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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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An introduction to statistical process control in research proteomics.

David Bramwell¹

¹Biosignatures Ltd., Keel House, Newcastle Upon Tyne, UK.

Journal of Proteomics

|June 25, 2013

Summary

This summary is machine-generated.

Statistical process control (SPC) offers a robust framework for enhancing quality control in proteomics research. Implementing SPC rules can significantly improve data quality and workflow performance for researchers.

Keywords:

2-DE Control chart FN FP False negative False positive LC–MS QC Quality control Quantitative proteomics SPC Statistical process control TN TP True negative True positive VSN Variance Stabilisation Normalisation

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Area of Science:

Proteomics
Analytical Chemistry
Biotechnology

Background:

Statistical Process Control (SPC) is a proven quality management method widely used in various industries.
Its application in research proteomics is currently limited, despite its success in clinical diagnostic laboratories.
SPC provides a quantitative framework suitable for improving qualitative assays, making it ideal for biological research.

Purpose of the Study:

To introduce SPC as an objective quality control strategy for proteomics research.
To demonstrate the benefits of SPC for proteomics researchers and the quality of their generated results.

Main Methods:

Deriving and implementing basic quality control rules based on SPC principles.
Applying SPC to characterize measurement systems in proteomics workflows.
Defining control rules to monitor and improve process performance.

Main Results:

Basic SPC rules are straightforward to derive and implement.
These rules can substantially enhance data quality in proteomics studies.
The characterization of measurement systems and definition of control rules drive performance improvements.

Conclusions:

SPC is a powerful tool for optimizing proteomics research workflows.
Objective quality control is essential for proteomics discovery experiments.
This work bridges QC concepts from clinical chemistry to multi-analyte proteomics systems.