Calibration plot for proteomics: A graphical tool to visually check the assumptions underlying FDR control in...

Area of Science:

• Proteomics
• Bioinformatics
• Statistical analysis

Background:

• False discovery rate (FDR) control is critical in mass spectrometry (MS)-based quantitative proteomics for identifying differentially abundant proteins.
• Classical FDR control relies on adjusted p-values, which are only accurate if p-values are well-calibrated.
• P-value calibration can be violated in practical MS proteomics analyses, potentially leading to underestimated FDR and unreliable results.

Purpose of the Study:

• To develop a straightforward graphical method for visually assessing p-value well-calibration in MS-based quantitative proteomics.
• To provide R code for integrating this visual assessment into existing proteomics analysis pipelines.
• To enhance the reliability of FDR control in proteomics studies.

Main Methods:

• Development of a novel graphical approach to visually evaluate p-value distribution and calibration.
• Implementation of the graphical method using R programming language for accessibility.
• Application of the method to assess p-value well-calibration in MS-based quantitative proteomics data.

Main Results:

• The proposed graphical method allows for intuitive and visual assessment of p-value well-calibration.
• The R code enables seamless integration of this visual check into standard proteomics analysis workflows.
• The method effectively identifies potential issues with p-value calibration that could impact FDR control.

Conclusions:

• The graphical method offers a robust solution for verifying p-value well-calibration in MS proteomics.
• Accurate p-value calibration is essential for reliable FDR control and valid identification of differentially abundant proteins.
• This approach improves the trustworthiness of quantitative proteomics findings by ensuring proper statistical rigor.