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ClustAll: An R package for patient stratification in complex diseases.

Authors :
Asier Ortega-Legarreta
Sara Palomino-Echeverria
Estefania Huergo
Vincenzo Lagani
Narsis A Kiani
Pierre-Emmanuel Rautou
Nuria Planell Picola
Jesper Tegner
David Gomez-Cabrero
Source :
PLoS Computational Biology, Vol 20, Iss 12, p e1012656 (2024)
Publication Year :
2024
Publisher :
Public Library of Science (PLoS), 2024.

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.

Subjects

Subjects :
Biology (General)
QH301-705.5

Details

Language :
English
ISSN :
1553734X, 15537358, and 35925701
Volume :
20
Issue :
12
Database :
Directory of Open Access Journals
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
edsdoj.b2a35925701246e3b3d45c2e86e5a2fa
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pcbi.1012656