Using matrix of thresholding partial correlation coefficients to infer regulatory network

DNA arrays measure the expression levels for thousands of genes simultaneously under different conditions. These measurements reflect many aspects of the underlying biological processes. A method based on the matrix of thresholding partial correlation coefficients (MTPCC) is proposed for network inference from expression profiles. It includes three main parts: (1) hierarchical cluster analysis, (2) cluster boundaries establishment, and (3) regulatory network inference. The method was applied to the expression data of 2467 genes in Saccharomyces cerevisiae measured under 79 different conditions [Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D., 1998. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 95, 14863–14868]. Using hierarchical clustering and cluster boundaries establishment, the 2467 genes were grouped into 12 clusters. The expression profiles of each cluster were expressed as a set of expression levels average over the cluster that constituted genes of each condition. Then the expression data of these clusters were subjected to the analysis of partial correlation, and the significance of each element in the obtained partial correlation coefficient matrix (PCCM) was examined by a permutation test. The corresponding undirected dependency graph (UDG) was obtained as a model of the regulatory network of S. cerevisiae. The veracity of the network was evidenced by the consistency of our results with the collected results from experimental studies.