Microarray researchers need easy-to-use tools to identify differences in the coexpression and coregulation of genes between phenotypes that cannot be identified with traditional tools. Often researchers compute Student's t-tests, analysis of variance (ANOVA), significance analysis of microarrays [1
] or empirical Bayes analysis [2
] for each gene on their microarray to identify individual differentially expressed genes (DEGs) among two or more phenotypes [3
]. Unfortunately, these approaches ignore coexpression because they cannot account for the complex multivariate relationships among genes. Multivariate statistical methods like hierarchical clustering and principle components analysis (PCA) are often used for quality control and exploration of microarray data. However, these multivariate methods do not effectively model coexpression nor do they allow for hypothesis tests to compare phenotypes. Gene-gene association networks built using ARACNe [4
], context likelihood relatedness (CLR) [5
], maximum relevancy (MR) [6
] and other methods often provide helpful models of coexpression and coregulation, but the networks are based on data from a single phenotype and are not easily compared using statistical tests. New methods are needed to account for the complex relationships among genes while providing hypothesis tests to compare phenotypes.
Several research groups have addressed the question of comparing the coexpression of specific gene-gene pairs or coexpression networks among two or more phenotypes. Two early examples used search algorithms to identify optimally sized clusters of coexpressed genes and resampling tests to identify significant differences among the coexpressed clusters between phenotypes [8
]. Other published methods used variations on familiar statistical techniques like Fisher's Z tests or modified F-statistics to directly compare pairwise gene-gene correlations between two phenotypes [10
]. Some of these methods [10
] are readily available as source scripts of package libraries in R http://www.r-project.org
. Some interesting approaches apply the results from statistical tests that compare pairwise gene-gene associations between two phenotypes to the construction and interpretation of gene coexpression networks [10
]. Both of these methods allow researchers to explore the complex differences among gene expression networks using statistical tests, but unfortunately neither method has been implemented in a user-friendly tool.
DAPfinder and DAPview are plug-ins for BRB-ArrayTools http://linus.nci.nih.gov/BRB-ArrayTools.html
, which will provide researchers with accessible tools to test differences in the coexpression between two phenotypes and explore those results on gene association networks. BRB-ArrayTools is a comprehensive microarray analysis package that does not require specific skills in programming or direct script usage. It is available for free to non-commercial users and has more than 11,000 users in 65 countries [15
]. Our DAPfinder and DAPview tools will identify and visualize individual significant differences in gene-gene association between the two classes, each of which we will call a Differentially Associated Pair (DAP). Output from these tools can be used to construct gene-gene association networks and identify the significant differences in coexpression between two groups. Our hope is that these tools can be used to identify systems-level features in the gene-gene association networks like network growth or decay, network merging or splitting, and network birth or death, reflecting functional changes in biological pathways.