Bi clustering
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Bi-Clustering. COMP 790-90 Seminar Spring 2011. Definition of OP-Cluster.

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Bi-Clustering

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Bi clustering

Bi-Clustering

COMP 790-90 Seminar

Spring 2011


Definition of op cluster

Definition of OP-Cluster

  • Let I be a subset of genes in the database. Let J be a subset of conditions. We say <I, J> forms an Order Preserving Cluster (OP-Cluster),if one of the following relationships exists for any pair of conditions.

Expression Levels

A1 A2 A3 A4

when


Problem statement

Problem Statement

  • Given a gene expression matrix, our goal is to find all the statistically significant OP-Clusters. The significance is ensured by the minimal size threshold nc and nr.


Conversion to sequence mining problem

Conversion to Sequence Mining Problem

Sequence:

Expression Levels

A1 A2 A3 A4


Ming op clusters a na ve approach

Ming OP-Clusters: A naïve approach

root

  • A naïve approach

    • Enumerate all possible subsequences in a prefix tree.

    • For each subsequences, collect all genes that contain the subsequences.

  • Challenge:

    • The total number of distinct subsequences are

a

a

b

c

d

b

b

c

d

a

c

d

c

d

d

b

d

b

c

c

d

a

d

d

c

d

b

c

b

d

c

d

a

A Complete Prefix Tree with 4 items {a,b,c,d}


Mining op clusters prefix tree

a:3

d:2

d:3

c:2

c:3

Mining OP-Clusters: Prefix Tree

  • Goal:

  • Build a compact prefix tree that includes all sub-sequences only occurring in the original database.

  • Strategies:

  • Depth-First Traversal

  • Suffix concatenation: Visit subsequences that only exist in the input sequences.

  • Apriori Property: Visit subsequences that are sufficiently supported in order to derive longer subsequences.

Root

a:1,2

a:1,2,3

a:1,2

a:1,2,3

b:3

d:1

d:1,2,3

d:1,2,3

d:1,3

d:1,3

b:2

a:3

b:1

c:1,3

c:1,2,3

d:2

d:3

c:1

c:2

c:3


References

References

  • J. Yang, W. Wang, H. Wang, P. Yu, Delta-cluster: capturing subspace correlation in a large data set, Proceedings of the 18th IEEE International Conference on Data Engineering (ICDE), pp. 517-528, 2002.

  • H. Wang, W. Wang, J. Yang, P. Yu, Clustering by pattern similarity in large data sets, to appear in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), 2002.

  • Y. Sungroh,  C. Nardini, L. Benini, G. De Micheli, Enhanced pClustering and its applications to gene expression data Bioinformatics and Bioengineering, 2004.

  • J. Liu and W. Wang, OP-Cluster: clustering by tendency in high dimensional space, ICDM’03.


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