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A new type of stochastic dependence revealed in gene expression data.

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Despite their multiple possibilities many of the high throughput technologies continue to be used in order to answer the question of differential expression. Further analysis tend to include additional information like GO or follow up experiments. The paper I’ll present proposes the existence of a characteristic dependency between some genes. More than an answer to current problems in microarray and other omic fields this work illustrates how new types of questions can be formulated about overall “rules” in gene expression. During the session I will also show briefly part of my own work along similar (but not identical) lines.

Paper abstract: Modern methods of microarray data analysis are biased towards selecting those genes that display the most pronounced differential expression. The magnitude of differential expression does not necessarily indicate biological significance and other criteria are needed to supplement the information on differential expression. Three large sets of microarray data on childhood leukemia were analyzed by an original method introduced in this paper. A new type of stochastic dependence between expression levels in gene pairs was deciphered by our analysis. This modulation-like unidirectional dependence between expression signals arises when the expression of a “gene-modulator’’ is stochastically proportional to that of a “gene-driver’’. A total of more than 35% of all pairs formed from 12550 genes were conservatively estimated to belong to this type. There are genes that tend to form Type A relationships with the overwhelming majority of genes. However, this picture is not static: the composition of Type A gene pairs may undergo dramatic changes when comparing two phenotypes. The ability to identify genes that act as ;;modulators’’ provides a potential strategy of prioritizing candidate genes.

Paper details: Stat Appl Genet Mol Biol. 2006;5:Article7. http://www.bepress.com/sagmb/vol5/iss1/art7/

This talk is part of the Bioinformatics jounal club for the -omics series.

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