QCMAP

QCMAP diagnoses and predicts LC-MS system performance in proteomics by training predictive models on QC metrics to support reliable protein identification and quantification.

Key Features:

• Diagnosis using standardized QC samples: Uses standardized HeLa cell QC samples to establish baselines and diagnose LC-MS system performance.
• Predictive modeling of performance factors: Trains predictive models on performance factors such as mass error and retention time to classify satisfactory versus unsatisfactory runs.
• Generalization across sample types: Validates models on independent biological samples, exemplified by brain samples, to assess transferability beyond training data.
• Benchmarking and optimization: Benchmarks biological sample runs to identify key factors influencing instrument performance and guide optimization efforts.

Scientific Applications:

• Proteomics quality control: Assess and monitor LC-MS system performance in proteomics experiments to ensure data quality.
• Protein identification and quantification: Support more reliable protein identification and quantification by detecting instrument performance issues that affect measurements.
• Biomarker discovery robustness: Improve confidence in biomarker discovery by identifying system-level artifacts that could confound downstream analyses.

Methodology:

Collect standardized QC samples (HeLa) and train predictive models on key performance factors (e.g., mass error, retention time); validate models on independent biological sample types such as brain samples and use model outputs to identify conditions associated with satisfactory or unsatisfactory LC-MS performance.