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Improving the analysis of designed studies by combining statistical modelling with study design information.

Thissen U, Wopereis S, van den Berg SA, Bobeldijk I, Kleemann R, Kooistra T, van Dijk KW, van Ommen B, Smilde AK - BMC Bioinformatics (2009)

Bottom Line: Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects.Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects.It was shown that ANOVA-PLS leads to a better statistical model that is more reliable and better interpretable compared to standard PLS analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl

ABSTRACT

Background: In the fields of life sciences, so-called designed studies are used for studying complex biological systems. The data derived from these studies comply with a study design aimed at generating relevant information while diminishing unwanted variation (noise). Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects. Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects.

Results: The benefit of this approach was demonstrated with an analysis that combines multivariate PLS (Partial Least Squares) regression with data decomposition from ANOVA (Analysis of Variance): ANOVA-PLS. As a case, a nutritional intervention study is used on Apoliprotein E3-Leiden (APOE3Leiden) transgenic mice to study the relation between liver lipidomics and a plasma inflammation marker, Serum Amyloid A. The ANOVA-PLS performance was compared to PLS regression on the non-decomposed data with respect to the quality of the modelled relation, model reliability, and interpretability.

Conclusion: It was shown that ANOVA-PLS leads to a better statistical model that is more reliable and better interpretable compared to standard PLS analysis. From a following biological interpretation, more relevant metabolites were derived from the model. The concept of combining data composition with a subsequent statistical analysis, as in ANOVA-PLS, is however not limited to PLS regression in metabolomics but can be applied for many statistical methods and many different types of data.

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ANOVA results of the SAA data. The SAA ANOVA results are shown for the factors time (top), diet (middle), and their interaction (bottom). In the interaction model, the grey line represents mice on Chow diet; the black line represents mice on HF-palm diet; and the dotted line represents mice on HF-bovine diet. The corresponding p-values are 0.13, 9·10-11, and 0.17, respectively.
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Figure 5: ANOVA results of the SAA data. The SAA ANOVA results are shown for the factors time (top), diet (middle), and their interaction (bottom). In the interaction model, the grey line represents mice on Chow diet; the black line represents mice on HF-palm diet; and the dotted line represents mice on HF-bovine diet. The corresponding p-values are 0.13, 9·10-11, and 0.17, respectively.

Mentions: Figure 5 shows the ANOVA results on the univariate SAA measurements. In this model, only the diet effect (48%) is significant (p = 9·10-11). The diet effect in the SAA measurements shows a similar trend as the diet effect in the lipidomics measurements, even though the latter effect is much smaller. The other effects (time: 4%; p = 0.13 and time × diet: 6.5%; p = 0.17) show a different behaviour for SAA than for the LC-MS metabolites. In addition, these effects have high p-values indicating that they do not reach significance in terms of the traditional cut-offs (α = 0.05 or α = 0.1).


Improving the analysis of designed studies by combining statistical modelling with study design information.

Thissen U, Wopereis S, van den Berg SA, Bobeldijk I, Kleemann R, Kooistra T, van Dijk KW, van Ommen B, Smilde AK - BMC Bioinformatics (2009)

ANOVA results of the SAA data. The SAA ANOVA results are shown for the factors time (top), diet (middle), and their interaction (bottom). In the interaction model, the grey line represents mice on Chow diet; the black line represents mice on HF-palm diet; and the dotted line represents mice on HF-bovine diet. The corresponding p-values are 0.13, 9·10-11, and 0.17, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2657790&req=5

Figure 5: ANOVA results of the SAA data. The SAA ANOVA results are shown for the factors time (top), diet (middle), and their interaction (bottom). In the interaction model, the grey line represents mice on Chow diet; the black line represents mice on HF-palm diet; and the dotted line represents mice on HF-bovine diet. The corresponding p-values are 0.13, 9·10-11, and 0.17, respectively.
Mentions: Figure 5 shows the ANOVA results on the univariate SAA measurements. In this model, only the diet effect (48%) is significant (p = 9·10-11). The diet effect in the SAA measurements shows a similar trend as the diet effect in the lipidomics measurements, even though the latter effect is much smaller. The other effects (time: 4%; p = 0.13 and time × diet: 6.5%; p = 0.17) show a different behaviour for SAA than for the LC-MS metabolites. In addition, these effects have high p-values indicating that they do not reach significance in terms of the traditional cut-offs (α = 0.05 or α = 0.1).

Bottom Line: Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects.Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects.It was shown that ANOVA-PLS leads to a better statistical model that is more reliable and better interpretable compared to standard PLS analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dutch nutrigenomics consortium of the Top Institute Food and Nutrition (TIFN), Wageningen, The Netherlands. uwe.thissen@tno.nl

ABSTRACT

Background: In the fields of life sciences, so-called designed studies are used for studying complex biological systems. The data derived from these studies comply with a study design aimed at generating relevant information while diminishing unwanted variation (noise). Knowledge about the study design can be used to decompose the total data into data blocks that are associated with specific effects. Subsequent statistical analysis can be improved by this decomposition if these are applied on selected combinations of effects.

Results: The benefit of this approach was demonstrated with an analysis that combines multivariate PLS (Partial Least Squares) regression with data decomposition from ANOVA (Analysis of Variance): ANOVA-PLS. As a case, a nutritional intervention study is used on Apoliprotein E3-Leiden (APOE3Leiden) transgenic mice to study the relation between liver lipidomics and a plasma inflammation marker, Serum Amyloid A. The ANOVA-PLS performance was compared to PLS regression on the non-decomposed data with respect to the quality of the modelled relation, model reliability, and interpretability.

Conclusion: It was shown that ANOVA-PLS leads to a better statistical model that is more reliable and better interpretable compared to standard PLS analysis. From a following biological interpretation, more relevant metabolites were derived from the model. The concept of combining data composition with a subsequent statistical analysis, as in ANOVA-PLS, is however not limited to PLS regression in metabolomics but can be applied for many statistical methods and many different types of data.

Show MeSH
Related in: MedlinePlus