Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/89433
Title: | Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles | Authors: | Lawlor, Nathan Fabbri, Alec Guan, Peiyong George, Joshy Karuturi, R. Krishna Murthy |
Keywords: | DRNTU::Engineering::Computer science and engineering R Software Gene Selection |
Issue Date: | 2016 | Source: | Lawlor, N., Fabbri, A., Guan, P., George, J., & Karuturi, R. K. M. (2016). multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles. Cancer Informatics, 15, 103-114. doi:10.4137/CIN.S38000 | Series/Report no.: | Cancer Informatics | Abstract: | Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. | URI: | https://hdl.handle.net/10356/89433 http://hdl.handle.net/10220/47077 |
DOI: | 10.4137/CIN.S38000 | Schools: | School of Computer Science and Engineering | Rights: | © 2016 the authors, publisher and licensee Libertas Academica Limited. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
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multiClust- An R-package for Identifying Biologically.pdf | 4.98 MB | Adobe PDF | View/Open |
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