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Title: Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data


1
Association Analysis-based Extraction of
Functional Information from Protein-Protein
Interaction Data
  • Vipin Kumar
  • University of Minnesota
  • kumar_at_cs.umn.edu
  • www.cs.umn.edu/kumar
  • Team Members Michael Steinbach, Rohit Gupta, Hui
    Xiong, Gaurav Pandey, Tushar Garg
  • Collaborators Chris Ding, Xiaofeng He, Ya
    Zhang, Stephen R. Holbrook
  • Research supported by NSF, IBM

2
Protein Function and Interaction Data
  • Proteins usually interact with other proteins to
    perform their function(s)
  • Interaction data provides a glimpse into the
    mechanisms underlying biological processes
  • Networks of pairwise protein-protein interactions
  • Protein complexes
  • Neighboring proteins in an interaction network
    tend to perform similar functions
  • Several computational approaches proposed for
    predicting protein function from interaction
    networks Pandey et al, 2006
  • A group of proteins occurring in many complexes
    may represent a functional modules that consists
    of proteins involved in similar biological
    processes

3
Problems with Available Interaction Data (I)
  • Noise Spurious or false positive interactions
  • Leads to significant fall in performance of
    protein function prediction algorithms Deng et
    al, 2003



Hart et al,2006
4
Problems with Available Interaction Data (II)
  • Incompleteness Unavailability of a major
    fraction of interactomes of major organisms
  • Yeast 50, Human 11
  • May delay the discovery of important knowledge


Hart et al, 2006
5
Overview
  • This talk is about using association analysis to
    address these limitations of protein interaction
    data

6
Association Analysis
  • Association analysis Analyzes relationships
    among items (attributes) in a binary transaction
    data
  • Example data market basket data
  • Applications in business and science
  • Marketing and Sales Promotion
  • Identification of functional modules from
    protein complexes
  • Noise removal from protein interaction data
  • 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
7
Association Analysis
  • 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
  • Has well-known limitations

Given d items, there are 2d possible candidate
itemsets
8
There are lots of measures proposed in the
literature
9
The H-confidence Measure
  • The h-confidence of a pattern P i1, i2,, im
  • Illustration
  • A pattern P is a hyperclique pattern if
    hconf(P)gthc, where hc is a user specified
    minimum h-confidence threshold

10
Alternate Equivalent Definitions of h-confidence
  • Given a pattern P i1, i2,, im
  • Definition
  • Definition

All-Confidence Measure Omiecinski TKDE 2003
11
Properties of Hyperclique Pattern
12
Cross Support Property of h-confidence
  • At high support, all patterns that involve low
    support items are eliminated
  • At low support, too many spurious patterns are
    generated that involve one high support item and
    one low support item

Support distribution of the pumsb dataset
13
Applications of H-confidence/Hypercliques
  • Pattern-preserving clustering Xiong et al, 2004,
    SDM
  • Reducing privacy leakage in databases Xiong et
    al, 2006c, VLDB Journal
  • Noise removal Xiong et al, 2006b, IEEE TKDE
  • Data points not a member of any hypercliques
    hypothesized to be noisy
  • Improved performance of several data analysis
    tasks (association analysis, clustering) on
    several types of data sets (text, microarray
    data)
  • Illustrates noise resistance property of
    hypercliques and h-confidence
  • Discovery of functional modules from protein
    complexes Xiong et al, 2005, PSB
  • Noise-resistant transformation of protein
    interaction networks Pandey et al, 2007, KDD

14
I. Application of Association Analysis
Identification of Protein Function Modules
  • Published in Xiong et al 2005, PSB
  • The TAP-MS dataset by Gavin et al 2002 Tandem
    affinity purification (TAP) mass spectrometry
    (MS)
  • Contains 232 multi-protein complexes formed using
    1361 proteins
  • Number of proteins per complex range from 2 to 83
    (average 12 components)
  • Hyperclique derived from this data can be used to
    discover frequently occurring groups of proteins
    in several complexes
  • Likely to constitute functional modules

Complexes Proteins
c1 p1, p2
c2 p1, p3, p4, p5
c3 p2, p3, p4, p6
15
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
  • http//www.geneontology.org

16
Hyperclique Patterns from Protein Complex Data
  • List of maximal hyperclique patterns at a support
    threshold 2 and an h-confidence threshold 60.
    1 Xiong et al. (Detailed results are at
    http//cimic.rutgers.edu/hui/pfm/pfm.html)

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 Snu114 12 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 Snu114 13 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
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
2 Tif4632 Tif4631 2 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
17
Summary
  • Number of hypercliques
  • Size-2 22, Size-3 18, Size-4 3, Size-5 2
  • Size-6 4, Size-7 3, Size-8 2, Size-10 1
  • Size-12 2, Size-13 2, Size-39 1
  • In most cases, proteins identified as
    hypercliques found to be functionally coherent
    and part of same biological process evaluated
    using GO hierarchies

18
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

19
More Hyperclique Examples
20
More Hyperclique Examples..
distinct proteins in cluster 12 proteins in
one group 12
distinct proteins in cluster 8 proteins in
one group 8
21
More Hyperclique Examples..
distinct proteins in cluster 12 proteins in
one group 12
22
More Hyperclique Examples..
23
More Hyperclique Examples..
  • Only two Proteins SRB2 and ECM2 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
24
Functional Annotation of Uncharacterized Proteins
  • Hyeperclique Pattern Emg1 Imp3 Imp4 Kre31 Mpp10
    Nop14 Sof1 YMR093W YPR144C Krr1 YDR449C Enp1
  • 8 of the 12 proteins have annotation of RNA
    binding
  • Other 4 proteins have no functional annotation
  • Hypothesis Unannotated proteins have same
    molecular function RNA binding, since
    hypercliques tend to have proteins that are
    functionally coherent

25
Identification of Functional Modules Using
Frequent Itemset-based Approach
  • Closed frequent itemset-based approach produces
    over 500 patterns of size 2 or more with support
    threshold of 2
  • Number of patterns
  • for (h-confidence lt 0.20) 198
  • Generally very poor
  • for (0.20 lt h-confidence lt 0.50) 246
  • moderate quality
  • for (h-confidence gt 0.50) 65
  • Generally very good
  • Proteins in large size patterns (with high
    h-confidence) are found to be better functionally
    related than even proteins in small size patterns
    (with less h-confidence)

26
Clustering of Protein Complex Data
  • Clustering software CLUTO (http//glaros.dtc.umn.e
    du/gkhome/views/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 clusters (as measured by internal
    similarity) are usually the candidates for
    functional modules

27
Clustering Results Summary
  • Clusters with high internal similarity (as ranked
    by Cluto program) and relatively small sizes are
    found to be functionally coherent using GO
    hierarchies
  • It is found that large clusters with relatively
    low internal similarity have proteins with
    multiple function annotations
  • Few examples to illustrate this are shown

28
Clustering Results GO Hierarchies
distinct proteins in cluster 6 proteins in
one group 6
distinct proteins in cluster 5 proteins in
one group 5
29
Clustering Results GO Hierarchies
30
Clustering Results GO Hierarchies
31
Clustering Results GO Hierarchies
32
Clustering Results GO Hierarchies
33
Summary of Results
  • Hypercliques show great promise for identifying
    protein modules and for annotating
    uncharacterized proteins
  • Clustering does not perform as well as
    hypercliques 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 be of different sizes
  • Data is high-dimensional

34
Application II Association Analysis-based
Pre-processing of Protein Interaction Networks
  • Overall Objective Accurate inference of protein
    function from interaction networks
  • Complexity Noise and incompleteness in
    interaction networks adversely impact accuracy of
    functional inferences Deng et al, 2003
  • Potential Approach Pre-processing of interaction
    networks

35
Our Approach
  • Transform graph G(V,E,W) into G(V,E,W)
  • Tries to meet three objectives
  • Addition of potentially biologically valid edges
  • Removal of potentially noisy edges
  • Assignment of weights to the resultant set of
    edges that indicate their reliability

Transformed PPI graph where Pi and Pj are
connected if (Pi,Pj) is a hyperclique pattern
Input PPI graph
36
Pair-wise H-Confidence
  • Measure of the affinity of two items in terms of
    the transactions in which they appear
    simultaneously Xiong et al, 2006
  • For an interaction network represented as an
    adjacency matrix
  • Unweighted Networks n1,n2 neighbors of
    p1,p2 m shared neighbors of p1,p2
  • Weighted Networks n1,n2sum(weights) of edges
    incident on p1,p2 m sum of min(weights)
    of edges to common
    neighbors of p1,p2

37
Related Approaches Neighborhood-based Similarity
  • Motivation Two proteins sharing several common
    neighbors are likely to have a valid interaction
  • Probability (p-value) of having m common
    neighbors given degrees of the two proteins n1
    and n2, and size of the network N Samanta et al,
    2003
  • Handles the problem of high degree nodes
  • common neighbors or Jacquard similarity
    (m/(n1n2-m)) Brun et al, 2003
  • Min(fractions of common neighbors) Min(m/n1,
    m/n2)
  • Identical to pairwise h-confidence

38
H-confidence Example
Unweighted Network
Weighted Network
p1 p2 p3 p4 p5
p1 0 0 1 0 1
p2 0 0 1 1 0
p3 1 1 0 0 1
p4 1 1 0 0 1
p5 1 0 1 1 0
p1 p2 p3 p4 p5
p1 0 0 0.5 0 0.1
p2 0 0 1 0.2 0
p3 0.5 1 0 0 0.1
p4 0 0.2 0 0 0.5
p5 0.1 0 0.1 0.5 0
Hconf(p1,p2) min(0.5,0.5) 0.5
Hconf(p1,p2) min(0.5/0.6,0.5/1.2)
0.416
39
Sparsification to remove spurious edges
Common neighbor- based transformation
Pruning to remove spurious edges
edges 6490
edges 95739
edges 6874
40
Validation of Final Network
  • Use FunctionalFlow algorithm Nabieva et al,
    2005 on the original and transformed graph(s)
  • One of the most accurate algorithms for
    predicting function from interaction networks
  • Produces likelihood scores for each protein being
    annotated with one of 75 MIPS functional labels
  • Likelihood matrix evaluated using two metrics
  • Multi-label versions of precision and recall
  • mi predictions made, ni known
    annotations, ki correct predictions
  • Precision/accuracy of top-k predictions
  • Useful for actual biological experimental
    scenarios

41
Test Protein Interaction Networks
  • Three yeast interaction networks with different
    types of weighting schemes used for experiments
  • Combined
  • Composed from Ito, Uetz and Gavin (2002)s data
    sets
  • Individual reliabilities obtained from EPR index
    tool of DIP
  • Overall reliabilities obtained using a noisy-OR
  • Krogan et al, 2006s data set
  • 6180 interactions between 2291 annotated proteins
  • Edge reliabilities derived using machine learning
    techniques
  • DIPCore Deane et al, 2002
  • 5K highly reliable interactions in DIP
  • No weights assigned assumed unweighted

42
Results on Combined data set
Precision-Recall
Accuracy of top-k predictions
43
Results on Krogan et als data set
Precision-Recall
Accuracy of top-k predictions
44
Results on DIPCore
Precision-Recall
Accuracy of top-k predictions
45
Noise removal capabilities of H-confidence
  • H-confidence and hypercliques have been shown to
    have noise removal capabilities Xiong et al,
    2006
  • To test its effectiveness, we added 50 random
    edges to DIPCore, and re-ran the transformation
    process
  • Fall in performance of transformed network is
    significantly smaller than that in the original
    network

46
Summary of Results
  • H-confidence-based transformations generally
    produce more accurate and more reliably weighted
    interaction graphs Validated function prediction
  • Generally, the less reliable the weights assigned
    to the edges in the raw network, the greater
    improvement in performance obtained by using an
    h-confidence-based graph transformation.
  • Better performance of the h-confidence-based
    graph transformation method is indeed due to the
    removal of spurious edges, and potentially the
    addition of biologically viable ones and
    effective weighting of the resultant set of edges.

47
References (I)
  • Pandey et al, 2006 Gaurav Pandey, Vipin Kumar
    and Michael Steinbach, Computational Approaches
    for Protein Function Prediction A Survey, TR
    06-028, Department of Computer Science and
    Engineering, University of Minnesota, Twin Cities
  • Pandey et al, 2007 G. Pandey, M. Steinbach, R.
    Gupta, T. Garg and V. Kumar, Association
    analysis-based transformations for protein
    interaction networks a function prediction case
    study. KDD 2007 540-549
  • Xiong et al, 2005 XIONG, H., HE, X., DING, C.,
    ZHANG, Y., KUMAR, V., AND HOLBROOK, S. R. 2005.
    Identification of functional modules in protein
    complexes via hyperclique pattern discovery. In
    Proc. Pacific Symposium on Biocomputing (PSB).
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  • Xiong et al, 2006a XIONG, H., TAN, P.-N., AND
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  • Xiong et al, 2006b XIONG, H., PANDEY, G.,
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    Analysis with Noise Removal, IEEE TKDE,
    18(3)304-319
  • Xiong et al, 2006c Hui Xiong, Michael
    Steinbach, and Vipin Kumar, Privacy Leakage in
    Multi-relational Databases A Semi-supervised
    Learning Perspective, VLDB Journal Special Issue
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    No. 4, pp. 388-402, November, 2006
  • Xiong et al, 2004 Hui Xiong, Michael Steinbach,
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    Hierarchical Clustering with Pattern
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  • Tan et al, 2005 TAN, P.-N., STEINBACH, M., AND
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  • Nabieva et al, 2005 NABIEVA, E., JIM, K.,
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References (II)
  • Brun et al, 2003 BRUN, C., CHEVENET, F.,MARTIN,
    D.,WOJCIK, J., GUENOCHE, A., AND JACQ, B. 2003.
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  • Samanta et al, 2003 SAMANTA, M. P. AND LIANG,
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  • Salwinski et al, 2004 Salwinski L, Miller CS,
    Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004)
    The Database of Interacting Proteins 2004
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  • Gavin et al, 2006 Gavin et al, 2006, Proteome
    survey reveals modularity of the yeast cell
    machinery, Nature 440, 631-636
  • Deane et al, 2002 Deane CM, Salwinski L,
    Xenarios I, Eisenberg D (2002) Protein
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