Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data Vipin Kumar University of Minnesota [email protected]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
Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data. Vipin Kumar University of Minnesota [email protected] www.cs.umn.edu/~kumar Team Members: Michael Steinbach, Rohit Gupta, Hui Xiong, Gaurav Pandey, Tushar Garg - PowerPoint PPT Presentation
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Association Analysis-based Extraction of Functional Information from
Team Members: Michael Steinbach, Rohit Gupta, Hui Xiong, Gaurav Pandey, Tushar Garg Collaborators: Chris Ding, Xiaofeng He, Ya Zhang, Stephen R. Holbrook
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 5
Overview
This talk is about using association analysis to address these limitations of protein interaction data
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-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
TID Items
1 Bread, Milk
2 Bread, Diaper, Beer, Eggs
3 Milk, Diaper, Beer, Coke
4 Bread, Milk, Diaper, Beer
5 Bread, Milk, Diaper, Coke
Set-Based Representation of Data
ons transactiTotal
Y and Xcontain that ons transacti# s Support,
Xcontain that ons transacti#
Y and Xcontain that ons transacti# c ,Confidence
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Has well-known limitations
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
null
AB AC AD AE BC BD BE CD CE DE
A B C D E
ABC ABD ABE ACD ACE ADE BCD BCE BDE CDE
ABCD ABCE ABDE ACDE BCDE
ABCDE
Association Analysis
Given d items, there are 2d possible candidate itemsets
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 8
There are lots of measures proposed in the literature
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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)>=hc, where hc is a user specified minimum h-confidence threshold
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 10
Alternate Equivalent Definitions of h-confidence
Given a pattern P = {i1, i2,…, im}
• Definition:
• Definition:
1 2( ) min{ ({ } { { }}) | { , ,..., }}mhconf P conf x P x x i i i
1 2( ) min{ ( ) | , { , ,..., }& }mhconf P conf X Y X Y i i i X Y P
All-Confidence Measure
Omiecinski – TKDE 2003
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 11
Properties of Hyperclique Pattern
Anti-monotone
High Affinity Property• High h-confidence implies tight coupling amongst all items in the pattern
Magnitude of relationship consistent with many other measures Jaccard, Correlation, Cosine
Cross support property
• Eliminates patterns involving items that have very different support levels
' , ( ') ( )if P P then hconf P hconf P
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 12
Cross Support Property of h-confidence
Support distribution of the pumsb dataset
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
Given a Pattern P = {i1, i2,…, im}
For any two Itemsets
hconf(P)
&X Y P X Y
supp{X} supp{Y}
,X Y P
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 13
Applications of H-confidence/Hypercliques
• Pattern-preserving clustering [Xiong et al, 2004, SDM]• Reducing privacy leakage in databases [Xiong et al,
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)
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 19
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 )
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 20
More Hyperclique Examples..
# distinct proteins in cluster = 12
# proteins in one group = 12
# distinct proteins in cluster = 8
# proteins in one group = 8
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 21
More Hyperclique Examples..
# distinct proteins in cluster = 12
# proteins in one group = 12
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 22
More Hyperclique Examples..
# distinct proteins in cluster = 10
# proteins in one group = 9
(rest denoted as )
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 24
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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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 < 0.20) = 198
• Generally very poor
• for (0.20 <= h-confidence < 0.50) = 246
• moderate quality
• for (h-confidence >= 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)
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 26
Clustering of Protein Complex Data
Clustering software CLUTO (http://
glaros.dtc.umn.edu/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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 29
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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 30
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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 31
Clustering Results – GO Hierarchies
# distinct proteins in cluster = 7
# proteins in one group = 4
(Rest of the 3 proteins are marked as )
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 32
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 )
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Input PPI graph
Transformed PPI graph where Pi
and Pj are connected if
(Pi,Pj) is a hyperclique
pattern
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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,n2=sum(weights) of edges incident on p1,p2
m = sum of min(weights) of edges to common neighbors of p1,p2
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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/(n1+n2-m)) [Brun et al, 2003]
• Min(fractions of common neighbors) = Min(m/n1, m/n2)– Identical to pairwise h-confidence
i j i j
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 38
H-confidence Example
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
Unweighted Network Weighted Network
Hconf(p1,p2)= min(0.5,0.5) = 0.5
Hconf(p1,p2)= min(0.5/0.6,0.5/1.2) = 0.416
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 39
Sparsification to remove spurious edges
Common neighbor-based transformation
Pruning to removespurious edges
# edges = 6490 # edges = 95739 # edges = 6874
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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, 2006]’s 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 42
Results on Combined data set
Precision-Recall Accuracy of top-k predictions
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 43
Results on Krogan et al’s data set
Precision-Recall Accuracy of top-k predictions
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 44
Results on DIPCore
Precision-Recall Accuracy of top-k predictions
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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.
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 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). 221–232.
[Xiong et al, 2006a] XIONG, H., TAN, P.-N., AND KUMAR, V. 2003. Hyperclique Pattern Discovery, Data Mining and Knowledge Discovery, 13(2):219-242
[Xiong et al, 2006b] XIONG, H., PANDEY, G., STEINBACH, M., AND KUMAR, V. 2006, Enhancing Data 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 on Privacy Preserving Data Management , Vol. 15, No. 4, pp. 388-402, November, 2006
[Xiong et al, 2004] Hui Xiong, Michael Steinbach, Pang-Ning Tan and Vipin Kumar, HICAP: Hierarchical Clustering with Pattern Preservation, SIAM Data Mining 2004
[Tan et al, 2005] TAN, P.-N., STEINBACH, M., AND KUMAR, V. 2005. Introduction to Data Mining. Addison-Wesley.[Nabieva et al, 2005] NABIEVA, E., JIM, K., AGARWAL, A., CHAZELLE, B., AND SINGH, M. 2005. Whole-proteome
prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21, Suppl. 1, i1–i9.[Deng et al, 2003] DENG, M., SUN, F., AND CHEN, T. 2003. Assessment of the reliability of protein–protein
interactions and protein function prediction. In Pac Symp Biocomputing. 140–151.[Gavin et al, 2002] A. Gavin et al. Functional organization of the yeast proteome by systematic analysis of protein
complexes, Nature, 415:141-147, 2002[Hart et al, 2006] G Traver Hart, Arun K Ramani and Edward M Marcotte, How complete are current yeast and human
Nov 26, 2007 Association Analysis-based Extraction of Functional Information from Protein-Protein Interaction Data 48
References (II)
[Brun et al, 2003] BRUN, C., CHEVENET, F.,MARTIN, D.,WOJCIK, J., GUENOCHE, A., AND JACQ, B. 2003. Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biology 5, 1, R6
[Samanta et al, 2003] SAMANTA, M. P. AND LIANG, S. 2003. Predicting protein functions from redundancies in large-scale protein interaction networks. Proc Natl Acad Sci U.S.A. 100, 22, 12579–12583
[Salwinski et al, 2004] Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004) The Database of Interacting Proteins: 2004 update. NAR 32 Database issue:D449-51, http://dip.doe-mbi.ucla.edu/
[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 interactions: Two methods for assessment of the reliability of high-throughput observations. Mol Cell Prot 1:349-356