MSstatsQC

MSstatsQC provides longitudinal statistical process-control methods to monitor system suitability and quality control of proteomic Selected Reaction Monitoring (SRM) experiments for reliable peptide quantification.

Key Features:

• System Suitability Tests (SST) and Quality Control (QC): Implements SST and ongoing QC workflows to verify instrument performance and sample profile integrity.
• Longitudinal statistical process control: Focuses on analysis of longitudinal data typical of SST and QC applications in proteomics.
• Simultaneous control charts: Incorporates simultaneous control chart methods for monitoring multiple metrics over time.
• Time-weighted control charts: Implements time-weighted control charts to emphasize recent observations in longitudinal monitoring.
• Change point analysis: Provides change point analysis to detect abrupt shifts in instrument or assay behavior.
• Tailored for SRM experiments: Methods and metrics are adapted specifically for Selected Reaction Monitoring (SRM) targeted proteomics.
• Validation on real and simulated data: Methods have been evaluated on simulated datasets and data from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium.
• Detection of chromatographic and instrumental issues: Enables early identification of chromatographic drift and instrumental performance problems affecting peptide quantification.
• Support for reproducibility assessment: Facilitates assessment of reproducibility across laboratories and experimental runs.

Scientific Applications:

• Mass spectrometer performance verification: Use SST to verify that mass spectrometers meet specified performance standards.
• Ongoing sample and assay QC: Monitor sample profile integrity and assay consistency across longitudinal experiments.
• Targeted proteomics and peptide quantification: Apply methods to SRM-targeted detection and quantification of peptides in complex matrices.
• Quantitative proteomic studies across labs and time: Support inter-laboratory reproducibility studies and longitudinal monitoring of quantitative proteomic workflows.
• Early failure detection: Detect chromatographic or instrumental deviations early to prevent compromised data quality.

Methodology:

Implements longitudinal statistical process-control methods explicitly including simultaneous and time-weighted control charts and change point analysis, with evaluations performed on simulated datasets and Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium data.