Automate Olink and SomaScan Analysis with Affinity Proteomics

Where this fits

You’re a bioinformatician staring at a pile of Olink NPX files and SomaLogic ADAT exports. You need to compare protein biomarker levels across two patient cohorts, but each platform has its own quirks: different normalization methods, QC thresholds, and output formats. You’ve spent two days writing ad‑hoc Python scripts and still don’t trust the results. This is exactly where the affinity-proteomics skill steps in—it wraps the entire analysis pipeline into a single, platform‑aware agent that handles Olink and SomaScan data from raw files to publication‑ready figures.

Why agents benefit

• Platform‑aware QC: Automatically detects the input format (NPX, ADAT, or raw RFU) and applies the correct QC filters (e.g., Olink’s NPX deviation, SomaScan’s calibration scale).
• Unified normalization: Handles platform‑specific normalization (Olink’s NPX, SomaLogic’s adaptive normalization) without manual intervention, so you can compare results across platforms.
• Statistical rigor: Runs differential abundance analysis with appropriate models (e.g., t‑tests, ANOVA, or mixed‑effects) and corrects for multiple testing—no need to reimplement statsmodels calls.
• Visualization on demand: Generates volcano plots, heatmaps, and PCA plots directly from the analysis output, saving hours of matplotlib tweaking.
• Reproducible pipeline: Every step is logged and parameterized, so your agent can rerun the analysis with new data or different thresholds and produce consistent, shareable results.

Practical scenarios

Cross‑platform biomarker discovery

You have Olink NPX data from one cohort and SomaScan RFU data from a second cohort. The skill aligns both to a common protein identifier, performs per‑platform normalization, and then runs a combined differential abundance analysis—giving you a single list of candidate biomarkers without manual merging.

Longitudinal cohort QC

You’re monitoring protein levels over multiple time points. The skill flags samples that fail platform‑specific QC, visualizes batch effects with PCA, and lets you exclude outliers before running the final analysis. This catches issues early instead of after a full run.

Rapid hypothesis testing for a grant proposal

You need a quick preview of whether a specific protein panel differs between treatment groups. Instead of writing a script from scratch, you ask the agent to “run differential abundance on these two groups and show a volcano plot.” The skill delivers the plot and a stats table in minutes.

Add it to your agent workflow

Install the skill with a single command:

npx skills add ClawBio/ClawBio --skill affinity-proteomics

Once installed, your agent can invoke it with natural language. For example, prompt: “Run QC and differential abundance on the Olink NPX files in data/ and produce a heatmap of the top 20 proteins.” The skill will install the required Python packages (somadata, scipy, statsmodels, etc.) and execute the pipeline end‑to‑end.

Compare before adopting

Before integrating this skill, compare it against your current workflow. Look at the quality of the underlying somadata library and the maintainer’s responsiveness on GitHub. Check whether the skill’s normalization choices match your lab’s standards. Also consider if you need a different tool for single‑cell proteomics or targeted assays—this skill is specifically for Olink and SomaScan bulk data. Finally, evaluate how well the skill fits your agent’s existing toolchain: does it rely on Python 3 and pip? If your environment is containerized, you may need to adapt the installation step.

Why it is worth tracking

With 1,100+ stars on GitHub and an active author (Reza), the ClawBio ecosystem is gaining momentum among bioinformatics agent builders. The MIT license and clear documentation lower the barrier to adoption, and the skill’s focused scope means it’s likely to stay stable. If you regularly process Olink or SomaLogic data—or plan to build agents that handle proteomics—this is a skill worth evaluating now. For more details, see the skill page on OpenAgentSkill: https://www.openagentskill.com/skills/clawbio-affinity-proteomics. As the field moves toward automated, reproducible omics analysis, having a ready‑made pipeline like this can save your team days of development time.