C16_netw_infer_hando - Network inference from dynamic(state information Input components states of components(in time Hypotheses regulatory

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1 Network inference from dynamic (state) information Input: components; states of components (in time) Hypotheses: regulatory framework Output: proposed regulatory network Validation: capture known interactions For inference of gene regulatory networks, the most frequently used state information comes from gene expression arrays (microarrays) There are several microarray types and methods, for our purposes it suffices to say that a microarray provides a readout of the relative or (semi)absolute expression level of each gene in the array. Inference methods • Need expression snapshots: – Clustering analysis – Bayesian networks • Need expression timecourse: Need expression timecourse: – Continuous – Differential equations – Discrete - Boolean • Need other types of information: – Data mining Clustering analysis Pairwise correlation of expression levels of two genes across time or conditions, e.g. by Pearson correlation or Euclidean distance These correlations are then clustered – Hierarchical clustering - forms a dendogram • Successive clusters are formed by aggregation of existing clusters. • Difficult to decide cut-off of similarity – K-clustering • K - predetermined number of groups • Decide criteria to group the genes – e.g. Group together genes with similar correlation coefficient. • Often used as an exploratory data analysis tool • More computationally intensive than hierarchical clustering but optimization can be performed. Drawback: No insight into the causal relationship Example of clustering analysis Data set – yeast cell cycle expression, expression ratios •S c o r e m a t r i x M i,j – matrix of similarities between expression ratio of each pair of genes. Two aggregate matrices Time delay Qian et al . (2001) J Mol. Bio, 314, 1053-1066 Inverted The central idea is to find the maximal aggregated score Max(E) off-diagonal – time shifted relationship Max(D) diagonal – inverted relationship
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2 Network is constructed using clustering methods Time delayed expression Simultaneous Qian et al . (2001) JMB, 314, 1053-1066 expression Inverted relationships in the expression profiles. Oncogenic signaling network • Constructed weighted gene co-expression network based on pairwise Pearson correlations • Hierarchical clustering to detect groups or modules of co-expressed genes.
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C16_netw_infer_hando - Network inference from dynamic(state information Input components states of components(in time Hypotheses regulatory

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