ClustAll: An R package for patient stratification in complex diseases

In the era of precision medicine, it is necessary to understand heterogeneity among patients with complex diseases to improve personalized prevention and management strategies. Here, we introduce ClustAll, a Bioconductor package designed for unsupervised patient stratification using clinical data. ClustAll is based on the previously validated methodology ClustAll, a clustering framework that effectively handles intricacies in clinical data, including mixed data types, missing values, and collinearity. Additionally, ClustAll stands out in its ability to identify multiple patient stratifications within the same population while ensuring their robustness. The updated implementation of ClustAll features S4 classes, parallel computing for enhanced computational efficiency, and user-friendly tools for exploring and comparing stratifications against clinical phenotypes. The performance of ClustAll has been validated using two public clinical datasets, confirming its effectiveness in patient stratification and highlighting its potential impact on clinical management. In summary, ClustAll is a powerful tool for patient stratification in personalized medicine.

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Publication Details

Journal
PLoS Computational Biology
Published
2024-12-13
DOI
https://doi.org/10.1371/journal.pcbi.1012656
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
0.00

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article

ClustAll: An R package for patient stratification in complex diseases

Vincenzo Lagani, Jesper Tegnér, Pierre‐Emmanuel Rautou, Narsis A. Kiani et al.
PLoS Computational Biology
Genetic Associations and Epidemiology
article

ClustAll: An R package for patient stratification in complex diseases

Vincenzo Lagani, Jesper Tegnér, Pierre‐Emmanuel Rautou, Narsis A. Kiani, Sara Palomino‐Echeverría, David Gómez-Cabrero, Estefanía Huergo, Asier Ortega‐Legarreta, Nuria Planell Picola
article en

Abstract

In the era of precision medicine, it is necessary to understand heterogeneity among patients with complex diseases to improve personalized prevention and management strategies. Here, we introduce ClustAll, a Bioconductor package designed for unsupervised patient stratification using clinical data. ClustAll is based on the previously validated methodology ClustAll, a clustering framework that effectively handles intricacies in clinical data, including mixed data types, missing values, and collinearity. Additionally, ClustAll stands out in its ability to identify multiple patient stratifications within the same population while ensuring their robustness. The updated implementation of ClustAll features S4 classes, parallel computing for enhanced computational efficiency, and user-friendly tools for exploring and comparing stratifications against clinical phenotypes. The performance of ClustAll has been validated using two public clinical datasets, confirming its effectiveness in patient stratification and highlighting its potential impact on clinical management. In summary, ClustAll is a powerful tool for patient stratification in personalized medicine.

PLoS Computational BiologyVol. 20(12)
Karolinska University Hospital (SE), Inserm (FR), Universidad Pública de Navarra (UPNA) (ES), Ilia State University (GE), Université Paris Cité (FR), Hôpital Beaujon (FR), Karolinska Institutet (SE), Assistance Publique – Hôpitaux de Paris (FR), Navarre Institute of Health Research (ES), Navarrabiomed (ES), Centre de Recherche sur l'Inflammation (FR), King Abdullah University of Science and Technology (SA), Universidad de Navarra (ES)
European Commission, Institut National de la Santé et de la Recherche Médicale
Openalex Percentile: Top 74%
Genetic Associations and Epidemiology
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