Computational Diagnostics Group compdiag MPI for Molecular Genetics

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Dept. Vingron
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NGFN Microarray Data Analysis Resource

Differential Co-expression:

Finding disease specific alterations in the coexpression of genes

Dennis Kostka

Gene expression is a tightly regulated process, crucial for the proper functioning of a cell. In microarray data, coregulation is reflected by strong correlations between expression levels. Molecular disease mechanisms typically constitute abnormalities in the coregulation of genes. Resulting changes in expression profiles help identifying disease related genes and in several cases facilitate improved diagnosis even prognosis of disease outcome. Alteration of gene regulation often results in up or down regulated genes. Common analysis strategies look for these differentially expressed genes or for genes which play an important role in some supervised learning algorithm.

We use a complementary approach. Not all changes in coregulation are manifested by up or down regulation of individual genes. The disruption of a regulatory mechanism due to a disease might well lead to changes the genes interplay and therefore affect the covariance structure of their expression. This leads to differential co-expression and is illustrated in the following:

Different types of structure in microarray data. The plots show simulated expression values for two prototypical situations: The plot on the left depicts a group of differentially expressed genes, whereas the genes in the right plot are differentially co-expressed.

Both situations lead to characteristic but distinct patterns. We present an algorithm that finds such groups of differentially co-expressed genes making them available for further investigation.



The software corresponding to the publication is available as C sourcecode an R package:
DCOEX - finding groups of differentially coexpressed genes

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