Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.
Bioinformatics
; 39(1)2023 01 01.
Article
em En
| MEDLINE
| ID: mdl-36472455
ABSTRACT
MOTIVATION Making sense of networked multivariate association patterns is vitally important to many areas of high-dimensional analysis. Unfortunately, as the data-space dimensions grow, the number of association pairs increases in O(n2); this means that traditional visualizations such as heatmaps quickly become too complicated to parse effectively. RESULTS:
Here, we present associationSubgraphs a new interactive visualization method to quickly and intuitively explore high-dimensional association datasets using network percolation and clustering. The goal is to provide an efficient investigation of association subgraphs, each containing a subset of variables with stronger and more frequent associations among themselves than the remaining variables outside the subset, by showing the entire clustering dynamics and providing subgraphs under all possible cutoff values at once. Particularly, we apply associationSubgraphs to a phenome-wide multimorbidity association matrix generated from an electronic health record and provide an online, interactive demonstration for exploring multimorbidity subgraphs. AVAILABILITY AND IMPLEMENTATION An R package implementing both the algorithm and visualization components of associationSubgraphs is available at https//github.com/tbilab/associationsubgraphs. Online documentation is available at https//prod.tbilab.org/associationsubgraphs_info/. A demo using a multimorbidity association matrix is available at https//prod.tbilab.org/associationsubgraphs-example/.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Software
/
Multimorbidade
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Revista:
Bioinformatics
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos