QCQuan

QCQuan assesses protein expression and data quality in label-based tandem mass spectrometry (MS) experiments to support proteomics and biomarker discovery.

Key Features:

• Automated quality assessment: Performs automated evaluation of protein expression and overall data quality in label-based tandem MS data sets.
• Artifact detection: Identifies effects arising from sample preparation and measurement processes that can compromise clinical sample analysis.
• Normalization: Integrates the CONSTANd normalization algorithm to adjust quantitative MS measurements.
• Exploratory metrics: Computes exploratory insights and robust quality control metrics for assessing experimental value.
• Differential expression: Conducts differential expression analysis using conservative statistical methods.
• Modeling approach: Avoids reliance on computationally intensive mixed models for rapid evaluation.
• Validation: Was evaluated on three reference data sets and reported quantitatively superior performance compared to a comparable workflow constructed with established software solutions.
• Data triage: Distinguishes high- and low-quality experimental data to prioritize samples for downstream quantitative analysis in high-throughput MS-based proteomics laboratories.

Scientific Applications:

• Proteomics quality control: Assessing and filtering label-based tandem MS data sets prior to downstream proteomics analyses.
• Biomarker discovery: Supporting biomarker discovery workflows by ensuring only high-quality quantitative MS data enter differential analyses.
• Clinical sample evaluation: Detecting sample-preparation and measurement artefacts that could bias clinical proteomics studies.

Methodology:

Integration of the CONSTANd normalization algorithm, computation of exploratory quality-control metrics, and conservative-statistics differential expression analysis while avoiding complex mixed models.