Assessing the statistical validity of proteomics based biomarkers

Smit, Suzanne; Breemen, Mariëlle J. van; Hoefsloot, Huub C. J.; Smilde, Age K.; Aerts, Johannes M. F. G.; Koster, Chris G. de

doi:10.1016/j.aca.2007.04.043

Cited by 167 publications

(157 citation statements)

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“…PLSDA was used to discriminate between the two groups. 25 cross model validations [Anderssen et al (2006)] or sometimes called double cross validation [Smit et al (2007)] were performed. In each double cross validation the samples were divided into seven groups.…”

Section: Dmentioning

confidence: 99%

Discriminant Q2 (DQ2) for improved discrimination in PLSDA models

et al. 2008

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Section: Dmentioning

confidence: 99%

Discriminant Q2 (DQ2) for improved discrimination in PLSDA models

et al. 2008

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“…In the first analysis, multiple linear regression analysis with forward stepwise variable selection was performed within a double-cross validation scheme with 7-fold single-cross validation and 8-fold double-cross validation (Smit et al, 2007) (inhouse written routine available on request). The analysis was repeated 30 times with different assignments of samples into validation, calibration, and test sets.…”

Section: Added Value Of Metabolomics Analysismentioning

confidence: 99%

Gender-Dependent Associations of Metabolite Profiles and Body Fat Distribution in a Healthy Population with Central Obesity: Towards Metabolomics Diagnostics

Szymańska

Bouwman

Strassburg

et al. 2012

OMICS: A Journal of Integrative Biology

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Gender-dependent associations of metabolite profiles and body fat distribution in a healthy population with central obesity: towards metabolomics diagnostics Szymanska, E.; Bouwman, J.; Strassburg, K.; Vervoort, J.; Kangas, A.J.; Soininen, P.; AlaKorpela, M.; Westerhuis, J.A.; van Duynhoven, J.P.M.; Mela, D.J.; Macdonald, I.A.; Vreeken, R.J.; Smilde, A.K.; Jacobs, D.M. Published in: Omics DOI:10.1089/omi.2012.0062 Link to publication Citation for published version (APA):Szymaska, E., Bouwman, J., Strassburg, K., Vervoort, J., Kangas, A. J., Soininen, P., ... Jacobs, D. M. (2012). Gender-dependent associations of metabolite profiles and body fat distribution in a healthy population with central obesity: towards metabolomics diagnostics. Omics, 16(12), 652-667. DOI: 10.1089/omi.2012.0062 General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. AbstractObesity is a risk factor for cardiovascular diseases and type 2 diabetes especially when the fat is accumulated to central depots. Novel biomarkers are crucial to develop diagnostics for obesity and related metabolic disorders. We evaluated the associations between metabolite profiles (136 lipid components, 12 lipoprotein subclasses, 17 low-molecular-weight metabolites, 12 clinical markers) and 28 phenotype parameters (including different body fat distribution parameters such as android (A), gynoid (G), abdominal visceral (VAT), subcutaneous (SAT) fat) in 215 plasma/serum samples from healthy overweight men (n = 32) and women (n = 83) with central obesity.(Partial) correlation analysis and partial least squares (PLS) regression analysis showed that only specific metabolites were associated to A:G ratio, VAT, and SAT, respectively. These association patterns were gender dependent. For example, insulin, cholesterol, VLDL, and certain triacylglycerols (TG 54:1-3) correlated to VAT in women, while in men VAT was associated with TG 50:1-5, TG 55:1, phosphatidylcholine (PC 32:0), and VLDL ((X)L). Moreover, multiple regression analysis revealed that waist circumference and total fat were sufficient to predict VAT and SAT in women. In contrast, only VAT but not SAT could be predicted in men and only when plasma metabolites were included, with PC 32:0 being most strongly associated with VAT. These findings collectively highlight the p...

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“…Other model validation techniques commonly employed include Leave one out cross validation or up to 10-fold cross validation [60,61]. Pérez-Guaita et al [62] more recently evaluated the use of permutation testing, commonly used in metabolomics [63] and proteomics [64], which employs a random reallocation of class labels in order to establish the statistical significance of a cross-validation figure of merit of a classifier. Ultimately, however, the validation of the integrated techniques of spectroscopy and multivariate classifiers will have to comply with the rigours of the clinical environment, including large scale blind datasets and randomised trials [1].…”

Section: Discussionmentioning

confidence: 99%

Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells

et al. 2016

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Vibrational Spectroscopy, both infrared absorption and Raman spectroscopy, have attracted increasing attention for biomedical applications, from in vivo and ex vivo disease diagnostics and screening, to in vitro screening of therapeutics. There remain, however, many challenges related to the accuracy of analysis of physically and chemically inhomogeneous samples, across heterogeneous sample sets. Data preprocessing is required to deal with variations in instrumental responses and intrinsic spectral backgrounds and distortions in order to extract reliable spectral data. Data postprocessing is required to extract the most reliable information from the sample sets, based on often very subtle changes in spectra associated with the targeted pathology or biochemical process. This review presents the current understanding of the factors influencing the quality of spectra recorded and the pre-processing steps commonly employed to improve on spectral quality. It further explores some of the most common techniques which have emerged for classification and analysis of the spectral data for biomedical applications. The importance of sample presentation and measurement conditions to yield the highest quality spectra in the first place is emphasised, as is the potential of model simulated datasets to validate both pre-and post-processing protocols.

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Assessing the statistical validity of proteomics based biomarkers

Cited by 167 publications

References 15 publications

Discriminant Q2 (DQ2) for improved discrimination in PLSDA models

Discriminant Q2 (DQ2) for improved discrimination in PLSDA models

Gender-Dependent Associations of Metabolite Profiles and Body Fat Distribution in a Healthy Population with Central Obesity: Towards Metabolomics Diagnostics

Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells

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