Application of clinical assay quality control (QC) to multivariate proteomics data: a workflow exemplified by 2-DE QC

Proteomics technologies can be effective for the discovery and assay of protein forms altered with disease. However, few examples of successful biomarker discovery yet exist. Critical to addressing this is the widespread implementation of appropriate QC (quality control) methodology. Such QC should combine the rigour of clinical laboratory assays with a suitable treatment of the complexity of the proteome by targeting separate assignable causes of variation. We demonstrate an approach, metric and example workflow for users to develop such targeted QC rules systematically and objectively, using a publicly available plasma DIGE data set. Hierarchical clustering analysis of standard channels is first used to discover correlated groups of features corresponding to specific assignable sources of technical variation. These effects are then quantified using a statistical distance metric, and followed on control charts. This allows measurement of process drift and the detection of runs that outlie for any given effect. A known technical issue on originally rejected gels was detected validating this approach, and relevant novel effects were also detected and classified effectively. Our approach was effective for 2-DE QC. Whilst we demonstrated this in a retrospective DIGE experiment, the principles would apply to ongoing QC and other proteomic technologies.

This work asserts that properly carried out QC is essential to proteomics discovery experiments. Its significance is that it provides one possible novel framework for applying such methods, with a particular consideration of how to handle the complexity of the proteome. It not only focusses on 2DE-based methodology but also demonstrates general principles. A combination of results and discussion based upon a publicly available data set is used to illustrate the approach and allows a structured discussion of factors that experimenters may wish to bear in mind in other situations. The demonstration is on retrospective data only for reasons of scope, but the principles applied are also important for ongoing QC, and this work serves as a step towards a later demonstration of that application. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.