Bio stats ml reporting

Bio Stats ML Reporting

Aggregate results, train ML models, and produce reports with validated references.

Instructions

1. Join outputs in DuckDB v1.1+ and build feature tables. Arrow / DuckLake integration is the recommended bridge into ML pipelines for large datasets.
2. Train baseline models and evaluate with cross-validation.
   • CPU baseline: scikit-learn v1.5+ for linear/tree/clustering baselines; XGBoost v2.1.4+ for gradient boosting.
   • GPU node available (CUDA): set device="cuda" on XGBoost (native since v2.0) by default. For sklearn-compatible estimators (random forest, k-means, PCA, UMAP), use RAPIDS cuML as a drop-in replacement and record the device in the run log.
3. Generate reports and validate references.
4. For exploratory omics projects, aggregate discovery evidence across the literature-derived analysis playbook, annotation, phylogenomics, viromics, and comparative-genomics outputs.
5. Comparative-axes rollup — join the per-axis comparison artifacts produced by upstream skills into a single comparative_axes_summary.tsv. The rollup must have one row per (query genome, axis) and include:
   • genome-property frontier (size, gene count, etc. — link to relative_genome_metrics.tsv and genome_size_frontier.tsv)
   • marker-gene census (link to marker_census.tsv)
   • family copy-number expansions/contractions (link to family_copy_number_comparison.tsv and family_expansion_candidates.tsv)
   • synteny / conserved neighborhoods (link to conserved_neighborhoods.tsv)
   • non-coding RNA census (link to ncRNA_census.tsv) Each row records observation, comparison baseline, literature reference, status (notable / conserved / artifact / negative), and a follow-up test.
6. Produce an interesting-findings section that ranks candidate discoveries relative to the literature-derived baseline and separates:
   • strong candidates with multiple evidence types
   • plausible candidates needing validation
   • likely artifacts or conserved lineage features
   • explicit negative findings where nothing notable was detected
7. Include the comparison baseline, literature context, confidence, and next discriminating analyses for each candidate.

Quick Reference

Task	Action
Run workflow	Follow the steps in this skill and capture outputs.
Validate inputs	Confirm required inputs and reference data exist.
Review outputs	Inspect reports and QC gates before proceeding.
Tool docs	See docs/README.md.

Input Requirements

Prerequisites:

• Tools available in the active environment (Pixi/conda/system). See docs/README.md for expected tools.
• Results tables and metadata are available. Inputs:
• results/.parquet or results/.tsv
• metadata.tsv

Output

• results/bio-stats-ml-reporting/models/
• results/bio-stats-ml-reporting/metrics.tsv
• results/bio-stats-ml-reporting/comparative_axes_summary.tsv
• results/bio-stats-ml-reporting/discovery_summary.tsv
• results/bio-stats-ml-reporting/report.md
• results/bio-stats-ml-reporting/logs/

Quality Gates

• Model performance sanity checks pass.
• Reference validation passes.
• On failure: retry with alternative parameters; if still failing, record in report and exit non-zero.
• Verify input tables are readable and schema-consistent.
• Discovery summary joins candidate genes/features to annotation evidence, comparison baseline, literature context, and confidence.
• comparative_axes_summary.tsv covers all five mandatory axes (genome-property frontier, marker-gene census, family copy-number, synteny/neighborhoods, ncRNA census) for every query genome, with rows for axes that produced negative findings.
• Final report states what is interesting, what is conserved/expected, what is likely artifact, and what should be tested next.

Examples

Example 1: Expected input layout

results/*.parquet or results/*.tsv
metadata.tsv

Troubleshooting

Issue: Missing inputs or reference databases Solution: Verify paths and permissions before running the workflow.

Issue: Low-quality results or failed QC gates Solution: Review reports, adjust parameters, and re-run the affected step.