Merging data from multiple sources is a relevant approach for
comprehensively evaluating complex systems. However, the inherent problems
encountered when analyzing single tables are amplified with the generation
of multi-block datasets, and finding the relationships between data layers
of increasing complexity constitutes a challenging task. For that purpose,
a generic methodology is proposed by combining the strength of established
data analysis strategies, i.e. multi-block approaches and the Orthogonal
Partial Least Squares (OPLS) framework to provide an efficient tool for the
fusion of data obtained from multiple sources. The package enables quick
and efficient implementation of the consensus OPLS model for any horizontal
multi-block data structures (observation-based matching). Moreover, it
offers an interesting range of metrics and graphics to help to determine
the optimal number of components and check the validity of the model
through permutation tests. Interpretation tools include score and loading
plots, Variable Importance in Projection (VIP), functionality predict for
SHAP computing, and performance coefficients such as R2, Q2, and DQ2
coefficients.
J. Boccard and D.N. Rutledge (2013)