MultiDCoX: Multi-factor analysis of differential co-expression
Background: Differential co-expression DCX signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expr...
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sg-ntu-dr.10356-892152020-03-07T11:48:59Z MultiDCoX: Multi-factor analysis of differential co-expression Liany, Herty Rajapakse, Jagath Chandana Karuturi, R. Krishna Murthy School of Computer Science and Engineering Differential Co-expression Gene Expression Background: Differential co-expression DCX signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression. Results: We developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially co-expressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression. Conclusions: MultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression. Published version 2018-05-17T05:39:09Z 2019-12-06T17:20:24Z 2018-05-17T05:39:09Z 2019-12-06T17:20:24Z 2017 Journal Article Liany, H., Rajapakse, J. C., & Karuturi, R. K. M. (2017). MultiDCoX: Multi-factor analysis of differential co-expression. BMC Bioinformatics, 18(S16), 111-124. https://hdl.handle.net/10356/89215 http://hdl.handle.net/10220/44821 10.1186/s12859-017-1963-7 en BMC Bioinformatics © 2017 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. 14 p. application/pdf |
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Differential Co-expression Gene Expression Liany, Herty Rajapakse, Jagath Chandana Karuturi, R. Krishna Murthy MultiDCoX: Multi-factor analysis of differential co-expression |
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Background: Differential co-expression DCX signifies change in degree of co-expression of a set of genes among different biological conditions. It has been used to identify differential co-expression networks or interactomes. Many algorithms have been developed for single-factor differential co-expression analysis and applied in a variety of studies. However, in many studies, the samples are characterized by multiple factors such as genetic markers, clinical variables and treatments. No algorithm or methodology is available for multi-factor analysis of differential co-expression. Results: We developed a novel formulation and a computationally efficient greedy search algorithm called MultiDCoX to perform multi-factor differential co-expression analysis. Simulated data analysis demonstrates that the algorithm can effectively elicit differentially co-expressed (DCX) gene sets and quantify the influence of each factor on co-expression. MultiDCoX analysis of a breast cancer dataset identified interesting biologically meaningful differentially co-expressed (DCX) gene sets along with genetic and clinical factors that influenced the respective differential co-expression. Conclusions: MultiDCoX is a space and time efficient procedure to identify differentially co-expressed gene sets and successfully identify influence of individual factors on differential co-expression. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Liany, Herty Rajapakse, Jagath Chandana Karuturi, R. Krishna Murthy |
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Article |
author |
Liany, Herty Rajapakse, Jagath Chandana Karuturi, R. Krishna Murthy |
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Liany, Herty |
title |
MultiDCoX: Multi-factor analysis of differential co-expression |
title_short |
MultiDCoX: Multi-factor analysis of differential co-expression |
title_full |
MultiDCoX: Multi-factor analysis of differential co-expression |
title_fullStr |
MultiDCoX: Multi-factor analysis of differential co-expression |
title_full_unstemmed |
MultiDCoX: Multi-factor analysis of differential co-expression |
title_sort |
multidcox: multi-factor analysis of differential co-expression |
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2018 |
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https://hdl.handle.net/10356/89215 http://hdl.handle.net/10220/44821 |
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1681049832505999360 |