Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles
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 g...
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sg-ntu-dr.10356-894332022-02-16T16:31:35Z Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles Lawlor, Nathan Fabbri, Alec Guan, Peiyong George, Joshy Karuturi, R. Krishna Murthy School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering R Software Gene Selection 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. Published version 2018-12-19T02:12:01Z 2019-12-06T17:25:23Z 2018-12-19T02:12:01Z 2019-12-06T17:25:23Z 2016 Journal Article 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 https://hdl.handle.net/10356/89433 http://hdl.handle.net/10220/47077 10.4137/CIN.S38000 27330269 en Cancer Informatics © 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. 12 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering R Software Gene Selection Lawlor, Nathan Fabbri, Alec Guan, Peiyong George, Joshy Karuturi, R. Krishna Murthy Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles |
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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. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Lawlor, Nathan Fabbri, Alec Guan, Peiyong George, Joshy Karuturi, R. Krishna Murthy |
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Article |
author |
Lawlor, Nathan Fabbri, Alec Guan, Peiyong George, Joshy Karuturi, R. Krishna Murthy |
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Lawlor, Nathan |
title |
Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles |
title_short |
Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles |
title_full |
Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles |
title_fullStr |
Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles |
title_full_unstemmed |
Multiclust: an R-package for identifying biologically relevant clusters in cancer transcriptome profiles |
title_sort |
multiclust: an r-package for identifying biologically relevant clusters in cancer transcriptome profiles |
publishDate |
2018 |
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https://hdl.handle.net/10356/89433 http://hdl.handle.net/10220/47077 |
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1725985738200186880 |