Bio research tools biomarker signature studio

Skill FridrichMethod/awesome-skills/skills/bio-research-tools-biomarker-signature-studio

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Install

npx -y skills add FridrichMethod/awesome-skills --skill bio-research-tools-biomarker-signature-studio

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Multi-omic biomarker discovery studio that ingests expression + metadata, performs QC, multi-strategy feature selection, nested CV model training, survival analysis hooks, and SHAP-based interpretation. Use to design translational biomarker panels with documented evidence.

SKILL.md

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Biomarker Signature Studio

Design validated biomarker panels that are explainable, stable, and ready for translational follow-up. This skill stitches together the existing biomarker pipeline tooling, adds configurable feature-selection ensembles, a small survival-analysis hook, and artifact export so downstream lab teams can review QC outputs.

What This Skill Does

QC + Harmonization: Align expression matrices (samples x features) with metadata, check label balance, and compute summary stats.
Feature Selection Ensemble: Supports Boruta, elastic-net stability, mutual-information top-K, and mRMR with optional intersection voting.
Model Factory: Trains multiple estimators (Logistic L1, RandomForest, XGBoost if present) under nested CV, picks champion by AUC.
Explainability + Export: Produces SHAP tables/plots when packages are available, exports feature rankings and model weights.
Survival Hook: If metadata contains time_to_event and event the skill computes concordance for selected features via Cox model.

All logic lives in scripts/biomarker_signature_studio.py.

Inputs

Expression matrix (--expression): CSV/TSV genes x samples or samples x genes (auto-detected by metadata match).
Metadata (--metadata): Must contain --label-column. Optional --id-column (default sample_id), time_to_event, event.
Optional gene list for filtering (--feature-list).
Output directory (--output-dir), created if missing.

Quick CLI Usage

python Skills/Research_Tools/Biomarker_Signature_Studio/scripts/biomarker_signature_studio.py \
  --expression data/expression.csv \
  --metadata data/metadata.csv \
  --label-column phenotype \
  --selectors boruta,lasso,mrmr \
  --models rf,logit \
  --output-dir outputs/biomarkers_run1

Key flags:

Flag	Description
`--selectors`	Comma list of selection strategies (`boruta`, `lasso`, `mrmr`, `mi_topk`).
`--models`	Models to evaluate (`logit`, `rf`, `xgb`).
`--k-features`	Target number of features for `mrmr`/`mi_topk`.
`--survival`	Enable Cox evaluation when survival columns exist.
`--random-state`	Reproducibility.
`--nested-folds`	Outer CV folds (default 5).

Workflow

Load + align inputs, infer orientation, impute missing values.
Standardize features (fit on train set only).
Run requested selectors; create intersection + union candidate lists.
For each selector output run nested CV training across requested models.
Export champion metrics (metrics.json), feature table (selected_features.csv), SHAP summary (shap_summary.csv when available), and survival stats (survival.json).

QC Expectations

Class count ratio ≤3:1; warnings logged otherwise.
Selected features between 5 and 250 unless user overrides.
Nested CV AUC ≥0.70 or flagged in report.
SHAP overlap with selected features ≥60% (reported).

Related Assets

examples/configs/biomarker_studio_template.yaml (scaffold for teams)
scripts/biomarker_signature_studio.py (entry point)
Existing biomarker workflow skill for orchestrated runs.

Use this skill whenever you need a ready-to-review biomarker dossier (data QC, model metrics, explainability artifacts) before moving to validation cohorts or lab assays.