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Application of a genomic model for high-dimensional chemometric analysis
Högskolan Dalarna, Akademin Industri och samhälle, Statistik.ORCID-id: 0000-0002-1057-5401
Vise andre og tillknytning
2014 (engelsk)Inngår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, nr 7, s. 548-557Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The rapid development of newtechnologies for large-scale analysis of genetic variation in the genomes of individuals and populations has presented statistical geneticists with a grand challenge to develop efficient methods for identifying the small proportion of all identified genetic polymorphisms that have effects on traits of interest. To address such a "large p small n" problem, we have developed a heteroscedastic effects model (HEM) that has been shown to be powerful in high-throughput genetic analyses. Here, we describe how this whole-genome model can also be utilized in chemometric analysis. As a proof of concept, we use HEM to predict analyte concentrations in silage using Fourier transform infrared spectroscopy signals. The results show that HEM often outperforms the classic methods and in addition to this presents a substantial computational advantage in the analyses of such high-dimensional data. The results thus show the value of taking an interdisciplinary approach to chemometric analysis and indicate that large-scale genomic models can be a promising new approach for chemometric analysis that deserve to be evaluated more by experts in the field. The software used for our analyses is freely available as an R package at http://cran.r-project.org/web/packages/bigRR/. Copyright (C) 2014 JohnWiley & Sons, Ltd.

sted, utgiver, år, opplag, sider
2014. Vol. 28, nr 7, s. 548-557
Emneord [en]
genomics, chemometrics, heteroscedastic effects model, generalized ridge regression, high-dimensional data
HSV kategori
Forskningsprogram
Forskningsprofiler 2009-2020, Komplexa system - mikrodataanalys
Identifikatorer
URN: urn:nbn:se:du-18634DOI: 10.1002/cem.2614ISI: 000340503500002Scopus ID: 2-s2.0-84904390647OAI: oai:DiVA.org:du-18634DiVA, id: diva2:838760
Tilgjengelig fra: 2015-07-01 Laget: 2015-06-29 Sist oppdatert: 2021-11-12bibliografisk kontrollert

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