Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization

Abstract

Motivation: Heterogeneous and voluminous data sources are common in modern datasets, particularly in systems biology studies. For instance, in multi-holistic approaches in the fruit biology field, data sources can include a mix of measurements such as morpho-agronomic traits, different kinds of molecules (nucleic acids and metabolites) and consumer preferences. These sources not only have different types of data (quantitative and qualitative), but also large amounts of variables with possibly non-linear relationships among them. An integrative analysis is usually hard to conduct, since it requires several manual standardization steps, with a direct and critical impact on the results obtained. These are important issues in clustering applications, which highlight the need of new methods for uncovering complex relationships in such diverse repositories. Results: We designed a new method named Clustermatch to easily and efficiently perform datamining tasks on large and highly heterogeneous datasets. Our approach can derive a similarity measure between any quantitative or qualitative variables by looking on how they influence on the clustering of the biological materials under study. Comparisons with other methods in both simulated and real datasets show that Clustermatch is better suited for finding meaningful relationships in complex datasets.

An efficient not-only-linear correlation coefficient based on machine learning

“…Recent implementations of MIC, for example, take several seconds to compute on a single variable pair across a few thousand objects or conditions [26]. We previously developed a clustering method for highly diverse datasets that significantly outperformed approaches based on Pearson, Spearman, DC and MIC in detecting clusters of simulated linear and nonlinear relationships with varying noise levels [29]. Here we introduce the Clustermatch Correlation Coefficient (CCC), an efficient not-only-linear coefficient that works across quantitative and qualitative variables.…”

Polyamines and legumes: Joint stories of stress, nitrogen fixation and environment

“…A prevalent question is how we integrate large extents of variables, both quantitative and qualitative, which may have a priori non-linear relationships between them. Some resources are emerging which provide insights into complex relationships derived from highly heterogeneous datasets (including the capacity to assess not only omics data but also morpho-agronomic traits) such as Cluster-match which has promising features in next generation agronomic data-mining (Pividori et al, 2018).…”