Recursive Consensus Clustering for novel subtype discovery from transcriptome data.
Sci Rep
; 10(1): 11005, 2020 07 03.
Article
en En
| MEDLINE
| ID: mdl-32620805
ABSTRACT
Large-scale transcriptomic data is used by biologists for the discovery of new molecular patterns or cell subpopulations. Clustering is one of the most popular methods for dimensionality reduction and data analysis for large scale datasets. The major problem while clustering the data is the selection of the optimal number of clusters (k) for each dataset and to discover new insights from it. We have developed Recursive Consensus Clustering (RCC), an unsupervised clustering algorithm for novel subtype discovery from both bulk and single-cell datasets. RCC is available as an R package and facilitates the generation of new biological insights through intuitive visualization of clustering results.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Perfilación de la Expresión Génica
/
Análisis de la Célula Individual
Límite:
Humans
Idioma:
En
Revista:
Sci Rep
Año:
2020
Tipo del documento:
Article
País de afiliación:
India