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

Abstract

Quantitative proteomics is entering its "third generation," where intricate experimental designs aim to increase the spatial and temporal resolution of protein changes. This paper re-analyses multiple internally consistent proteomic datasets generated from whole cell homogenates and fractionated brain tissue samples providing a unique opportunity to explore the different factors influencing experimental outcomes. The results clearly indicate that improvements in data handling are required to compensate for the increased mean CV associated with complex study design and intricate upstream tissue processing. Furthermore, applying arbitrary inclusion thresholds such as fold change in protein abundance between groups can lead to unnecessary exclusion of important and biologically relevant data.

Keywords: Bioinformatics; Differential protein expression; LC-MS/MS; Protein marker discovery; Proteome analysis.