Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics

Area of Science:

• Proteomics
• Analytical Chemistry
• Biotechnology

Background:

• Coefficient of Variation (CV) is widely used in mass spectrometry-based proteomics to assess data dispersion.
• Increasing technical advancements necessitate standardized methods for calculating and reporting CVs to measure quantitative precision.
• Lack of standardized guidelines can lead to variability in results and hinder method comparison.

Purpose of the Study:

• To highlight the impact of CV calculation methods, data normalization, and software parameters on data dispersion.
• To propose recommendations for calculating and reporting CVs in technical studies focused on precision.
• To introduce a new R package, proteomicsCV, for calculating CVs in proteomics data.

Main Methods:

• Review of existing CV calculation methodologies in proteomics.
• Analysis of the influence of different normalization techniques and software parameters on CV values.
• Development and validation of the proteomicsCV R package.

Main Results:

• Demonstrated significant effects of CV equation choices, normalization strategies, and software parameters on measured data dispersion.
• Established a set of best-practice recommendations for reporting CVs in technical proteomics studies.
• The proteomicsCV R package provides a standardized tool for CV calculation.

Conclusions:

• Standardized calculation and reporting of CVs are essential for reliable benchmarking of proteomics methods.
• Adherence to proposed guidelines and use of tools like proteomicsCV will improve reproducibility and comparability in the field.
• Consistent reporting practices are vital for advancing quantitative precision in mass spectrometry proteomics.