Sun, G., Krasnitz, A. (2019) Statistically Supported Identification of Tumor Subtypes. Methods Mol Biol, 1878. pp. 209-216. ISSN 1064-3745
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: | bioinformatics diseases & disorders > cancer bioinformatics > genomics and proteomics > computers bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification diseases & disorders bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics diseases & disorders > neoplasms bioinformatics > computational biology bioinformatics > genomics and proteomics > computers > computer software bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > mRNA |
| CSHL Authors: | |
| Communities: | CSHL labs > Krasnitz lab CSHL Cancer Center Program > Cancer Genetics and Genomics Program |
| Depositing User: | Matthew Dunn |
| Date: | 2019 |
| Date Deposited: | 12 Nov 2018 21:51 |
| Last Modified: | 02 Feb 2024 21:03 |
| Related URLs: | |
| URI: | https://repository.cshl.edu/id/eprint/37360 |
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