DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

BackgroundLarge microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns.ResultsWe present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset.ConclusionsDiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.

[1]  Lucinda K. Southworth,et al.  Aging Mice Show a Decreasing Correlation of Gene Expression within Genetic Modules , 2009, PLoS genetics.

[2]  Rainer Breitling,et al.  Generalizing Genetical Genomics: Getting Added Value from Environmental Perturbation Concluding Remarks and Future Perspectives , 2022 .

[3]  A. G. de la Fuente.  From 'differential expression' to 'differential networking' - identification of dysfunctional regulatory networks in diseases. , 2010, Trends in genetics : TIG.

[4]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[5]  S. Horvath,et al.  Weighted gene coexpression network analysis strategies applied to mouse weight , 2007, Mammalian Genome.

[6]  Liang Chen,et al.  A statistical method for identifying differential gene-gene co-expression patterns , 2004, Bioinform..

[7]  S. Horvath,et al.  Statistical Applications in Genetics and Molecular Biology , 2011 .

[8]  Heidrun Ellinger-Ziegelbauer,et al.  Carcinogen-specific gene expression profiles in short-term treated Eker and wild-type rats indicative of pathways involved in renal tumorigenesis. , 2007, Cancer research.

[9]  Raya Khanin,et al.  How Scale-Free Are Biological Networks , 2006, J. Comput. Biol..

[10]  Christina Kendziorski,et al.  Statistical methods for gene set co-expression analysis , 2009, Bioinform..

[11]  Ju Han Kim,et al.  Identifying set-wise differential co-expression in gene expression microarray data , 2009, BMC Bioinformatics.

[12]  Michael Watson,et al.  CoXpress: differential co-expression in gene expression data , 2006, BMC Bioinformatics.

[13]  S. Horvath,et al.  Conservation and evolution of gene coexpression networks in human and chimpanzee brains , 2006, Proceedings of the National Academy of Sciences.

[14]  D. Botstein,et al.  The transcriptional program of sporulation in budding yeast. , 1998, Science.

[15]  S. Bergmann,et al.  Comparative Gene Expression Analysis by a Differential Clustering Approach: Application to the Candida albicans Transcription Program , 2005, PLoS genetics.

[16]  Eric E Schadt,et al.  Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks. , 2009, Endocrinology.

[17]  Bin Zhang,et al.  Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R , 2008, Bioinform..

[18]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[19]  Jingyuan Fu,et al.  Defining gene and QTL networks. , 2009, Current opinion in plant biology.

[20]  Terence P. Speed,et al.  A comparison of normalization methods for high density oligonucleotide array data based on variance and bias , 2003, Bioinform..

[21]  E. Schadt.  Molecular networks as sensors and drivers of common human diseases , 2009, Nature.

[22]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[23]  Christina Backes,et al.  GeneTrail—advanced gene set enrichment analysis , 2007, Nucleic Acids Res..

[24]  Sangsoo Kim,et al.  Gene expression Differential coexpression analysis using microarray data and its application to human cancer , 2005 .