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Association Pattern Analysis – Applications in Bioinformatics Vipin Kumar University of Minnesota Team Members:

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Presentation on theme: "Association Pattern Analysis – Applications in Bioinformatics Vipin Kumar University of Minnesota Team Members:"— Presentation transcript:

1 Association Pattern Analysis – Applications in Bioinformatics Vipin Kumar University of Minnesota Team Members: Michael Steinbach, Rohit Gupta, Blayne Field, Meenal Chhabra, Beth Zirbes Work done in collaboration with Hui Xiong, X. He, Chris Ding, Ya Zhang, Stephen R. Holbrook Research supported by NSF, IBM

2 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 2 Data Mining for Bioinformatics  Recent technological advances are helping to generate large amounts of both medical and genomic data High-throughput experiments/techniques -Gene and protein sequences -Gene-expression data -Biological networks and phylogenetic profiles Electronic Medical Records -IBM-Mayo clinic partnership has created a DB of 5 million patients -Single Nucleotides Polymorphisms (SNPs)  Data mining offers potential solution for analysis of large-scale data Automated analysis of patients history for customized treatment Prediction of the functions of anonymous genes Identification of putative binding sites in protein structures for drugs/chemicals discovery Protein Interaction Network

3 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 3 Association Analysis Association analysis: Analyzes relationships among items (attributes) in a binary transaction data –Example data: market basket data –Applications in business and science Identification of functional modules from protein complexes Marketing and Sales Promotion Two types of patterns –Itemsets: Collection of items Example: {Milk, Diaper} –Association Rules: X  Y, where X and Y are itemsets. Example: Milk  Diaper Set-Based Representation of Data

4 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 4 I. Identification of Protein Function Modules  Proteins usually do not function in isolation. They interact with other proteins either in pairs or as components of large complexes  Protein complexes can be determined using large scale experimental studies  Functional module is a group of proteins that is involved in common elementary biological function  Association analysis techniques can be used for identification of functional modules from a collection of protein complexes Protein ComplexesProteins c1p1, p2 c2p1, p3, p4, p5 c3p2, p3, p4, p6 Protein Complex Data

5 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 5 II. Personalized Medicine Given: Patient data set that records –Phenotypic characteristics –Genetic characteristics (SNPs) –Disease Objective: Find relationships between disease and medical and genomic characteristics Association analysis can be used to find groups of phenotypic and genetic characteristics that are highly associated with disease

6 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 6 III. Protein Function Prediction Using Phylogenetic Profiles Phylogenetic profiles: –For a given protein, BLAST its sequence against N sequenced genomes –Construct a vector with N coordinates s.t. if a protein has a homolog in the organism n, set coordinate n to 1, Otherwise set it to 0 Basic Idea: If two proteins, P1 and P2 function/interact together, they must co-evolve. So every organism that has a homolog of P1 must also have a homolog of P2 Association techniques can be used to identify the protein groups and the functional linkages among them with the help of phylogenetic profiles

7 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 7  Process of finding interesting patterns: Find frequent itemsets using a support threshold Find association rules for frequent itemsets Sort association rules according to confidence  Support filtering is necessary To eliminate spurious patterns To avoid exponential search -Support has anti-monotone property: X  Y implies  ( Y ) ≤  ( X )  Confidence is used because of its interpretation as conditional probability Association Analysis Given d items, there are 2 d possible candidate itemsets

8 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 8 Drawback of Confidence Coffee Tea15520 Tea Association Rule: Tea  Coffee Confidence= P(Coffee|Tea) = 0.75 but P(Coffee) = 0.9  Although confidence is high, rule is misleading  P(Coffee|Tea) = Ref: Brin, Motwani

9 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 9 There are lots of measures proposed in the literature

10 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 10 Comparing Different Measures 10 examples of contingency tables: Rankings of contingency tables using various measures [4] Tan et al:

11 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 11 h-Confidence h-confidence(i 1,i 2,..,i k ): Advantages of h-confidence: 1.High h-confidence implies tight coupling amongst all items in the pattern 2.Eliminate cross-support patterns such as {caviar,milk} 3.Min function has anti-monotone property low support, high h-confidence patterns can be discovered efficiently milk caviar [5,6] Xiong et al

12 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 12 Protein Complex Data Protein Complexes (Higher-order Functions) Functional Modules (Elementary Functions)  The TAP-MS dataset by Gavin et al 2002: Tandem affinity purification (TAP) – mass spectrometry (MS)  Contains 232 multi-protein complexes formed using over 1300 proteins Protein ComplexesProteins c1p1, p2 c2p1, p3, p4, p5 c3p2, p3, p4, p6

13 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 13 Hyperclique Patterns from Protein Complex Data 2 Tif4632 Tif Cdc33 Snp1 2 YHR020W Mir1 2 Cka1 Ckb1 2 Ckb2 Cka2 2 Cop1 Sec27 2 Erb1 YER006W 2 Ilv1 YGL245W 2 Ilv1 Sec27 2 Ioc3 Rsc8 2 Isw2 Itc1 2 Kre33 YJL109C 2 Kre33 YPL012W 2 Mot1 Isw1 2 Npl3 Smd3 2 Npl6 Isw2 2 Npl6 Mot1 2 Rad52 Rfa1 2 Rpc40 Rsc8 2 Rrp4 Dis3 2 Rrp40 Rrp46 2 Cbf5 Kre33 3 YGL128C Clf1 YLR424W 3 Cka2 Cka1 Ckb1 3 Has1 Nop12 Sik1 3 Hrr25 Enp1 YDL060W 3 Hrr25 Swi3 Snf2 3 Kre35 Nog1 YGR103W 3 Krr1 Cbf5 Kre33 3 Nab3 Nrd1 YML117W 3 Nog1 YGR103W YER006W 3 Bms1 Sik1 Rpp2b 3 Rpn10 Rpt3 Rpt6 3 Rpn11 Rpn12 Rpn8 3 Rpn12 Rpn8 Rpn10 3 Rpn9 Rpt3 Rpt5 3 Rpn9 Rpt3 Rpt6 3 Brx1 Sik1 YOR206W 3 Sik1 Kre33 YJL109C 3 Taf145 Taf90 Taf60 4 Fyv14 Krr1 Sik1 YLR409C 4 Mrpl35 Mrpl8 YML025C Mrpl3 4 Rpn12 Rpn8 Rpt3 Rpt6 5 Rpn6 Rpt2 Rpn12 Rpn3 Rpn8 5 Ada2 Gcn5 Rpo21 Spt7 Taf60 6 YLR033W Ioc3 Npl6 Rsc2 Itc1 Rpc40 6 Dim1 Ltv1 YOR056C YOR145C Enp1 YDL060W 6 Luc7 Rse1 Smd3 Snp1 Snu71 Smd2 6 Pre3 Pre2 Pre4 Pre5 Pre8 Pup3 7 Clf1 Lea1 Rse1 YLR424W Prp46 Smd2 Snu114 7 Pre1 Pre7 Pre2 Pre4 Pre5 Pre8 Pup3 7 Blm3 Pre10 Pre2 Pre4 Pre5 Pre8 Pup3 8 Clf1 Prp4 Smb1 Snu66 YLR424W Prp46 Smd2 Snu114 8 Pre2 Pre4 Pre5 Pre8 Pup3 Pre6 Pre9 Scl1 10 Cdc33 Dib1 Lsm4 Prp31 Prp6 Clf1 Prp4 Smb1 Snu66 YLR424W 12 Dib1 Lsm4 Prp31 Prp6 Clf1 Prp4 Smb1 Snu66 YLR424W Prp46 Smd2 Snu Emg1 Imp3 Imp4 Kre31 Mpp10 Nop14 Sof1 YMR093W YPR144C Krr1 YDR449C Enp1 13 Ecm2 Hsh155 Prp19 Prp21 Snt309 YDL209C Clf1 Lea1 Rse1 YLR424W Prp46 Smd2 Snu Brr1 Mud1 Prp39 Prp40 Prp42 Smd1 Snu56 Luc7 Rse1 Smd3 Snp1 Snu71 Smd2 39 Cus1 Msl1 Prp3 Prp9 Sme1 Smx2 Smx3 Yhc1 YJR084W Brr1 Dib1 Ecm2 Hsh155 Lsm4 Mud1 Prp11 Prp19 Prp21 Prp31 Prp39 Prp40 Prp42 Prp6 Smd1 Snt309 Snu56 Srb2 YDL209C Clf1 Lea1 Luc7 Prp4 Rse1 Smb1 Smd3 Snp1 Snu66 Snu71 YLR424W  List of maximal hyperclique patterns at a support threshold 0 and an h-confidence threshold 60%. [1] Xiong et al. (Detailed results are at

14 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 14 Functional Group Verification Using Gene Ontology  Hypothesis: Proteins within the same pattern are more likely to perform the same function and participate in the same biological process  Gene Ontology Three separate ontologies: Biological Process, Molecular Function, Cellular Component Organized as a DAG describing gene products (proteins and functional RNA) Collaborative effort between major genome databases

15 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 15 Function Annotation for Hyperclique { PRE2 PRE4 PRE5 PRE6 PRE8 PRE9 PUP3 SCL1 }  GO hierarchy shows that the identified proteins in hyperclique perform the same function and involved in same biological process

16 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 16 More Hyperclique Examples # distinct proteins in cluster = 13 # proteins in one group = 10 (rest denoted as ) # distinct proteins in cluster = 13 # proteins in one group = 12 (rest denoted as )

17 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 17 More Hyperclique Examples.. # distinct proteins in cluster = 12 # proteins in one group = 12 # distinct proteins in cluster = 8 # proteins in one group = 8

18 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 18 More Hyperclique Examples.. # distinct proteins in cluster = 12 # proteins in one group = 10 (rest denoted by )

19 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 19 More Hyperclique Examples.. # distinct proteins in cluster = 10 # proteins in one group = 9 (rest denoted as )

20 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 20 More Hyperclique Examples..  Only two Proteins SRB2 and CSN12 involved in cellular process and development got clustered together with group of proteins involved in physiological process  It is observed that 37 proteins out of 39 annotated proteins are responsible for same molecular function, mRNA splicing via spliceosome # distinct proteins in cluster = 39 # proteins in one group = 32 # proteins at node ‘mRNA splicing’ = 37

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23 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 23 Clustering of Protein Complex Data  Clustering software CLUTO ( ) is used to cluster the proteins in groups Repeated bisection method is used as the base method for clustering Cosine similarity measure is used to find similarity between proteins  Parameter to define the maximum number of clusters that could be obtained is set to 100  Best 17 clusters (as measured by internal similarity) are analyzed as candidates for functional modules

24 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 24 Clustering Results – GO Hierarchies # distinct proteins in cluster = 3 # proteins in one group = 2 (Protein Pho12 not annotated) # distinct proteins in cluster = 4 # proteins in one group = 4

25 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 25 Clustering Results – GO Hierarchies # distinct proteins in cluster = 8 # proteins in one group = 8 # distinct proteins in cluster = 7 # proteins in one group = 6 (rest denoted by )

26 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 26 Clustering Results – GO Hierarchies # distinct proteins in cluster = 5 # proteins in one group = 5 # distinct proteins in cluster = 2 # proteins in one group = 2

27 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 27 Clustering Results – GO Hierarchies # distinct proteins in cluster = 6 # proteins in one group = 6 # distinct proteins in cluster = 5 # proteins in one group = 5

28 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 28 Clustering Results – GO Hierarchies  Proteins MNN10 and ANP1 (denoted by ) involved in metabolism got clustered together with group of proteins involved in physiological process # distinct proteins in cluster = 6 # proteins in one group = 4

29 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 29 Clustering Results – GO Hierarchies # distinct proteins in cluster = 11 # proteins in one group = 10  Protein SKN1 (denoted by ) involved in metabolism got clustered together with proteins involved in cellular physiological process

30 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 30 Clustering Results – GO Hierarchies # distinct proteins in cluster = 30 # proteins in one group = 22 Group of 22 proteins

31 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 31 Clustering Results – GO Hierarchies # distinct proteins in cluster = 7 # proteins in one group = 4 (Rest of the 3 proteins are marked as )

32 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 32 Clustering Results – GO Hierarchies # distinct proteins in cluster = 8 # proteins in one group = 8 # distinct proteins in cluster = 5 # proteins in one group = 5

33 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 33 Clustering Results – GO Hierarchies # distinct proteins in cluster = 8 # proteins in one group = 6 (rest denoted by )  Protein AAP1 and VAM6 (denoted by ) got clustered together with proteins involved in biological process of membrane fusion

34 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 34 Clustering Results – GO Hierarchies  Protein AAP1 and VAM6 (denoted by ) got clustered together with group of proteins involved in biological process of membrane fusion # distinct proteins in cluster = 8 # proteins in one group = 4 (rest denoted by )

35 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 35 Clustering Results – GO Hierarchies # distinct proteins in cluster = 7 # proteins in one group = 5 (rest denoted by )

36 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 36 Error Tolerant Itemsets (ETIs) An error-tolerant itemset (ETI) can have a fraction  of the items missing in each transaction. Example: see the data in the table –Let  = 1/4. In other words, each transaction needs to have 3/4 (75%) of the items. –X = {i 1, i 2, i 3, i 4 } and Y = {i 5, i 6, i 7, i 8 } are both ETIs with a support of 4

37 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 37 ETIs to Identify Protein Functional Modules  Groups of proteins are identified as error tolerant itemsets (ETIs)  ETI relaxes the density constraints of the pattern in both dimensions  Maximum sparseness allowed: 0.2 (along row) and 0.25 (along column)  Minimum support: 5 protein complexes  Gene Ontology is used to validate following three identified ETIs {CLF1,LEA1,PRP4,PRP46,RSE1,SMB1,SMD2,SNU114,SPP382} {Pre2,Pre4,Pre5,Pre6,Pre8, Pre9,Pup3,Rpt3,Scl1} {Rpn10,Rpn12,Rpn3,Rpn6,Rpn8,Rpn9,Rpt2,Rpt3,Rpt6}

38 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 38 ETI Pattern validated using GO  Pattern: {CLF1, LEA1, PRP4, PRP46, RSE1, SMB1, SMD2, SNU114, SPP382}  Almost all proteins involved in one biological process (mRNA splicing)

39 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 39 More ETI Patterns..  Pattern: {Pre2,Pre4,Pre5,Pre6,Pre8, Pre9,Pup3,Rpt3,Scl1}  All proteins involved in one biological process, ubiquitin- dependent protein catabolism  Hyperclique technique identified the same pattern except protein RPT3, which is found to have same function – relaxing the constraints using ETI technique helped identify bigger group

40 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 40 More ETI Patterns..  Pattern: {Rpn10, Rpn12, Rpn3, Rpn6, Rpn8, Rpn9, Rpt2, Rpt3, Rpt6}  All proteins involved in one biological process, ubiquitin-dependent protein catabolism

41 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 41 Concluding Remarks  Hyperclique and ETI patterns show great promise for identifying protein modules and for annotating uncharacterized proteins  Clustering does not perform as well as hypercliques and ETI due to a variety of reasons: Each protein gets assigned to some cluster even if there is no right cluster for it Modules can be overlapping Modules can of different sizes Data is high-dimensional

42 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 42 References [1] Hui Xiong, X. He, Chris Ding, Ya Zhang, Vipin Kumar, Stephen R. Holbrook, Identification of Functional Modules in Protein Complexes via Hyperclique Pattern Discovery, in Proc. of the Pacific Symposium on Biocomputing, (PSB 2005), 2005 [2] Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, Introduction to Data Mining, Addison-Wesley April 2005 [3] Jinze Liu, Susan Paulsen, Xing Xu, Wei Wang, Andrew Nobel, Jan Prins, Mining Approximate Frequent Item sets in the Presence of Noise: Algorithms and Analysis, SIAM 2006 [4] Pang-Ning Tan, Vipin Kumar, and Jaideep Srivastava, Selecting the Right Interestingness Measure for Association Patterns, Proc of the Eighth ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD-2002) [5] Hui Xiong, Pang-Ning Tan, and Vipin Kumar, Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution, In Proc. of the Third IEEE International Conference on Data Mining (ICDM 2003) [6] Hui Xiong, Pang-Ning Tan, and Vipin Kumar, Hyperclique Pattern Discovery, Data Mining and Knowledge Discovery Journal, accepted for publication as a regular paper, 2006 [7] A. Gavin et al. Functional organization of the yeast proteome by systematic analysis of protein complexes, Nature, 415: , 2002 [8] Matteo Pellegrini et al., Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles, Proc. Natl. Acad. Sci. USA Vol. 96, pp. 4285–4288, April 1999, Biochemistry

43 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 43 Organizing Committee Steering Committee Chair Vipin Kumar, University of Minnesota Conference Co-Chairs Chid Apte, IBM Research David Skillicorn, Queen’s University Program Co-Chairs Srinivasan Parthasarathy, Ohio State University Bing Liu, University of Illinois at Chicago Tutorial Chair Pang-Ning Tan, Michigan State University Workshop Co-Chairs Michael Berry, University of Tennessee Philip Chan, Florida Institute of Technology Publicity Chair Hui Xiong, Rutgers University

44 June 28, 2006 Association Pattern Analysis – Applications in Bioinformatics 44


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