Correlation analysis reveals the emergence of coherence in the gene expression dynamics following system perturbation.

Abstract:

:Time course gene expression experiments are a popular means to infer co-expression. Many methods have been proposed to cluster genes or to build networks based on similarity measures of their expression dynamics. In this paper we apply a correlation based approach to network reconstruction to three datasets of time series gene expression following system perturbation: 1) Conditional, Tamoxifen dependent, activation of the cMyc proto-oncogene in rat fibroblast; 2) Genomic response to nutrition changes in D. melanogaster; 3) Patterns of gene activity as a consequence of ageing occurring over a life-span time series (25y-90y) sampled from T-cells of human donors. We show that the three datasets undergo similar transitions from an "uncorrelated" regime to a positively or negatively correlated one that is symptomatic of a shift from a "ground" or "basal" state to a "polarized" state. In addition, we show that a similar transition is conserved at the pathway level, and that this information can be used for the construction of "meta-networks" where it is possible to assess new relations among functionally distant sets of molecular functions.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Neretti N,Remondini D,Tatar M,Sedivy JM,Pierini M,Mazzatti D,Powell J,Franceschi C,Castellani GC

doi

10.1186/1471-2105-8-S1-S16

subject

Has Abstract

pub_date

2007-03-08 00:00:00

pages

S16

issn

1471-2105

pii

1471-2105-8-S1-S16

journal_volume

8 Suppl 1

pub_type

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