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MubasherMohammed
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affinity-proteomics

Automate affinity proteomics analysis with platform-aware QC and differential testing.

Updated May 10, 2026
Search Tags:#data visualization#bioinformatics#proteomics#differential abundance#affinity proteomics
One-click install
npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill affinity-proteomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: affinity-proteomics
Source: https://github.com/MubasherMohammed/opencode-BioInfo/tree/main/python/skills/affinity-proteomics
Command: npx skills add https://github.com/MubasherMohammed/opencode-BioInfo --skill affinity-proteomics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires somadata, scipy, statsmodels, seaborn, scikit-learn, numpy, pandas, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies the analysis of affinity-based proteomics data, providing platform-aware quality control, normalization, differential abundance testing, and visualization.

Core Features & Use Cases

  • Platform-Aware Analysis: Supports Olink NPX and SomaLogic ADAT platforms with specific QC and normalization methods.
  • Differential Abundance Testing: Perform t-test or Mann-Whitney U tests with FDR correction.
  • Visualization: Generate volcano plots, heatmaps, and PCA plots for data visualization.
  • Use Case: Imagine you have affinity proteomics data from Olink and SomaLogic platforms. Use this Skill to analyze the data, identify differentially abundant proteins, and visualize the results.

Quick Start

Run the affinity-proteomics skill on your data with the following command:

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

Frequently Asked Questions about affinity-proteomics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform differential abundance testing on Olink proteomics data?▼

Differential abundance testing on Olink proteomics data is automated using t-test or Mann-Whitney U tests with FDR correction. The tool manages platform-aware normalization automatically to prepare data for statistical analysis and output results.

Can I use this to generate volcano plots and heatmaps from SomaLogic ADAT files?▼

Yes, you can generate volcano plots, heatmaps, and PCA plots from SomaLogic ADAT files. The script provides platform-aware quality control and visualization directly from affinity proteomics data inputs.

What's the best way to analyze SomaLogic and Olink data in Python?▼

Analyzing SomaLogic and Olink data in Python is best handled by platform-aware scripts using scipy, statsmodels, and pandas. This standardizes quality control, normalization, and visualization for both ADAT and NPX formats.

Does this tool support quality control and normalization for affinity proteomics data?▼

Yes, it supports platform-aware quality control and normalization for affinity proteomics data. It specifically processes Olink NPX and SomaLogic ADAT formats to ensure accurate statistical testing and visualization.

Do I need somadata to run affinity proteomics analysis?▼

Yes, somadata is required to run affinity proteomics analysis, along with scipy, statsmodels, seaborn, scikit-learn, numpy, pandas, and matplotlib for data processing and visualization.

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