Class-specific correlations of gene expressions: identification and their effects on clustering analyses.

Current microarray studies primarily focus on identifying individual genes with differential expression levels across different conditions or classes. A potential problem is that they may disregard multidimensional information hidden in gene interactions. In this study, we propose an approach to detect gene interactions related to study phenotypes through identifying gene pairs with correlations that appear to be class or condition specific. In addition, we explore the effects of ignoring class-specific correlations (CSC) on correlation-based gene-clustering analyses. Our simulation studies show that ignoring CSC can significantly decrease the accuracy of gene clustering and increase the dissimilarity within clusters. Our results from a DLBCL (distinct types of diffuse large B cell lymphoma) data set illustrate that CSC are clearly present and have great adverse effects on gene-clustering results if ignored. Meanwhile, interesting biological interpretations may be derived from studying gene pairs with CSC. This study demonstrates that our algorithm is simple and computationally efficient and has the ability to detect gene pairs with CSC that are informative for uncovering interesting regulation patterns.