Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) <doi:10.3389/fmicb.2023.1170391>.

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) <doi:10.1016/j.aca.2013.01.022>.

Helping biologists to choose the most suitable approach to link their research to conservation. After answering few questions on the data available, geographic and taxonomic scope, 'conserveR' ranks existing methods for conservation prioritization and systematic conservation planning by suitability. The methods data base of 'conserveR' contains 133 methods for conservation prioritization based on a systematic review of > 12,000 scientific publications from the fields of spatial conservation prioritization, systematic conservation planning, biogeography and ecology.