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 PLS being popular in multivariate analysis, describing specificity of latent variables for corresponding effects. We discuss the statistical properties of the PLS 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 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.
R
PLS
OPLS
R
PLS weights, p values
OPLS weights, p-values
R
p values
p-values