Dakna, M; Harris, K; Kalousis, A; Carpentier, S; Kolch, W; Schanstra, JP; ... Girolami, M; + view all Dakna, M; Harris, K; Kalousis, A; Carpentier, S; Kolch, W; Schanstra, JP; Haubitz, M; Vlahou, A; Mischak, H; Girolami, M; - view fewer (2010) Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers. BMC BIOINFORMATICS , 11 , Article 594. 10.1186/1471-2105-11-594.
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Background: The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School.Results: We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test set is essential.Conclusions: Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.
|Title:||Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers|
|Open access status:||An open access publication. A version is also available from UCL Discovery.|
|Additional information:||© 2010 Dakna et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.|
|Keywords:||CHRONIC KIDNEY-DISEASE, DNA MICROARRAY DATA, MASS-SPECTROMETRY, CLINICAL PROTEOMICS, URINARY PROTEOME, SAMPLE-SIZE, VERIFICATION BIAS, DISCOVERY, CANCER, SERUM|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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