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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| Format: | Journal Article |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/89215 http://hdl.handle.net/10220/44821 |
| _version_ | 1824456957489053696 |
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| author | Liany, Herty Rajapakse, Jagath Chandana Karuturi, R. Krishna Murthy |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Liany, Herty Rajapakse, Jagath Chandana Karuturi, R. Krishna Murthy |
| author_sort | Liany, Herty |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T04:02:21Z |
| format | Journal Article |
| id | ntu-10356/89215 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T04:02:21Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Differential Co-expression Gene Expression Liany, Herty Rajapakse, Jagath Chandana Karuturi, R. Krishna Murthy MultiDCoX: Multi-factor analysis of differential co-expression |
| title | 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_short | MultiDCoX: Multi-factor analysis of differential co-expression |
| title_sort | multidcox multi factor analysis of differential co expression |
| topic | Differential Co-expression Gene Expression |
| url | https://hdl.handle.net/10356/89215 http://hdl.handle.net/10220/44821 |
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