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DECODE: an integrated differential co-expression and differential expression analysis of gene expression data

BACKGROUND: Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored. RESULTS: In this study, we proposed a novel...

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Autores principales: Lui, Thomas WH, Tsui, Nancy BY, Chan, Lawrence WC, Wong, Cesar SC, Siu, Parco MF, Yung, Benjamin YM
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4449974/
https://www.ncbi.nlm.nih.gov/pubmed/26026612
http://dx.doi.org/10.1186/s12859-015-0582-4
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author Lui, Thomas WH
Tsui, Nancy BY
Chan, Lawrence WC
Wong, Cesar SC
Siu, Parco MF
Yung, Benjamin YM
author_facet Lui, Thomas WH
Tsui, Nancy BY
Chan, Lawrence WC
Wong, Cesar SC
Siu, Parco MF
Yung, Benjamin YM
author_sort Lui, Thomas WH
collection PubMed
description BACKGROUND: Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored. RESULTS: In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism. CONCLUSIONS: By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone. DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0582-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-44499742015-06-01 DECODE: an integrated differential co-expression and differential expression analysis of gene expression data Lui, Thomas WH Tsui, Nancy BY Chan, Lawrence WC Wong, Cesar SC Siu, Parco MF Yung, Benjamin YM BMC Bioinformatics Methodology Article BACKGROUND: Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored. RESULTS: In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism. CONCLUSIONS: By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone. DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-015-0582-4) contains supplementary material, which is available to authorized users. BioMed Central 2015-05-31 /pmc/articles/PMC4449974/ /pubmed/26026612 http://dx.doi.org/10.1186/s12859-015-0582-4 Text en © Lui et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.
spellingShingle Methodology Article
Lui, Thomas WH
Tsui, Nancy BY
Chan, Lawrence WC
Wong, Cesar SC
Siu, Parco MF
Yung, Benjamin YM
DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
title DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
title_full DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
title_fullStr DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
title_full_unstemmed DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
title_short DECODE: an integrated differential co-expression and differential expression analysis of gene expression data
title_sort decode: an integrated differential co-expression and differential expression analysis of gene expression data
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4449974/
https://www.ncbi.nlm.nih.gov/pubmed/26026612
http://dx.doi.org/10.1186/s12859-015-0582-4
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