Statistically Supported Identification of Tumor Subtypes

Sun, G., Krasnitz, A. (2019) Statistically Supported Identification of Tumor Subtypes. Methods Mol Biol, 1878. pp. 209-216. ISSN 1064-3745

URL: https://www.ncbi.nlm.nih.gov/pubmed/30378078
DOI: 10.1007/978-1-4939-8868-6_12

Abstract

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.

Item Type: Paper
Subjects: diseases & disorders > cancer
bioinformatics > genomics and proteomics > computers > computer software
CSHL Authors:
Communities: CSHL labs > Krasnitz lab
Depositing User: Matthew Dunn
Date: 2019
Date Deposited: 12 Nov 2018 21:51
Last Modified: 12 Nov 2018 21:51
Related URLs:
URI: http://repository.cshl.edu/id/eprint/37360

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