Fast tree aggregation for consensus hierarchical clustering

Abstract

Background: In unsupervised learning and clustering, data integration from different sources and types is a difficult question discussed in several research areas. For instance in omics analysis, dozen of clustering methods have been developed in the past decade. When a single source of data is at play, hierarchical clustering (HC) is extremely popular, as a tree structure is highly interpretable and arguably more informative than just a partition of the data. However, applying blindly HC to multiple sources of data raises computational and interpretation issues. Results: We propose mergeTrees, a method that aggregates a set of trees with the same leaves to create a consensus tree. In our consensus tree, a cluster at height h contains the individuals that are in the same cluster for all the trees at height h. The method is exact and proven to be O(nq log(n)), n being the individuals and q being the number of trees to aggregate. Our implementation is extremely effective on simulations, allowing us to process many large trees at a time. We also rely on mergeTrees to perform the cluster analysis of two real-omics data sets, introducing a spectral variant as an efficient and robust by-product. Conclusions: Our tree aggregation method can be used in conjunction with hierarchical clustering to perform efficient cluster analysis. This approach was found to be robust to the absence of clustering information in some of the data sets as well as an increased variability within true clusters. The method is implemented in R/C++ and available as an R package named mergeTrees, which makes it easy to integrate in existing or new pipelines in several research areas.

Comparative phylogenetics of repetitive elements in a diverse order of flowering plants (Brassicales)

“…To construct a consensus dendrogram, hierarchical clusters produced with Copia , Gypsy , and total TE abundances were used. The consensus dendrogram was calculated using the mergeTree v 0.1.3 package in R ( Hulot et al 2019 ). C. serrulata was not included in these analyses, due to the lack of DNA sequencing data for this species.…”

Phenotype clustering in health care: A narrative review for clinicians

“…Consensus clustering, sometimes called aggregated clustering or clustering ensembles, uses multiple clusterings derived from (a) different clustering algorithms, (b) multiple permutations of a single algorithm, or (c) multiple iterations of a single algorithm on subgroups of a dataset to derive one, final set of cluster assignments. Consensus clustering has the theoretical advantages of minimizing overfitting and optimizing stability of cluster assignments, as has been shown for hierarchical clustering on genomic datasets from disparate sources and for identifying subgroups of heterogeneous intensive care unit patients (Vranas et al, 2017;Hulot et al, 2020).…”

Re-interpreting Rules Interpretability

“…Since our approach involves unions of rules, it is partly related to the works where rules or decision trees are merged for various purposes. Hierarchical merging of several trees was addressed in the context of the problem of learning decision trees from multiple sources of the data-so the challenge is to produce one tree that will cover the decisions of others [25]. Another problem that is addressed is construction of consensus trees from different ones with the goal of producing a more stable model [26].…”