Back to Search
Start Over
Fusing metabolomics data sets with heterogeneous measurement errors
- Source :
- PLoS ONE, 13(4), e0195939, PLoS ONE, Vol 13, Iss 4, p e0195939 (2018), PLoS ONE, PLoS ONE, 13(4), PLoS ONE, 13(4):e0195939. Public Library of Science
- Publication Year :
- 2018
-
Abstract
- Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement errors increase with increasing measurement level and that different platforms have different measurement error variance. In this paper we compare three different approaches to correct for the measurement error heterogeneity, by transformation of the raw data, by weighted filtering before modelling and by a modelling approach using a weighted sum of residuals. For an illustration of these different approaches we analyse data from healthy obese and diabetic obese individuals, obtained from two metabolomics platforms. Concluding, the filtering and modelling approaches that both estimate a model of the measurement error did not outperform the data transformation approaches for this application. This is probably due to the limited difference in measurement error and the fact that estimation of measurement error models is unstable due to the small number of repeats available. A transformation of the data improves the classification of the two groups. ispartof: PLOS ONE vol:13 issue:4 ispartof: location:United States status: published
- Subjects :
- 0301 basic medicine
PARTIAL LEAST-SQUARES
Computer science
Physiology
lcsh:Medicine
computer.software_genre
Biochemistry
Mass Spectrometry
0302 clinical medicine
Mathematical and Statistical Techniques
Metabolites
Medicine and Health Sciences
AMINO-ACIDS
DISCRIMINANT-ANALYSIS
Amino Acids
Amines
lcsh:Science
Chromatography, High Pressure Liquid
Principal Component Analysis
Multidisciplinary
Organic Compounds
Discriminant Analysis
Variance (accounting)
Lipids
Multidisciplinary Sciences
Chemistry
Physiological Parameters
Principal component analysis
Physical Sciences
Science & Technology - Other Topics
PROTEOMICS
Scientific Experimental Error
Data mining
Statistics (Mathematics)
Research Article
Quality Control
MODELS
Glycine
Data transformation (statistics)
Research and Analysis Methods
CLASSIFICATION
03 medical and health sciences
Humans
Metabolomics
Obesity
Statistical Methods
Science & Technology
Observational error
SUBJECT
Organic Chemistry
Body Weight
lcsh:R
COMPONENT ANALYSIS
Chemical Compounds
Biology and Life Sciences
Proteins
Linear discriminant analysis
Lipid Metabolism
TRANSFORMATION
030104 developmental biology
Transformation (function)
Metabolism
Aliphatic Amino Acids
Multivariate Analysis
Errors-in-variables models
lcsh:Q
computer
030217 neurology & neurosurgery
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, 13(4), e0195939, PLoS ONE, Vol 13, Iss 4, p e0195939 (2018), PLoS ONE, PLoS ONE, 13(4), PLoS ONE, 13(4):e0195939. Public Library of Science
- Accession number :
- edsair.doi.dedup.....3546cc60ca44e2f972d3fbde13b37db1