title: DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.
abstract: 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 .
authors.affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. tlui27@yahoo.com.
authors.forename: Thomas W H
authors.initials: TW
authors.lastname: Lui
authors.affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. nancytsui.cuhk@gmail.com.
authors.forename: Nancy B Y
authors.initials: NB
authors.lastname: Tsui
authors.affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. wing.chi.chan@polyu.edu.hk.
authors.forename: Lawrence W C
authors.initials: LW
authors.lastname: Chan
authors.affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. cesar.wong@polyu.edu.hk.
authors.forename: Cesar S C
authors.initials: CS
authors.lastname: Wong
authors.affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. parco.siu@polyu.edu.hk.
authors.forename: Parco M F
authors.initials: PM
authors.lastname: Siu
authors.affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. ben.yung@polyu.edu.hk.
authors.forename: Benjamin Y M
authors.initials: BY
authors.lastname: Yung
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism*; D019295:Computational Biology / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D060066:Protein Interaction Maps; D012984:Software