In this paper, we demonstrate the interpretative power of partial least square models and how robust interpretability can lead to new quantitative insights. Interpretability methods for partial least square decomposition are useful by virtue of OPLS being popular in multivariate analysis, describing specificity of latent variables for corresponding effects. We discuss the statistical properties of the OPLS weights, p-values associated with specific axes, as well as their alignment properties. We present hierarchical pathway enrichment results stemming from aligned p-values, which are compared with results derived from enrichment analysis, as an external validation of our method. The applicability of this approach is further demonstrated by applying it to both publically available microarray data from multiple sclerosis and diabetes patients as well as RNA sequencing data from breast cancer patients . Our method confers more global, physiologically relevant, knowledge about the datasets than traditional and more myopic methods .
In this paper, we ask if it is possible to increase the interpretability in multivariate analysis by aligning and projecting covariates onto comparative subspaces. We demonstrate our method as well as the interpretative power of PLS decomposed models and how robust interpretability can lead to quantitative insights. We discuss the statistical properties of the PLS weights, p-values associated with specific axes, as well as their alignment properties. The applicability of this approach within life science is also demonstrated by applying it to three use cases of publically available datasets. Further we present hierarchical pathway enrichment results stemming from aligned p-values, which are compared with results derived from enrichment analysis, as an external validation of our method. We find that the method can uncover known results from genomics for all of the studied use cases, i.e. microarray data from multiple sclerosis and diabetes patients as well as RNA sequencing data from breast cancer patients .
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demonstrate
ask if it is possible to increase the interpretability in multivariate analysis by aligning and projecting covariates onto comparative subspaces. We demonstrate our method as well as
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partial least square
PLS decomposed
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new
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Interpretability methods for partial least square decomposition are useful by virtue of OPLS being popular in multivariate analysis, describing specificity of latent variables for corresponding effects.
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OPLS
PLS
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We
The applicability of this approach within life science is also demonstrated by applying it to three use cases of publically available datasets. Further we
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The applicability of this approach is further demonstrated by applying it to both publically available
We find that the method can uncover known results from genomics for all of the studied use cases, i.e.
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. Our method confers more global, physiologically relevant, knowledge about the datasets than traditional and more myopic methods