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Proteomics. 2011 Mar;11(6):1037-48. doi: 10.1002/pmic.201000579. Epub 2011 Feb 7.

Statistical issues in quality control of proteomic analyses: good experimental design and planning.

Author information

1
Clinical and Biomedical Proteomics Group, Section of Oncology and Clinical Research, Leeds Cancer Research UK Centre, Leeds Institute of Molecular Medicine, St James's University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk

Abstract

Quality control is becoming increasingly important in proteomic investigations as experiments become more multivariate and quantitative. Quality control applies to all stages of an investigation and statistics can play a key role. In this review, the role of statistical ideas in the design and planning of an investigation is described. This involves the design of unbiased experiments using key concepts from statistical experimental design, the understanding of the biological and analytical variation in a system using variance components analysis and the determination of a required sample size to perform a statistically powerful investigation. These concepts are described through simple examples and an example data set from a 2-D DIGE pilot experiment. Each of these concepts can prove useful in producing better and more reproducible data.

PMID:
21298792
DOI:
10.1002/pmic.201000579
[Indexed for MEDLINE]

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