficient (batch likely confounded with group)")
}}
fit <- lmFit(expr, design)
fit <- eBayes(fit)
result <- topTable(fit, coef = "grouptest",
number = nrow(expr),
sort.by = "none",
adjust.method = "BH")
write.csv(result, "{result_csv}", row.names = TRUE)
cat("OK\\n")
"""
proc = subprocess.run(
["Rscript", "--vanilla", "-"],
input=r_script,
capture_output=True,
text=True,
timeout=300,
)
if proc.returncode != 0:
raise RuntimeError(
f"Rscript/limma (covariate) failed (exit {proc.returncode}):\n"
f"{proc.stderr.strip()}"
)
if not os.path.exists(result_csv):
raise RuntimeError(
f"Rscript ran but produced no output.\nstdout: {proc.stdout}\n"
f"stderr: {proc.stderr}"
)
top_df = pd.read_csv(result_csv, index_col=0)
top_df = top_df.rename(columns={
"logFC": "log2FoldChange",
"t": "stat",
"P.Value": "pvalue",
"adj.P.Val": "padj",
})
top_df.index = genes
return top_df[["log2FoldChange", "stat", "pvalue", "padj"]]
|