proteomics-ms-qc

Name: proteomics-ms-qc
Availability: InStock
Author: TianGzlab

Automate quality control on mass spectrometry proteomics data from CSV input.

155|26|Updated Mar 14, 2026

Search Tags:#quality control #data analysis #mass spectrometry #proteomics #PTXQC #MS QC

Ready to run

npx skills add https://github.com/TianGzlab/OmicsClaw --skill proteomics-ms-qc

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: proteomics-ms-qc
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-ms-qc
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill proteomics-ms-qc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the critical need for early detection of issues in mass spectrometry data, preventing costly downstream analysis on compromised samples and ensuring the reliability of proteomics experiments.

Core Features & Use Cases

Automated QC Metrics: Computes essential quality control statistics like missing value rates, coefficient of variation (CV), and intensity distributions.
Sample Completeness Analysis: Assesses the proportion of detected proteins/peptides for each sample.
Use Case: Before performing differential expression analysis, run this Skill on your raw proteomics data to identify samples with high missingness or poor CVs, allowing you to decide whether to exclude them from further analysis.

Quick Start

Run mass spectrometry quality control on the provided data file 'my_proteomics_data.csv'.

Frequently Asked Questions about proteomics-ms-qc

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

FAQPage Schema

How do I perform mass spectrometry quality control on raw proteomics data?▼

Perform mass spectrometry quality control by processing your CSV data file to compute missing value rates, coefficient of variation, and intensity distributions. This automated analysis identifies potential data quality issues and outputs a comprehensive QC report with JSON results and CSV metrics.

What metrics are used to assess proteomics data integrity and identify poor samples?▼

Proteomics data integrity is assessed using missing value rates, coefficient of variation (CV), intensity distributions, and sample correlations. These metrics help identify samples with high missingness or poor CVs that should be excluded from downstream analysis.

Can I use PTXQC and rawTools to automate MS QC for proteomics experiments?▼

Yes, automated MS QC for proteomics experiments is performed using tools like PTXQC, rawTools, or MSstatsQC. The process analyzes sample completeness, evaluates the proportion of detected proteins and peptides, and generates a comprehensive QC report to ensure reliability.

When should I run a QC report before differential expression analysis?▼

Run a QC report before differential expression analysis to detect mass spectrometry data issues early. This prevents costly downstream analysis on compromised samples by evaluating missing value rates and sample correlations to decide which samples to exclude.

Does proteomics MS QC require a specific input data format?▼

Proteomics MS QC requires input data in CSV format. Processing the CSV file computes quality control statistics including coefficient of variation and intensity distributions, generating outputs in JSON results, CSV metrics, and a comprehensive QC report.