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Article Dans Une Revue Bioinformatics Année : 2023

Automated calibration of consensus weighted distance-based clustering approaches using sharp

Barbara Bodinier
Dragana Vuckovic
  • Fonction : Auteur
Sabrina Rodrigues
  • Fonction : Auteur
Marc Chadeau-Hyam

Résumé

Motivation: In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms. Results: We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularised approaches. We propose a procedure for the calibration of the number of clusters (and regularisation parameter) by maximising a novel consensus score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) models calibrated by maximising our consensus score compared to existing calibration scores, and (ii) weighted compared to unweighted approaches in the presence of features that do not contribute to cluster definition. Application on real gene expression data measured in lung tissue reveals clear clusters corresponding to different lung cancer subtypes. Availability and implementation: The R package sharp (version ≥ 1.4.0) is available on CRAN.
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Dates et versions

hal-04440490 , version 1 (06-02-2024)

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Barbara Bodinier, Dragana Vuckovic, Sabrina Rodrigues, Sarah Filippi, Julien Chiquet, et al.. Automated calibration of consensus weighted distance-based clustering approaches using sharp. Bioinformatics, 2023, 39 (11), ⟨10.1093/bioinformatics/btad635⟩. ⟨hal-04440490⟩
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