Dalarna University's logo and link to the university's website

du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Application of a genomic model for high-dimensional chemometric analysis
Dalarna University, School of Technology and Business Studies, Statistics.ORCID iD: 0000-0002-1057-5401
Show others and affiliations
2014 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, no 7, p. 548-557Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
2014. Vol. 28, no 7, p. 548-557
Keywords [en]
genomics, chemometrics, heteroscedastic effects model, generalized ridge regression, high-dimensional data
National Category
Genetics Probability Theory and Statistics
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-18634DOI: 10.1002/cem.2614ISI: 000340503500002Scopus ID: 2-s2.0-84904390647OAI: oai:DiVA.org:du-18634DiVA, id: diva2:838760
Available from: 2015-07-01 Created: 2015-06-29 Last updated: 2021-11-12Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Rönnegård, Lars

Search in DiVA

By author/editor
Rönnegård, Lars
By organisation
Statistics
In the same journal
Journal of Chemometrics
GeneticsProbability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 546 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf