期刊
ANALYTICA CHIMICA ACTA
卷 592, 期 2, 页码 210-217出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2007.04.043
关键词
biomarker discovery; classification; curse of dimensionality; statistical validation; double cross-validation; principal component discriminant analysis; Gaucher disease; rank products; permutation test; surface enhanced laser desorption ionization time-of-flight mass spectrometry
A strategy is presented for the statistical validation of discrimination models in proteomics studies. Several existing tools are combined to form a solid statistical basis for biomarker discovery that should precede a biochemical validation of any biomarker. These tools consist of permutation tests, single and double cross-validation. The cross-validation st eps can simply be combined with a new variable selection method, called rank products. The strategy is especially suited for the low-samples-to-variables-ratio (undersampling) case, as is often encountered in proteomics and metabolomics studies. As a classification method, principal component discriminant analysis is used; however, the methodology can be used with any classifier. A dataset containing serum samples from Gaucher patients and healthy controls serves as a test case. Double cross-validation shows that the sensitivity of the model is 89% and the specificity 90%. Potential putative biomarkers are identified using the novel variable selection method. Results from permutation tests support the choice of double cross-validation as the tool for determining error rates when the modelling procedure involves a tuneable parameter, This shows that even cross-validation does not guarantee unbiased results. The validation of discrimination models with a combination of permutation tests and double cross-validation helps to avoid erroneous results which may result from the undersampling. (C) 2007 Elsevier B.V All rights reserved.
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