Constrained query of order-preserving submatrix in gene expression data

Abstract

Order-preserving submatrix (OPSM) has become important in modelling biologically meaningful subspace cluster, capturing the general tendency of gene expressions across a subset of conditions. With the advance of microarray and analysis techniques, big volume of gene expression datasets and OPSM mining results are produced. OPSM query can efficiently retrieve relevant OPSMs from the huge amount of OPSMdatasets. However, improvingOPSMquery relevancy remains a difficult task in real life exploratory data analysis processing. First, it is hard to capture subjective interestingness aspects, e.g., the analyst’s expectation given her/his domain knowledge. Second, when these expectations can be declaratively specified, it is still challenging to use them during the computational process of OPSM queries. With the best of our knowledge, existing methods mainly focus on batch OPSM mining, while few works involve OPSM query. To solve the above problems, the paper proposes two constrained OPSM query methods, which exploit userdefined constraints to search relevant results from two kinds of indices introduced. In this paper, extensive experiments are conducted on real datasets, and experiment results demonstrate that the multi-dimension index (cIndex) and enumerating sequence index (esIndex) based queries have better performance than brute force search.

Keywords

gene expression data / OPSM / constrained query / brute-force search / feature sequence / cIndex

References

[1] Pensa R G, Boulicaut J F. Constrained coclustering of gene expression data. In: Proceedings of the 8th SIAM International Conference on Data Mining. 2008, 25–36

[2] Alqadah F, Bader J S, Anand R, Reddy C K. Query-based biclustering using formal concept analysis. In: Proceedings of the 12th SIAM International Conference on Data Mining. 2012, 648–659

[3] Jiang T, Li Z H, Chen Q, Li K W, Wang Z, Pan W. Towards orderpreserving submatrix search and indexing. In: Proceedings of the 20th International Conference on Database Systems for Advanced Applications. 2015, 309–326

[4] Gao B J, Griffith O L, Ester M, Jones S J M. Discovering significant OPSMsubspace clusters in massive gene expression data. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, 922–928

[5] Gao B J, Griffith O L, Ester M, Xiong H, Zhao Q, Jones S J M. On the deep order-preserving submatrix problem: a best effort approach. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(2): 309–325

[6] Sim K, Gopalkrishnan V, Zimek A, Cong G. A survey on enhanced subspace clustering. Data Mining and Knowledge Discovery, 2013, 26(2): 332–397

[7] Madeira S C, Oliveira A L. Biclustering algorithms for biological data analysis: a survey. IEEE/ACMTransactions on Computational Biology and Bioinformatics, 2004, 1(1): 24–45

[8] Jiang D X, Tang C, Zhang A D. Cluster analysis for gene expression data: a survey. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(11): 1370–1386

[9] Kriegel H P, Kröger P, Zimek A. Clustering high-dimensional data: a survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery from Data, 2009, 3(1): 337–348

[10] Yue F, Sun L, Wang K Q, Wang Y J, Zuo W M. State-of-the-art of cluster analysis of gene expression data. Acta Automatica Sinica, 2008, 34(2): 113–120

[11] Zou Q, Li X B, Jiang W R, Lin Z Y, Li G L, Chen K. Survey ofMapReduce frame operation in bioinformatics. Briefings in Bioinformatics, 2014, 15(4): 637–647

[12] Zou Q, Guo M Z, Liu Y, Wang J. A classification method for classimbalanced data and its application on bioinformatics. Journal of Computer Research and Development, 2010, 47(8): 1407–1414

[13] Dhollander T, Sheng Q, Lemmens K, Moor B D, Marchal K, Moreau Y. Query-driven module discovery in microarray data. Bioinformatics, 2007, 23(19): 2573–2580

[14] Zhao H, Cloots L, Bulcke T V D, Wu Y, Smet R D, Storms V, Meysman P, Engelen K, Marchal K. Query-based biclustering of gene expression data using probabilistic relational models. BMC Bioinformatics, 2011, 12(S-1): S37

[15] Pensa R G, Robardet C, Boulicaut J F. Towards constrained coclustering in ordered 0/1 data sets. In: Proceedings of the 16th International Symposium on Foundations of Intelligent Systems. 2006, 425–434

[16] Pensa R G, Robardet C, Boulicaut J F. Constraint-driven co-clustering of 0/1 data. Constrained Clustering: Advances in Algorithms, Theory and Applications, 2008, 145–170

[17] Cheng Y, Church G M. Biclustering of expression data. In: Proceedings of the 8th International Conference on Intelligent Systems for Molecular Biology. 2000, 93–103

[18] Yang J, Wang W, Wang H, Yu P S. Delta-clusters: capturing subspace correlation in a large data set. In: Proceedings of the 18th International Conference on Data Engineering. 2002, 517–528

[19] Wang H, Wang W, Yang J, Yu P S. Clustering by pattern similarity in large data sets. In: Proceedings of the 2002 ACM SIGMOD international conference on Management of Data. 2002, 394–405

[20] Wang H, Pei J, Yu P S. Pattern-based similarity search for microarray data. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2005, 814–819

[21] Ben-Dor A, Chor B, Karp R M, Yakhini Z. Discovering local structure in gene expression data: the order-preserving submatrix problem. Journal of Computational Biology, 2003, 10(3-4): 373–384

[22] Liu J, Wang W. OP-cluster: clustering by tendency in high dimensional space. In: Proceedings of the 3rd IEEE International Conference on Data Mining. 2003, 187–194

[23] Zhao Y, Yu J X, Wang G, Chen L, Wang B, Yu G. Maximal subspace coregulated gene clustering. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(1): 83–98

[24] Kriegel H P, Kröger P, Renz M, Wurst S H R. A generic framework for efficient subspace clustering of high-dimensional data. In: Proceedings of the 5th IEEE International Conference on Data Mining. 2005, 250–257

[25] An P. Research on biclustering methods for gene expression data analysis. Disseration for the Master Degree. Suzhou: Soochow University, 2013

[26] Jiang T, Li Z H, Chen Q, Wang Z, Pan W, Wang Z. Parallel partitioning and mining gene expression data with butterfly network. In: Proceedings of the 24th International Conference on Database and Expert Systems Applications. 2013, 129–144

[27] Jiang T, Li Z H, Chen Q, Wang Z, Li K, Pan W. OMEGA: an orderpreserving submatrix mining, indexing and search tool. In: Proceedings of the European Conference onMachine Learning and Knowledge Discovery in Databases. 2015, 303–307

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg