proteomics-analysis

Analyze mass spectrometry proteomics data to identify differentially expressed proteins and generate publication-ready plots.

64|12|Updated Apr 7, 2026

Search Tags:#normalization #mass-spectrometry #proteomics #differential-expression #heatmap #qc #volcano-plot

Ready to run

npx skills add https://github.com/MDhewei/bioinfor-claw --skill proteomics-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: proteomics-analysis
Source: https://github.com/MDhewei/bioinfor-claw/tree/main/multiomics-data-analysis/proteomics-analysis
Command: npx skills add https://github.com/MDhewei/bioinfor-claw --skill proteomics-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, scipy, and includes scripts (resource) components.

What problem does it solve?

Analyze mass spectrometry proteomics data to QC, normalize, impute missing values, and identify differentially expressed proteins, producing publication-ready visualizations.

Core Features & Use Cases

QC metrics per sample (missing value patterns, CV, correlations) to assess data quality and reliability.
Normalization and imputation workflows (median, quantile, log2-median, VSN-like) to produce comparable protein abundances.
Differential expression analysis using Welch's t-test with FDR correction, plus visualization (volcano plots, heatmaps) for group comparisons.
Use Case: Compare treated vs control proteomics samples to discover upregulated proteins and generate publication-ready figures.

Quick Start

Provide a protein intensity matrix and corresponding metadata, then run the tool to obtain DE results, QC metrics, and visualizations.

Frequently Asked Questions about proteomics-analysis

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

FAQPage Schema

How do I identify differentially expressed proteins in mass spectrometry proteomics data?▼

To identify differentially expressed proteins in mass spectrometry proteomics data, you provide a protein intensity matrix and sample metadata. The tool applies normalization, imputation, and Welch's t-test with FDR correction to output DE results.

Can I use this proteomics analysis workflow for both TMT and LFQ datasets?▼

Yes, this proteomics analysis workflow supports TMT, LFQ, and DIA datasets. It processes your protein intensity matrices to perform batch correction and generate QC metrics for treated vs control experimental designs.

How do I generate volcano plots and heatmaps for upregulated proteins?▼

To generate volcano plots and heatmaps for upregulated proteins, run the differential expression analysis on your normalized data. The tool outputs publication-ready visualizations directly from the computed DE results.

What normalization methods are available for correcting missing values in DIA proteomics data?▼

Available normalization methods for DIA proteomics data include median, quantile, log2-median, and VSN-like workflows. The tool also handles missing value imputation to ensure comparable protein abundances across samples.

Does this tool provide QC metrics for assessing mass spectrometry sample quality?▼

Yes, the tool provides QC metrics per sample including missing value patterns, coefficient of variation (CV), and correlations. These metrics assess data quality and reliability before running differential expression analysis.