Statistically Supported Identification of Tumor Subtypes.
Hierarchical clustering
Permutation tests
Tumor subtypes
Unsupervised learning
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2019
2019
Historique:
entrez:
1
11
2018
pubmed:
1
11
2018
medline:
1
6
2019
Statut:
ppublish
Résumé
Identification of biologically and clinically consequential subtypes within tumor types is a long-standing goal of cancer bioinformatics. Here we provide practical guidance to the use of a recently developed statistical subtyping tool, termed Tree Branches Evaluated Statistically for Tightness (TBEST), and its eponymous R language implementation. TBEST employs hierarchical clustering to partition the data at a user-specified level of significance. Functionalities of the package are illustrated using as an example a benchmark data set of mRNA expression levels in leukemia.
Identifiants
pubmed: 30378078
doi: 10.1007/978-1-4939-8868-6_12
doi:
Substances chimiques
RNA, Messenger
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM