1. 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

2. http://www.ipam.ucla.edu/programs/fg2000/fgt_speed7.ppt cDNA Microarray Experiment

3. w 11 w 12 w 13 w 21 w 22 w 23 w 31 w 32 w 33 sample 1sample 2sample 3 genes samples Microarray Data  asymmetric dimensionality 10 ~ 100 samples 1000 ~ 10000 genes

4. Scope and Goal Sample Partition Microaray Database Gene Expression Matrices Gene Expression Data Analysis Important patterns Important patterns Important patterns Gene Microarray Images Gene Expression Patterns Visualization

5. Microarray Data Analysis  Analysis from two angles  sample as object, gene as attribute  gene as object, sample/condition as attribute

6. Sample-based Analysis Informative Genes Non- informative Genes gene 1 gene 6 gene 7 gene 2 gene 4 gene 5 gene 3 1 2 34 5 6 7 samples

7. Related Work  New tools using traditional methods :  Clustering with feature selection:  Subspace clustering TreeView CLUTO CIT SOTA GeneSpring J-Express CLUSFAVOR SOM K-means Hierarchical clustering Graph based clustering PCA

8. Quality Measurement  Intra-phenotype consistency:  Inter-phenotype divergency:  The quality of phenotype and informative genes:

9. 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. ofor each gene, try possible insert/remove ofor each sample, try best movement.

10. 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. ocompute reference degree for each sample groups; oselect k groups of samples; odo partition adjustment.  Adjust the candidate informative genes. ocompute W for reference partition on G operform possible adjustment of each genes  Refinement Phase

11. Reference Partition Detection  Reference degree: measurement of a sample group over all gene groups  The sample group having the highest reference degree  S p0, S p1, S p2 … S px,…  Partition adjustment: check the missing samples

12. Gene Adjustment  For each gene, try possible insert/remove

13.  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*m 2 *I) Refinement Phase

14. Phenotype Detection Data SetMS-IFNMS-CONLeukemia- G1 Leukemia- G2 ColonBreast Data Size4132*284132*307129*387129*342000*623226*22 J-Express 0.48150.48510.50920.49650.49390.4112 SOTA 0.48150.49200.60170.49200.49390.4112 CLUTO 0.48150.48280.57750.48660.49660.6364 Kmeans/PCA 0.48410.48510.65860.49200.49660.5844 SOM / PCA 0.52380.54020.50920.49200.49390.5844  -cluster 0.48940.48510.50070.45380.47960.4719 Heuristic 0.80520.62300.97610.70860.62930.8638 Mutual 0.83870.65130.97780.75580.68270.8749

15. Informative Gene Selection

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