fmschulz

bio-stats-ml-reporting

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

fmschulz 3 1 Updated 2mo ago
GitHub

Install

npx skillscat add fmschulz/omics-skills/bio-stats-ml-reporting

Install via the SkillsCat registry.

SKILL.md

Bio Stats ML Reporting

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

Instructions

  1. Join outputs in DuckDB and build feature tables.
  2. Train baseline models and evaluate with cross-validation.
  3. Generate reports and validate references.

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.
References - See ../bio-skills-references.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/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.

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.