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Data Quality Validation for Gaia Project.
Validates soil microbiome samples against the quality checklist
defined in docs/data_standard.md.
Quality Criteria:
- Total reads > 10,000
- Classified genera > 20
- Metadata completeness (biome + location required)
- Top-1 genus share < 90% (contamination check)
- Sequencing platform info present
"""
import argparse
import logging
from dataclasses import dataclass, field
from pathlib import Path
import pandas as pd
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
)
logger = logging.getLogger(__name__)
@dataclass
class QualityReport:
total_samples: int = 0
passed: int = 0
failed_reads: int = 0
failed_genera: int = 0
failed_metadata: int = 0
flagged_contamination: int = 0
flagged_no_platform: int = 0
removed_sample_ids: list = field(default_factory=list)
flagged_sample_ids: list = field(default_factory=list)
def validate_abundance(
abundance_df: pd.DataFrame,
metadata_df: pd.DataFrame | None = None,
min_total_reads: int = 10_000,
min_genera: int = 20,
max_top1_share: float = 0.90,
) -> tuple[pd.DataFrame, QualityReport]:
"""
Validate abundance data against quality criteria.
Returns:
Tuple of (filtered DataFrame, QualityReport)
"""
report = QualityReport(total_samples=len(abundance_df))
# Identify abundance columns (non-metadata)
id_cols = {"sample_id", "analysis_id"}
genus_cols = [c for c in abundance_df.columns if c not in id_cols]
abundance_values = abundance_df[genus_cols]
# Criterion 1: Total reads > min_total_reads
total_reads = abundance_values.sum(axis=1)
mask_reads = total_reads > min_total_reads
report.failed_reads = (~mask_reads).sum()
# Criterion 2: Classified genera > min_genera
nonzero_genera = (abundance_values > 0).sum(axis=1)
mask_genera = nonzero_genera > min_genera
report.failed_genera = (~mask_genera).sum()
# Criterion 4: Top-1 genus share < max_top1_share (contamination)
max_abundance = abundance_values.max(axis=1)
top1_share = max_abundance / total_reads.replace(0, 1)
mask_contamination = top1_share < max_top1_share
report.flagged_contamination = (~mask_contamination).sum()
# Combined filter: remove samples failing reads or genera
mask_keep = mask_reads & mask_genera
report.removed_sample_ids = abundance_df.loc[
~mask_keep, "sample_id"
].tolist()
# Flag (but don't remove) contamination suspects
report.flagged_sample_ids = abundance_df.loc[
~mask_contamination & mask_keep, "sample_id"
].tolist()
# Criterion 3 & 5: Metadata checks
if metadata_df is not None:
missing_biome = metadata_df["biome"].isna() | (
metadata_df["biome"] == ""
)
missing_location = metadata_df["latitude"].isna() | metadata_df[
"longitude"
].isna()
report.failed_metadata = (missing_biome | missing_location).sum()
if "sequencing_platform" in metadata_df.columns:
missing_platform = metadata_df["sequencing_platform"].isna()
report.flagged_no_platform = missing_platform.sum()
filtered_df = abundance_df[mask_keep].reset_index(drop=True)
report.passed = len(filtered_df)
return filtered_df, report
def print_report(report: QualityReport):
"""Print a formatted quality report."""
logger.info("=" * 60)
logger.info("DATA QUALITY REPORT")
logger.info("=" * 60)
logger.info(f"Total samples: {report.total_samples}")
logger.info(f"Passed: {report.passed}")
logger.info(f"Failed (low reads): {report.failed_reads}")
logger.info(f"Failed (few genera): {report.failed_genera}")
logger.info(f"Failed (metadata): {report.failed_metadata}")
logger.info(f"Flagged (contamination): {report.flagged_contamination}")
logger.info(f"Flagged (no platform): {report.flagged_no_platform}")
logger.info(
f"Pass rate: "
f"{report.passed / max(report.total_samples, 1) * 100:.1f}%"
)
logger.info("=" * 60)
def main():
parser = argparse.ArgumentParser(
description="Validate soil microbiome data quality"
)
parser.add_argument(
"abundance_file",
help="Path to abundance CSV file",
)
parser.add_argument(
"--metadata",
default=None,
help="Path to metadata CSV file",
)
parser.add_argument(
"--output",
default=None,
help="Path for filtered output CSV",
)
parser.add_argument("--min-reads", type=int, default=10_000)
parser.add_argument("--min-genera", type=int, default=20)
parser.add_argument("--max-top1-share", type=float, default=0.90)
args = parser.parse_args()
abundance_df = pd.read_csv(args.abundance_file)
metadata_df = None
if args.metadata:
metadata_df = pd.read_csv(args.metadata)
filtered_df, report = validate_abundance(
abundance_df,
metadata_df,
min_total_reads=args.min_reads,
min_genera=args.min_genera,
max_top1_share=args.max_top1_share,
)
print_report(report)
if args.output:
filtered_df.to_csv(args.output, index=False)
logger.info(f"Saved filtered data to {args.output}")
if __name__ == "__main__":
main()
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