/affinity-proteomics

name: affinity-proteomics
description: Unified analysis pipeline for affinity-based proteomics platforms — Olink (PEA, NPX) and SomaLogic SomaScan (SOMAmer,
  RFU). Platform-aware QC, normalisation, differential abundance, volcano plots, heatmaps, and PCA.
license: MIT
metadata:
  version: 0.1.0
  author: Reza
  tags:
  - proteomics
  - olink
  - somalogic
  - somascan
  - npx
  - affinity
  - differential-abundance
  - biomarker
  openclaw:
    requires:
      bins:
      - python3
    always: false
    emoji: 🧪
    homepage: https://github.com/ClawBio/ClawBio
    os:
    - darwin
    - linux
    install:
    - kind: pip
      package: somadata
    - kind: pip
      package: scipy
    - kind: pip
      package: statsmodels
    - kind: pip
      package: seaborn
    - kind: pip
      package: scikit-learn
    trigger_keywords:
    - Olink
    - SomaLogic
    - SomaScan
    - NPX
    - proteomics
    - affinity proteomics
    - protein biomarker
    - plasma proteomics
    - ADAT

🧪 Affinity Proteomics Pipeline

You are **Affinity Proteomics**, a specialised ClawBio agent for Olink and SomaLogic SomaScan data analysis. Your role is to run platform-aware QC, differential abundance testing, and visualisation from affinity-based proteomics data.

Why This Exists

• **Without it**: Researchers must write bespoke scripts for each platform — Olink NPX and SomaLogic ADAT have completely different file formats, normalisation methods, and QC conventions
• **With it**: A single command handles both platforms with correct QC, normalisation, and analysis under a unified interface
• **Why ClawBio**: The existing `proteomics-de` skill handles mass-spectrometry LFQ data (MaxQuant/DIA-NN) and does not cover affinity-based platforms. This skill fills that gap

Core Capabilities

1. **Dual-platform support**: Olink NPX (CSV/Parquet) and SomaLogic ADAT under one interface 2. **Platform-specific QC**: Olink (QC_Warning, LOD, sample median) / SomaLogic (RowCheck, ColCheck, normalisation scale factors, MAD outlier filtering) 3. **Differential abundance**: t-test or Mann-Whitney U with Benjamini-Hochberg FDR correction 4. **Visualisation**: Volcano plot, heatmap (top N proteins), PCA plot 5. **Structured reporting**: Markdown report, result.json, per-protein TSV, reproducibility bundle 6. **Skill Action Menu**: `result.json` includes a workflow state plus read-only follow-up actions for compact report cards

Input Formats

| Format | Extension | Platform | Example | |--------|-----------|----------|---------| | Olink NPX | `.csv` | Olink Explore / Target 96 | `olink_demo_npx.csv` | | SomaLogic ADAT | `.adat` | SomaScan v4.0/v4.1 | `example_data.adat` (via somadata) | | Sample metadata | `.csv` | Both (Olink requires separate file) | `olink_demo_meta.csv` |

CLI Reference

# Olink demo
python skills/affinity-proteomics/affinity_proteomics.py \
  --demo --platform olink --output /tmp/olink_demo

# SomaLogic demo
python skills/affinity-proteomics/affinity_proteomics.py \
  --demo --platform somascan --output /tmp/soma_demo

# Real Olink data
python skills/affinity-proteomics/affinity_proteomics.py \
  --platform olink --input data.csv --meta samples.csv \
  --group-col Group --contrast "Case,Control" --output results/

# Via ClawBio runner
python clawbio.py run affprot --demo --platform olink

Demo

python clawbio.py run affprot --demo --platform olink

Expected output: Differential abundance report for 80 samples (40 Case / 40 Control) across 40 proteins, with 5 truly differentially expressed proteins recovered, volcano plot, heatmap, PCA, and reproducibility bundle.

Output Structure

• `report.md` — markdown report with QC, differential abundance, and top-protein sections
• `result.json` — structured summary with `chat_summary_lines`, `preferred_artifacts`, `workflow_state`, and `suggested_actions`
• `tables/diff_abundance.tsv` — per-protein differential abundance table
• `figures/volcano.png`, `figures/heatmap.png`, `figures/pca.png` — standard demo figures
• `reproducibility/` — command and software-version metadata

Suggested Actions

The demo result emits `workflow_state.lifecycle: "ready"` and offers two read-only actions: `Top Proteins` and `Volcano Summary`. In chat, the user sees those labels as numbered options; selecting one runs the stored structured request.

`state_id` is derived as a SHA-256 hash over a compact deterministic state payload: platform, contrast, protein counts, significant-protein direction counts, and the top protein rows carried in each action request. If a stored request's `state_id` no longer matches that payload, the skill returns a structured `expired` result instead of rendering a stale follow-up.

{
  "workflow_state": {
    "state_schema": "affinity_proteomics.workflow_state.v1",
    "state_id": "sha256:...",
    "lifecycle": "ready",
    "state_label": "differential-abundance-ready",
    "description": "OLINK differential abundance results for Case vs Control are available."
  },
  "suggested_actions": [
    {
      "action_id": "show-top-proteins",
      "label": "Top Proteins",
      "estimate": "~5s",
      "request": {
        "schema": "affinity_proteomics.action_request.v1",
        "action": "top-proteins",
        "state_schema": "affinity_proteomics.workflow_state.v1",
        "state_id": "sha256:...",
        "n": 5,
        "platform": "olink",
        "contrast": ["Case", "Control"],
        "total_proteins_tested": 40,
        "significant_proteins": 5,
        "proteins": [
          {"protein_id": "OID00001", "gene": "GENE1", "log2fc": 0.0, "padj": "0.00e+00"}
        ]
      }
    }
  ]
}

Dependencies

**Required**:

• `somadata` >= 1.2 — SomaLogic ADAT parsing
• `scipy` >= 1.10 — statistical tests
• `statsmodels` >= 0.14 — multiple testing correction
• `matplotlib` >= 3.7 — plotting
• `seaborn` >= 0.13 — heatmaps
• `numpy` >= 1.24 — numerical operations
• `pandas` >= 2.0 — data manipula