Deep Ranking Analysis by Power Eigenvectors (DRAPE): A wizard for ranking and multi-criteria decision making

Abstract Ranking and multi-criteria decision-making approaches are useful tools to analyse multivariate data and obtain useful insights into data structure and the relationships between samples and variables. In this study, we present a new ranking approach, named Deep Ranking Analysis by Kendall Eigenvectors (DRAKE), which is based on the Power-Weakness Ratio analysis and provides a set of sequential rankings. Such a sequential ranking procedure allows to gather deeper insights into the analysed dataset. Moreover, by a “retro”-regression procedure, the relevance of each variable in determining the final rankings can be assessed, while a consensus ranking can be obtained by a Principal Component Analysis (PCA). In this study, we present the theory of the novel method, and show two applications to real datasets.

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