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In this paper, we ask if it is possible to increase the interpretability inmultivariate analysis by aligning and projecting covariates onto comparativesubspaces. We demonstrate our method as well as the interpretative power of PLSdecomposed models and how robust interpretability can lead to quantitativeinsights.  We discuss the statistical properties of the PLS weights, $p$-valuesassociated with specific axes, as well as their alignment properties.  The applicability of this approach within life science is also demonstratedby applying it to three use cases of publically available datasets.  Further we present hierarchical pathway enrichment results stemming fromaligned $p$-values, which are compared with results derived from enrichmentanalysis, as an external validation of our method.  We find that the method can uncover known results from genomics for all ofthe studied use cases, i.e. microarray data from multiple sclerosis anddiabetes patients as well as RNA sequencing data from breast cancer patients.

Exploratory Projection to Latent Structure Models for use in Transcriptomic Analysis