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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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The design of the study. This figure shows the division of the mice into the three diet groups and the time points on which the measurements have been performed.
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Figure 1: The design of the study. This figure shows the division of the mice into the three diet groups and the time points on which the measurements have been performed.

Mentions: The study design is shown in Figure 1. The study involved 72 male ApoE3-Leiden mice with an age of 14 ± 2 weeks. Three weeks before the start of the study, the mice were fed a standard chow diet. At the start of the study, 24 mice were switched to a High Fat Bovine diet (HF-bovine; 45 energy % bovine lard + 0.25% cholesterol), 24 62 mice to a High Fat Palm Oil diet (HF-palm; 45 energy % palm oil), and 24 mice stayed on the Chow diet. Of each diet group, 6 mice were sacrificed at time points 1 and 3 days, and 1 and 2 weeks. As a consequence of the study design, important factors underlying the data sets are time, diet, their interaction: time × diet, and individual (biological) variation.


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)

The design of the study. This figure shows the division of the mice into the three diet groups and the time points on which the measurements have been performed.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: The design of the study. This figure shows the division of the mice into the three diet groups and the time points on which the measurements have been performed.
Mentions: The study design is shown in Figure 1. The study involved 72 male ApoE3-Leiden mice with an age of 14 ± 2 weeks. Three weeks before the start of the study, the mice were fed a standard chow diet. At the start of the study, 24 mice were switched to a High Fat Bovine diet (HF-bovine; 45 energy % bovine lard + 0.25% cholesterol), 24 62 mice to a High Fat Palm Oil diet (HF-palm; 45 energy % palm oil), and 24 mice stayed on the Chow diet. Of each diet group, 6 mice were sacrificed at time points 1 and 3 days, and 1 and 2 weeks. As a consequence of the study design, important factors underlying the data sets are time, diet, their interaction: time × diet, and individual (biological) variation.

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