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Statistical validation of megavariate effects in ASCA.

Vis DJ, Westerhuis JA, Smilde AK, van der Greef J - BMC Bioinformatics (2007)

Bottom Line: However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist.If the observed effect is clearly different from this distribution the effect is deemed significant The permutation approach is studied using simulated data which gave successful results.In this metabolomics experiment the dosage and time-interaction effect were validated, both effects are significant.

View Article: PubMed Central - HTML - PubMed

Affiliation: BioSystems Data Analysis group, Swammerdam Institute for Life Science, University of Amsterdam, The Netherlands. science@danielvis.nl

ABSTRACT

Background: Innovative extensions of (M) ANOVA gain common ground for the analysis of designed metabolomics experiments. ASCA is such a multivariate analysis method; it has successfully estimated effects in megavariate metabolomics data from biological experiments. However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist.

Methods: A permutation approach is used to validate megavariate effects observed with ASCA. By permuting the class labels of the underlying experimental design, a distribution of no-effect is calculated. If the observed effect is clearly different from this distribution the effect is deemed significant

Results: The permutation approach is studied using simulated data which gave successful results. It was then used on real-life metabolomics data set dealing with bromobenzene-dosed rats. In this metabolomics experiment the dosage and time-interaction effect were validated, both effects are significant. Histological screening of the treated rats' liver agrees with this finding.

Conclusion: The suggested procedure gives approximate p-values for testing effects underlying metabolomics data sets. Therefore, performing model validation is possible using the proposed procedure.

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Related in: MedlinePlus

Example study to certify the validation procedure, it consists of one significantly different and one nonsignificantly different data set. Figures A and C show the SSQ reference distribution found by permuting the data. If the red dot is outside most the reference distribution and is on the right side, the group is significantly different. The figures B and D show the data from this example experiment. Careful inspection of figure B reveals the top half differs from the bottom half, it is more yellow and red then the bottom half. The D figure lacks this property.
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Figure 1: Example study to certify the validation procedure, it consists of one significantly different and one nonsignificantly different data set. Figures A and C show the SSQ reference distribution found by permuting the data. If the red dot is outside most the reference distribution and is on the right side, the group is significantly different. The figures B and D show the data from this example experiment. Careful inspection of figure B reveals the top half differs from the bottom half, it is more yellow and red then the bottom half. The D figure lacks this property.

Mentions: Figures 1b &1d show the two example data sets, the rows are individual samples and the columns are the metabolites. The colored cells show each metabolite value of every sample.


Statistical validation of megavariate effects in ASCA.

Vis DJ, Westerhuis JA, Smilde AK, van der Greef J - BMC Bioinformatics (2007)

Example study to certify the validation procedure, it consists of one significantly different and one nonsignificantly different data set. Figures A and C show the SSQ reference distribution found by permuting the data. If the red dot is outside most the reference distribution and is on the right side, the group is significantly different. The figures B and D show the data from this example experiment. Careful inspection of figure B reveals the top half differs from the bottom half, it is more yellow and red then the bottom half. The D figure lacks this property.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2211757&req=5

Figure 1: Example study to certify the validation procedure, it consists of one significantly different and one nonsignificantly different data set. Figures A and C show the SSQ reference distribution found by permuting the data. If the red dot is outside most the reference distribution and is on the right side, the group is significantly different. The figures B and D show the data from this example experiment. Careful inspection of figure B reveals the top half differs from the bottom half, it is more yellow and red then the bottom half. The D figure lacks this property.
Mentions: Figures 1b &1d show the two example data sets, the rows are individual samples and the columns are the metabolites. The colored cells show each metabolite value of every sample.

Bottom Line: However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist.If the observed effect is clearly different from this distribution the effect is deemed significant The permutation approach is studied using simulated data which gave successful results.In this metabolomics experiment the dosage and time-interaction effect were validated, both effects are significant.

View Article: PubMed Central - HTML - PubMed

Affiliation: BioSystems Data Analysis group, Swammerdam Institute for Life Science, University of Amsterdam, The Netherlands. science@danielvis.nl

ABSTRACT

Background: Innovative extensions of (M) ANOVA gain common ground for the analysis of designed metabolomics experiments. ASCA is such a multivariate analysis method; it has successfully estimated effects in megavariate metabolomics data from biological experiments. However, rigorous statistical validation of megavariate effects is still problematic because megavariate extensions of the classical F-test do not exist.

Methods: A permutation approach is used to validate megavariate effects observed with ASCA. By permuting the class labels of the underlying experimental design, a distribution of no-effect is calculated. If the observed effect is clearly different from this distribution the effect is deemed significant

Results: The permutation approach is studied using simulated data which gave successful results. It was then used on real-life metabolomics data set dealing with bromobenzene-dosed rats. In this metabolomics experiment the dosage and time-interaction effect were validated, both effects are significant. Histological screening of the treated rats' liver agrees with this finding.

Conclusion: The suggested procedure gives approximate p-values for testing effects underlying metabolomics data sets. Therefore, performing model validation is possible using the proposed procedure.

Show MeSH
Related in: MedlinePlus