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Storytelling and Clustering for Cellular Signaling Pathways

This study focuses on discovering relationships between cellular signaling pathways through graph structure and clustering. It introduces a novel approach combining subgraph discovery, hierarchical clustering, and storytelling to understand and visualize complex pathway interactions.

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Storytelling and Clustering for Cellular Signaling Pathways

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  1. Storytelling and Clustering for Cellular Signaling Pathways M. Shahriar Hossain, Monika Akbar, Nicholas F. Polys Department of Computer Science, Virginia Tech, Blacksburg, VA 24061.

  2. Objective • STKE Dataset • Cell interactions through chemical signals • Discover relationships between the pathways • Graph structure • Subgraph discovery problem • Pathways relationships • Clustering • Storytelling

  3. Myocyte Adrenergic Pathway (CMP_9043)

  4. Dataset properties

  5. Design Pipeline Pathway Graphs STKE Dataset Preprocessor Frequent Subgraphs Frequent Subgraph Discovery Clustering NN Storytelling

  6. Subsequent Candidate Generation l q q l p p p m o m o m o n n n • Apriori – incremental approach [17] • FSG [2] • Generate a (k+1)-edge candidate subgraph by combining two k-edge subgraphs where these two k-edge subgraphs have a common core subgraphof (k-1)-edges. • Cost of comparison between subgraphs (and core subgraphs) is reduced using hash-code of each subgraph object.

  7. Subsequent Candidate Generation q l p q l p p o m o o m n m n n z t r o l l l m p p p n p o o o m m m o m n n n r n l p o m n l p p s s o o m m n n • Instance: • Number of 5-edge subgraphs: 21 • Core subgraph comparisons for s1: 20 Not generated …………………………………………. ………………………………................ ………………………………………….

  8. Master Pathway Graph (MPG) Total Unique Nodes:1205 Total Relations:1376

  9. SEG - Subgraph Extension Generation q l p n m m o s o n p l l q r p r l m p o m o n n l p s m o n • Neighborhood Extension • Neighborhood list : {q, r, s} • Comparison is not required. • Subgraph is extended from physical evidence

  10. Design Pipeline Pathway Graphs STKE Dataset Preprocessor Frequent Subgraphs Frequent Subgraph Discovery Clustering NN Storytelling

  11. Subgraph Discovery • What so novel about pruning edges? min_sup=2%

  12. ‘Importance Factor’ of a subgraph: sfipf • For i-th subgraph j-th pathway: Subgraph frequency, Inverse pathway frequency,

  13. Dataset Properties (sfipf) Number of edges in MPG=1376 Total pathways=50

  14. Subgraph Discovery

  15. Subgraph Discovery

  16. Subgraph Discovery Overall attempts saved = 89.52% Overall time saved = 99.39%

  17. Clustering • Hierarchical Agglomerative Clustering (HAC) • k-means • Unsupervised measure of clusters’ validity • Average Silhouette Coefficient (ASC) [19]

  18. Clustering

  19. Clustering

  20. Design Pipeline Pathway Graphs STKE Dataset Preprocessor Frequent Subgraphs Frequent Subgraph Discovery Clustering NN Storytelling

  21. Pathway Relations (StoryTelling) p1 p7 p2 p8 S T p3 p9 • Bidirectional Search • Cover tree for NN

  22. Day-to-day life example From Roman Holiday From Terminator 3 From: Roman Holiday To: Terminator 3

  23. Examples in STKE • http://people.cs.vt.edu/msh/infoviz/3/

  24. Pathway Relations (StoryTelling)

  25. Pathway Relations (StoryTelling)

  26. Pathway Relations (StoryTelling)

  27. Future Directions • Compare our SEG graph methods with text based clustering and storytelling • Examine costs and benefits for combining text and graph mining techniques

  28. References [1] Science Signaling, The signal Transduction Knowledge Environment (STKE), "The Database of Cell Signaling", http://stke.sciencemag.org/cm/ [2] Kuramochi, M. and Karypis, G., "An efficient algorithm for discovering frequent subgraphs", IEEE Transactions on KDE, Vol. 16(9), September 2004, pp. 1038-1051. [3] Breslin, T., Krogh, M., Peterson, C., and Troein, C., "Signal transduction pathway profiling of individual tumor samples", BMC Bioinformatics, June 29, 2005. [4] Kumar, D., Ramakrishnan, N., Helm, R. F., and Potts, M., "Algorithms for Storytelling", IEEE Transactions on KDE, Vol. 20(6), June 2008, pp. 736-751. [5] Ratprasartporn, N., Cakmak, A., and Ozsoyoglu, G., "On Data and Visualization Models for Signaling Pathways", 18th SSDBM, 2006, pp. 133-142. [6] Xu, X., and Yu, Y., "Modeling and Verifying WNT Signaling Pathway", 3rd Intl. Conf. on ICNC. 2007, Vol. 2, pp. 319 - 323. [7] Schreiber, F., "Comparison of metabolic pathways using constraint graph drawing", 1st Asia-Pacific bioinformatics Conf. on Bioinfo., Australia, Vol. 19, 2003, pp. 105 - 110. [8] Abello, J., van Ham, F., and Krishnan, N., "ASKGraphView: A Large Scale Graph Visualization System", IEEE Transactions on Visualization and Computer Graphics, Vol. 12(5), 2006, pp. 669 - 676. [9] Miyake, S., Tohsato, A., Takenaka, Y., and Matsuda, H. "A clustering method for comparative analysis between genomes and pathways", 8th Intl. Conf. on Database Systems for Advanced Applications, March 2003 pp. 327 - 334.

  29. References [10] Yan, X., and Han, J. "gSpan: graph-based substructure pattern mining", IEEE ICDM, 2002, pp. 721-724. [11] Moti, C., and Ehud, G. "Diagonally Subgraphs Pattern Mining", 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, 2004, pp. 51-58. [12] Ketkar, N., Holder, L., Cook, D., Shah, R., and Coble, J. "Subdue: Compression-based Frequent Pattern Discovery in Graph Data", ACM KDD Workshop on Open-Source Data Mining, August 2005, pp. 71-76. [13] Zhang, T., Ramakrishnan, R., and Livny, M., "BIRCH: An Efficient Data Clustering Method for Very Large Databases", ACM SIGMOD Intl. Conf. on Management of Data, Canada, 1996, pp. 103-114. [14] Wagsta, K., Cardie, C., Rogers, S., and Schroedl, S., "Constrained K-means Clustering with Background Knowledge", ICML 2001, pp. 577-584. [15] Lin, F., and Hsueh, C. M., "Knowledge map creation and maintenance for virtual communities of practice", Intl. Journal of Information Processing and Management, ACM, Vol. 42(2), 2006, pp. 551-568. [16] Beygelzimer, A., Kakade, S., Langford, J., "Cover trees for nearest neighbor", ICML 2006, pp. 97-104. [17] Agrawal, R., and Srikant, R. "Fast Algorithms for Mining Association Rules", Intl. Conf. on Very Large Data Bases, Santiago, Chile, September 1994, pp. 487-499. [18] Agrawal, R., Mehta, M., Shafer, J., Srikant, R., Arning, A. and Bollinger, T. "The Quest Data Mining System", KDD'96, USA, 1996, pp. 244-249. [19] Tan, P. N., Steinbachm, M., and Kumar, V., "Introduction to Data Mining", Addison-Wesley, ISBN: 0321321367, April 2005, pp. 539-547. [20] http://people.cs.vt.edu/amonika/infoviz/

  30. Thank You

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