Mining phenotypes and informative genes from gene expression data
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Mining Phenotypes and Informative Genes from Gene Expression Data. Chun Tang, Aidong Zhang and Jian Pei Department of Computer Science and Engineering State University of New York at Buffalo. cDNA Microarray Experiment. http://www.ipam.ucla.edu/programs/fg2000/fgt_speed7.ppt.

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Mining Phenotypes and Informative Genes from Gene Expression Data

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Mining phenotypes and informative genes from gene expression data

Mining Phenotypes and Informative Genes from Gene Expression Data

Chun Tang, Aidong Zhang and Jian Pei

Department of Computer Science and Engineering

State University of New York at Buffalo


Cdna microarray experiment

cDNA Microarray Experiment

http://www.ipam.ucla.edu/programs/fg2000/fgt_speed7.ppt


Microarray data

Microarray Data

sample 1

sample 2

sample 3

w11 w12 w13

w21 w22 w23

w31 w32 w33

genes

  • asymmetric dimensionality

    • 10 ~ 100 samples

    • 1000 ~ 10000 genes

samples


Scope and goal

Gene

Expression

Matrices

Gene Expression

Patterns

Scope and Goal

Microaray Database

Gene

Microarray

Images

Sample

Partition

Gene Expression

Data Analysis

Visualization

Important

patterns

Important

patterns

Important

patterns


Microarray data analysis

Microarray Data Analysis

  • Analysis from two angles

    • sample as object, gene as attribute

    • gene as object, sample/condition as attribute


Sample based analysis

samples

1 2 3

456 7

gene1

Informative Genes

gene2

gene3

gene4

Non- informative Genes

gene5

gene6

gene7

Sample-based Analysis


Related work

Related Work

  • New tools using traditional methods :

  • Clustering with feature selection:

  • Subspace clustering

  • SOM

  • K-means

  • Hierarchical clustering

  • Graph based clustering

  • PCA


Quality measurement

Quality Measurement

  • Intra-phenotype consistency:

  • Inter-phenotype divergency:

  • The quality of phenotype and informative genes:


Heuristic searching

Heuristic Searching

  • Starts with a random K-partition of samples and a subset of genes as the candidate of the informative space.

  • Iteratively adjust the partition and the gene set toward the optimal solution.

    • for each gene, try possible insert/remove

    • for each sample, try best movement.


Mutual reinforcing adjustment

Mutual Reinforcing Adjustment

  • Divide the original matrix into a series of exclusive sub-matrices based on partitioning both the samples and genes.

  • Post a partial or approximate phenotype structure called a reference partition of samples.

    • compute reference degree for each sample groups;

    • select k groups of samples;

    • do partition adjustment.

  • Adjust the candidate informative genes.

    • compute W for reference partition on G

    • perform possible adjustment of each genes

  • Refinement Phase


Reference partition detection

Reference Partition Detection

  • Reference degree: measurement of a sample group over all gene groups

  • The sample group having the highest reference degree Sp0 , Sp1 , Sp2 … Spx ,…

  • Partition adjustment: check the missing samples


Gene adjustment

Gene Adjustment

  • For each gene, try possible insert/remove


Refinement phase

Refinement Phase

  • The partition corresponding to the best state may not cover all the samples.

  • Add every sample not covered by the reference partition into its matching group  the phenotypes of the samples.

  • Then, a gene adjustment phase is conducted. We execute all adjustments with a positive quality gain  informative space.

  • Time complexity O(n*m2*I)


Phenotype detection

Phenotype Detection


Informative gene selection

Informative Gene Selection


References

Agrawal, Rakesh, Gehrke, Johannes, Gunopulos, Dimitrios and Raghavan, Prabhakar.

Automatic subspace clustering of high dimensional data for data mining applications.

In SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management

of Data, pages 94–105, 1998.

Azuaje, Francisco. Making Genome Expression Data Meaningful: Prediction and

Discovery of Classes of Cancer Through a Connectionist Learning Approach. In Proceedings

of IEEE International Symposium on Bioinformatics and Biomedical Engineering,

pages 208–213, 2000.

Ben-Dor A., Friedman N. and Yakhini Z. Class discovery in gene expression data.

In Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB

2001), pages 31–38. ACM Press, 2001.

Cheng Y., Church GM. Biclustering of expression data. Proceedings of the Eighth

International Conference on Intelligent Systems for Molecular Biology (ISMB), 8:93–

103, 2000.

DeRisi J., Penland L., Brown P.O., Bittner M.L., Meltzer P.S., Ray M., Chen Y., Su

Y.A. and Trent J.M. Use of a cDNA microarray to analyse gene expression patterns

in human cancer. Nature Genetics, 14:457–460, 1996.

Getz G., Levine E. and Domany E. Coupled two-way clustering analysis of gene microarray

data. Proc. Natl. Acad. Sci. USA, Vol. 97(22):12079–12084, October 2000.

Golub T.R., Slonim D.K., Tamayo P., Huard C., Gassenbeek M., Mesirov J.P., Coller

H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield D.D. and Lander E.S.

Molecular classification of cancer: Class discovery and class prediction by gene expression

monitoring. Science, Vol. 286(15):531–537, October 1999.

Yang, Jiong, Wang, Wei, Wang, Haixun and Yu, Philip S. δ-cluster: Capturing Subspace

Correlation in a Large Data Set. In Proceedings of 18th International Conference

on Data Engineering (ICDE 2002), pages 517–528, 2002.

Xing E.P. and Karp R.M. Cliff: Clustering of high-dimensional microarray data via

iterative feature filtering using normalized cuts. Bioinformatics, Vol. 17(1):306–315,

2001.

References

  • Agrawal, Rakesh, Gehrke, Johannes, Gunopulos, Dimitrios and Raghavan, Prabhakar. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, pages 94–105, 1998.

  • Ben-Dor A., Friedman N. and Yakhini Z. Class discovery in gene expression data. In Proc. Fifth Annual Inter. Conf. on Computational Molecular Biology (RECOMB 2001), pages 31–38. ACM Press, 2001.

  • Cheng Y., Church GM. Biclustering of expression data. Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology (ISMB), 8:93–103, 2000.

  • Golub T.R., Slonim D.K., Tamayo P., Huard C., Gassenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield D.D. and Lander E.S. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, Vol. 286(15):531–537, October 1999.

  • Xing E.P. and Karp R.M. Cliff: Clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. Bioinformatics, Vol. 17(1):306–315, 2001.


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