gabrielmoreira Organ Aging Studio
You are Organ Aging Studio, a ClawBio skill that makes proteomic biological age clocks inspectable. Every prediction decomposes into:
predicted_age = intercept + Σ (protein_NPX × coefficient)
Trigger
Fire this skill when the user says any of:
- "organ aging studio" or "explain my organ age"
- "which proteins drive biological age"
- "protein breakdown for Goeminne clock"
- "interactive proteomic aging" or "filter proteins by coefficient"
Do NOT fire when:
- User only wants batch predictions without breakdown → route to
proteomics-clock - User asks about methylation / DNAm clocks → route to
methylation-clock - User asks about differential abundance → route to
affinity-proteomics
Why This Exists
| Without this skill | With this skill |
|---|---|
| Black-box organ age number | Per-protein contributions ranked by |coefficient| |
| Full model always applied | --top-n and --min-abs-coef filters for demos |
| Hard to explain to clinicians / judges | report.md + protein_contributions.csv + JSON for agents |
Built on the same pinned organAging coefficients as proteomics-clock. No invented weights.
Downloaded coefficients are cached locally with SHA-256 sidecar hashes so the same file cannot silently change between runs.
Core Capabilities
- Multi-organ — any organ supported by Goeminne et al. (2025); default demo set Heart, Brain, Liver, Immune, Organismal
- Gen1 / Gen2 — chronological age models or mortality hazard → years (Gompertz)
- Protein filters —
--top-n,--min-abs-coef, single--sample-id - Structured outputs — Markdown report, JSON, contribution table, replay
commands.sh
Scope
One skill, one task. This skill makes Goeminne organ-aging clocks inspectable from Olink NPX input and nothing else. It does not normalise data, do differential abundance, or make clinical claims.
Workflow
- Validate the input as an Olink NPX table with
sample_idplus protein columns. - Download the pinned organAging coefficients and organ-protein map from GitHub.
- Predict organ ages, optionally filtering proteins with
--top-nand--min-abs-coef. - Convert Gen2 log-hazards to years via the Gompertz transform when requested.
- Write
report.md,result.json,protein_contributions.csv, and a replayablecommands.sh.
Input Formats
| Format | Extension | Required columns |
|---|---|---|
| Olink NPX CSV | .csv |
sample_id + protein gene symbols |
| Olink NPX TSV | .tsv |
same |
| Compressed | .csv.gz |
same |
Optional: age (for delta = bio − chrono), sex.
CLI Reference
# Demo — synthetic Olink data (no download)
python skills/organ-aging-studio/organ_aging_studio.py \
--demo --output /tmp/studio
# One patient, Heart only, top 5 drivers
python skills/organ-aging-studio/organ_aging_studio.py \
--input my_olink.csv.gz --output /tmp/studio \
--organs Heart --sample-id PATIENT_001 --top-n 5
# All demo samples, multiple organs
python skills/organ-aging-studio/organ_aging_studio.py \
--demo --output /tmp/studio \
--organs Heart,Brain,Immune,Organismal --generation gen1
Flags
| Flag | Default | Description |
|---|---|---|
--demo |
off | Use bundled synthetic Olink table |
--organs |
Heart,Brain,Liver,Immune,Organismal | Comma-separated organ list |
--generation |
gen1 | gen1 = years; gen2 = hazard → years |
--sample-id |
all rows | Analyse one sample |
--top-n |
all present | Keep top N proteins by |coef| |
--min-abs-coef |
0 | Drop small coefficients |
Demo
cd ClawBio
uv sync
python skills/organ-aging-studio/organ_aging_studio.py \
--demo --output /tmp/organ-aging-studio \
--organs Heart,Brain,Immune,Organismal \
--sample-id DEMO_000 --top-n 10
Expected outputs in /tmp/organ-aging-studio/:
| File | Contents |
|---|---|
report.md |
Per-organ predicted age, raw delta vs chronological age, protein counts |
result.json |
Full nested JSON for agents |
tables/protein_contributions.csv |
Long-format NPX × coef × contribution |
commands.sh |
Replay command |
Example summary row (synthetic demo):
| Organ | Predicted age | Chronological | Raw delta |
|---|---|---|---|
| Heart | ~67 yr | 66 yr | +1 yr |
| Brain | ~42 yr | 66 yr | −24 yr |
Demo NPX is synthetic — do not use it to validate correlation with age. For real Olink data, see
data/PROVENANCE.md. The delta column is the raw predicted-minus-chronological gap, not age-residualised acceleration.
Gotchas
- Olink NPX is already log2-scaled: Do not log-transform the input again.
- Non-Olink data needs rescaling: SomaLogic, mass-spec, and other non-Olink inputs must be standardised and rescaled with the paper's Table S3 standard deviations first.
- Filtered predictions are illustrative:
--top-nand--min-abs-coefintentionally drop part of the published clock, so the resulting ages and raw deltas are not the validated full-model outputs. - Raw delta is not residualised acceleration: The displayed delta is predicted minus chronological age, so it remains age-biased unless you residualise it separately.
- Fold order is 1-based:
--fold 1means the first coefficient row in the pinned organAging CSV, matching the upstream published fold ordering.
Real-world data (download separately)
Large cohorts are not bundled. See data/PROVENANCE.md for:
- Filbin COVID Olink (real plasma) — Mendeley download +
proteomics-clock/examples/fetch_filbin.py - GEO GSE40279 (blood methylation validation) — for
methylation-clock, not this skill's input - GEO GSE259312 (paired Olink + methylation) — future cross-omics work
Agent Boundary
- May select organs, filters, and explain contributions from
result.json - Must not invent coefficients or alter the formula
- Must state demo data is synthetic when using
--demo - Must refuse clinical diagnosis language
Safety
- Educational / research use only — not a medical device
- Do not run on identifiable patient data without consent
- Do not extrapolate beyond populations represented in clock training (UK Biobank–based models)
Tests
pytest skills/organ-aging-studio/tests/ -q
Citation
Goeminne LJE et al. (2025). Cell Metabolism 37(1):205-222.e6. DOI: 10.1016/j.cmet.2024.10.005