Journal
EUROPEAN PHYSICAL JOURNAL B
Volume 50, Issue 1-2, Pages 255-258Publisher
SPRINGER
DOI: 10.1140/epjb/e2006-00103-4
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We analyze gene expression time-series data of yeast (S. cerevisiae) measured along two full cell-cycles. We quantify these data by using q-exponentials, gene expression ranking and a temporal mean-variance analysis. We construct gene interaction networks based on correlation coefficients and study the formation of the corresponding giant components and minimum spanning trees. By coloring genes according to their cell function we find functional clusters in the correlation networks and functional branches in the associated trees. Our results suggest that a percolation point of functional clusters can be identified on these gene expression correlation networks.
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