A System for Enhancing Genome-Wide Coexpression Dynamics Study
A system for enhancing genome-wide coexpression dynamics study Ker-Chau Li†‡, Ching-Ti Liu, Wei Sun, Shinsheng Yuan†, and Tianwei Yu Department of Statistics, 8125 Mathematical Sciences Building, University of California, Los Angeles, CA 90095-1554 Edited by Michael S. Waterman, University of Southern California, Los Angeles, CA, and approved August 30, 2004 (received for review April 28, 2004) Statistical similarity analysis has been instrumental in elucidation mRNA level. Yet a third possibility can be described in terms of LA. of the voluminous microarray data. Genes with correlated expres- This more advanced concept of statistical association originates sion profiles tend to be functionally associated. However, the from the need to describe a situation as schematized in Fig. 1 Left, majority of functionally associated genes turn out to be uncorre- wherein two opposing trends between X and Y are displayed. The lated. One conceivable reason is that the expression of a gene can positive and negative correlations cancel each other out, rendering be sensitively dependent on the often-varying cellular state. The the overall correlation insignificant. It would be valuable to learn intrinsic state change has to be plastically accommodated by why and how the change of trend occurs. But for real data, such gene-regulatory mechanisms. To capture such dynamic coexpres- hidden trends are not easy to detect directly from the scatterplot of sion between genes, a concept termed ‘‘liquid association’’ (LA) has X and Y. To alleviate the difficulty, we look for additional variables been introduced recently. LA offers a scoring system to guide a that may be associated with the change of the trend.
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