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Detecting active subnetworks in molecular interaction networks with missing data Luke Hunter Texas A&M University SHURP 2007 Student

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Outline of Talk Introduction Overall Strategy Previous Papers Graph Construction Scoring Function Search Approaches Experiments Future Work

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Introduction Background: Ideker et al. define an ‘active subnetwork’ as a connected set of genes with unexpectedly high levels of differential expression Objective: Find active subnetworks of metabolites Motivation: High throughput data analysis Mechanisms Cell state (disease, drug treatment, and environment)

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Overall Strategy 1) Build graph 2) Obtain data (p-values) 3) Create scoring function 4) Find high-scoring subsets 5) Validate results

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Previous Papers (1): Ideker et al. (2002) “Discovering regulatory and signalling circuits in molecular interaction networks” Goal: find active subnetworks Graph Galactose utilization (~300 nodes, ~300 links) P-P & P-DNA for yeast (~4000 nodes, ~7500 links) Data from perturbations of GAL pathway Scoring Aggregate z-score & calibration (more later) Scoring over multiple conditions Searching Simulated Annealing Results Don’t contradict literature Breaks up / organizes data

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Previous Papers (2): Rajagopalan & Agarwal (2004) Goal: maximally include query list in minimal subset Graph Gathered data from 3 sources (~9000 nodes, ~30,000 links) Scoring Used aggregate z-score & calibration (from Ideker, 2002) Modified to consider node degree and node significance Searching Greedy Algorithm with DFS Results Experiments are not convincing “Inferring pathways from gene lists using a literature-derived _network of biological relationships”

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Graph Construction KEGG Data (Kanehisa et al.) Nodes: ligands (i.e.--compounds, glycans, & drugs; ~25,000) Links: reactions (~29,000) Measured Data Chronic ischemia (304 ligands) Glucose tolerance (124 ligands) Planned myocardial infarction (107 ligands) Problems with measured data Ambiguity Not in KEGG Duplicates

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Scoring Functions (1) Naïve Ideker et al. (2002)Whitlock (2005) Rajagopalan & Agarwal (2004) Use aggregate z-score of Ideker Create “corrected” node score Modify for node significance Modify for node degree Discrepancy with Ideker paper

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Scoring Functions (2) Significance vs. Strength Geometric MeanPiecewise FunctionWeighted Geometric Mean

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Scoring Functions (3) Establish Significance of Scores 1) Scramble 2) Search 3) Obtain distribution

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Search Approaches (1): Simulated Annealing Ideker et al. 2002

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Search Approaches (2): Greedy Algorithm w/ DFS 1)Build graph and calculate corrected node scores 2)Use BFS to group nodes with positive corrected scores 3)For each connected component do a limited DFS and try to merge with nearby connected components if merge would increase the overall score 4)Prune nodes with small z-scores (so long as connectivity is maintained)

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Algorithm Test

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Future Goals Remove “distant” unknown nodes? Evaluate scoring functions Evaluate search strategies Implement Google MapReduce Apply to more data sets Use cytoscape software

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Acknowledgements NSF REU Program Fritz Gabriel Everyone else

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References Ideker, T., Ozier, O., Schwikowski, B., and Siegel, A.F Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18: S233–S240. Rajagopalan, D., & Agarwal, P. (2005). Inferring pathways from gene lists using a literature-derived network of biological relationships. Bioinformatics 21, 788– 793. Whitlock, M. (2005). Combining probability from independent tests: the weighted Z-method is superior to Fisher’s approach. J. Evol. Biol. 16, Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K.F., Itoh, M., Kawashima, S., Katayama, T., Araki, M., and Hirakawa, M.; From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 34, D (2006). Dean, J., & Ghemawat, S. (2004). MapReduce: Simplified Data Processing on Large Clusters. OSDI 2004.

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