Journal
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
Volume 410, Issue 24, Pages 6305-6319Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00216-018-1240-2
Keywords
Metabolomics; Reliability; Bootstrap; Uncertainty estimation; Chemometrics; Biomarker discovery
Funding
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (National Council for Scientific and Technological Development) of Brazil [REF.203264/2014-26]
Ask authors/readers for more resources
Recent progress in metabolomics has been aided by the development of analysis techniques such as gas and liquid chromatography coupled with mass spectrometry (GC-MS and LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The vast quantities of data produced by these techniques has resulted in an increase in the use of machine algorithms that can aid in the interpretation of this data, such as principal components analysis (PCA) and partial least squares (PLS). Techniques such as these can be applied to biomarker discovery, interlaboratory comparison, and clinical diagnoses. However, there is a lingering question whether the results of these studies can be applied to broader sets of clinical data, usually taken from different data sources. In this work, we address this question by creating a metabolomics workflow that combines a previously published consensus analysis procedure with PCA and PLS models using uncertainty analysis based on bootstrapping. This workflow is applied to NMR data that come from an interlaboratory comparison study using synthetic and biologically obtained metabolite mixtures. The consensus analysis identifies trusted laboratories, whose data are used to create classification models that are more reliable than without. With uncertainty analysis, the reliability of the classification can be rigorously quantified, both for data from the original set and from new data that the model is analyzing.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available