An introduction to statistical process control in research proteomics.

BACKGROUND Statistical process control is a well-established and respected method which provides a general purpose, and consistent framework for monitoring and improving the quality of a process. It is routinely used in many industries where the quality of final products is critical and is often required in clinical diagnostic laboratories [1,2]. To date, the methodology has been little utilised in research proteomics. It has been shown to be capable of delivering quantitative QC procedures for qualitative clinical assays [3] making it an ideal methodology to apply to this area of biological research. OBJECTIVE To introduce statistical process control as an objective strategy for quality control and show how it could be used to benefit proteomics researchers and enhance the quality of the results they generate. RESULTS We demonstrate that rules which provide basic quality control are easy to derive and implement and could have a major impact on data quality for many studies. CONCLUSIONS Statistical process control is a powerful tool for investigating and improving proteomics research work-flows. The process of characterising measurement systems and defining control rules forces the exploration of key questions that can lead to significant improvements in performance. BIOLOGICAL SIGNIFICANCE This work asserts that QC is essential to proteomics discovery experiments. Every experimenter must know the current capabilities of their measurement system and have an objective means for tracking and ensuring that performance. Proteomic analysis work-flows are complicated and multi-variate. QC is critical for clinical chemistry measurements and huge strides have been made in ensuring the quality and validity of results in clinical biochemistry labs. This work introduces some of these QC concepts and works to bridge their use from single analyte QC to applications in multi-analyte systems. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.

[1]  Allison Fs.  An historical review of quality control in hematology. , 1983 .

[2]  W. A. Shewhart,et al.  Statistical method from the viewpoint of quality control , 1939 .

[3]  Sten A Westgard,et al.  The quality of laboratory testing today: an assessment of sigma metrics for analytic quality using performance data from proficiency testing surveys and the CLIA criteria for acceptable performance. , 2006, American journal of clinical pathology.

[4]  J O Westgard,et al.  Quality control for qualitative assays: quantitative QC procedure designed to assure analytical quality required for an ELISA of hepatitis B surface antigen. , 1997, Clinical chemistry.

[5]  E R JENNINGS,et al.  The use of control charts in the clinical laboratory. , 1950, American journal of clinical pathology.

[6]  Sanford Weisberg,et al.  An R Companion to Applied Regression , 2010 .

[7]  Lorenzo J. Vega-Montoto,et al.  QuaMeter: multivendor performance metrics for LC-MS/MS proteomics instrumentation. , 2012, Analytical chemistry.

[8]  Sunil J Rao,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2003 .

[9]  David Bramwell,et al.  Application of clinical assay quality control (QC) to multivariate proteomics data: a workflow exemplified by 2-DE QC. , 2013, Journal of proteomics.

[10]  Martin Dumbach,et al.  Overview of studies , 2021, Life in Lockdown.

[11]  David L. Tabb,et al.  The 2012 PRG Study: Assessing Longitudinal Variability in Routine Peptide LC-MS/MS Analysis , 2012 .

[12]  Martin Vingron,et al.  Variance stabilization applied to microarray data calibration and to the quantification of differential expression , 2002, ISMB.

[13]  D. B. Tonks,et al.  A study of the accuracy and precision of clinical chemistry determinations in 170 Canadian laboratories. , 1963, Clinical chemistry.

[14]  Robert Tonge,et al.  Considerations for powering a clinical proteomics study: Normal variability in the human plasma proteome , 2009, Proteomics. Clinical applications.

[15]  Stephen O. David,et al.  A novel experimental design for comparative two‐dimensional gel analysis: Two‐dimensional difference gel electrophoresis incorporating a pooled internal standard , 2003, Proteomics.

[16]  A. R. Crathorne,et al.  Economic Control of Quality of Manufactured Product. , 1933 .

[17]  J O Westgard,et al.  A multi-rule Shewhart chart for quality control in clinical chemistry. , 1981, Clinical chemistry.