umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
Proposed minimum reporting standards for data analysis in metabolomics
Show others and affiliations
2007 (English)In: Metabolomics, Vol. 3, 231-41 p.Article in journal (Refereed) Published
Abstract [en]

The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses.

Place, publisher, year, edition, pages
2007. Vol. 3, 231-41 p.
Keyword [en]
Chemometrics, Multivariate, Megavariate, Unsupervised learning, Supervised learning, Informatics, Bioinformatics, Statistics, Biostatistics, Machine learning, Statistical learning
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:umu:diva-16435DOI: doi:10.1007/s11306-007-0081-3OAI: oai:DiVA.org:umu-16435DiVA: diva2:156108
Available from: 2007-09-27 Created: 2007-09-27 Last updated: 2012-09-05

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Sjöström, MichaelTrygg, Johan

Search in DiVA

By author/editor
Sjöström, MichaelTrygg, Johan
By organisation
Department of Chemistry
Biological Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 84 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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