Total variance should drive data handling strategies in third generation proteomic studies

Area of Science:

• Proteomics
• Biochemistry
• Systems Biology

Background:

• Quantitative proteomics is advancing with complex experimental designs for higher resolution.
• Challenges arise in analyzing data from intricate study designs and tissue processing.

Purpose of the Study:

• Re-analyze proteomic datasets to identify factors influencing experimental outcomes.
• Assess the impact of data handling and inclusion thresholds on results.

Main Methods:

• Re-analysis of multiple internally consistent proteomic datasets.
• Utilized data from whole cell homogenates and fractionated brain tissue samples.

Main Results:

• Increased mean coefficient of variation (CV) observed with complex designs and upstream processing.
• Arbitrary inclusion thresholds (e.g., fold change) can exclude relevant biological data.

Conclusions:

• Improved data handling strategies are essential for complex quantitative proteomics studies.
• Rethinking arbitrary inclusion thresholds is necessary to retain biologically significant findings.