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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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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. |
format | Online Article Text |
id | pubmed-4449974 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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