A1 Refereed original research article in a scientific journal

A case study of normalization, missing data and variable selection methods in lipidomics




AuthorsM. Kujala, J. Nevalainen

PublisherWILEY-BLACKWELL

Publication year2015

JournalStatistics in Medicine

Journal name in sourceSTATISTICS IN MEDICINE

Journal acronymSTAT MED

Volume34

Issue1

First page 59

Last page73

Number of pages15

ISSN0277-6715

DOIhttps://doi.org/10.1002/sim.6296(external)


Abstract

Lipidomics is an emerging field of science that holds the potential to provide a readout of biomarkers for an early detection of a disease. Our objective was to identify an efficient statistical methodology for lipidomicsespecially in finding interpretable and predictive biomarkers useful for clinical practice. In two case studies, we address the need for data preprocessing for regression modeling of a binary response. These are based on a normalization step, in order to remove experimental variability, and on a multiple imputation step, to make the full use of the incompletely observed data with potentially informative missingness. Finally, by cross-validation, we compare stepwise variable selection to penalized regression models on stacked multiple imputed data sets and propose the use of a permutation test as a global test of association. Our results show that, depending on the design of the study, these data preprocessing methods modestly improve the precision of classification, and no clear winner among the variable selection methods is found. Lipidomics profiles are found to be highly important predictors in both of the two case studies.




Last updated on 2024-26-11 at 17:50