Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 534480
Title Better interpretable models after correcting for natural variation : Residual approaches examined
Author(s) Koeman, Mike; Engel, Jasper; Jansen, Jeroen; Buydens, Lutgarde
Source Chemometrics and Intelligent Laboratory Systems 174 (2018). - ISSN 0169-7439 - p. 142 - 148.
DOI https://doi.org/10.1016/j.chemolab.2018.01.007
Department(s) Biometris (PPO/PRI)
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Disease diagnosis - Interpretation - Metabolomics - PCA - Residuals - Smearing
Abstract The interpretation of estimates of model parameters in terms of biological information is often just as important as the predictions of the model itself. In this study we consider the identification of metabolites in a possibly biologically heterogeneous case group that show abnormal patterns with respect to a set of (healthy) control observations. For this purpose, we filter normal (baseline) natural variation from the data by projection of the data on a control sample model: the residual approach. This step should more easily highlight the abnormal metabolites. Interpretation is, however, hindered by a problem we named the ‘residual bias’ effect, which may lead to the identification of the wrong metabolites as ‘abnormal’. This effect is related to the smearing effect. We propose to alleviate residual bias by considering a weighted average of the filtered and raw data. This way, a compromise is found between excluding irrelevant natural variation from the data and the amount of residual bias that occurs. We show for simulated and real-world examples that this compromise may outperform inspection of the raw or filtered data. The method holds promise in numerous applications such as disease diagnoses, personalized healthcare, and industrial process control.
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