1. Brad Windle, Ph.D. 628-1956 bwindle@hsc.vcu.edu Unsupervised Learning and Microarrays Web Site: http://www.people.vcu.edu/~bwindle Link to Courses and then lecture for this class

2. Gene Expression Profiling Unsupervised Learning Cluster Analysis and Applications Good review of microarray data analysis is Computational analysis of microarray data. Quackenbush J. Nat Rev Genet 2001 Jun;2(6):418-427

3. Reductionism versus Systems Approach Why generate global analyses? as opposed to picking a gene/protein and hoping you get lucky and it has great significance to the big picture or to mankind’s health.

4. Normalizing Data Northern blot For normalizing samples, you would divide experimental values by the mean of the values thought to be constant through the samples

5. Sample values are typically normalized by dividing by the mean of the reference values or mean of all values

6. What about normalizing gene values across all the samples? 100 10 Rationale for normalizing samples does not apply to genes One strategy is to subtract the mean (mean centering).

7. Log transformation.01 1 10 100 // -2 0 2

8. Gene to Gene Variability

10. Cluster Analysis Goal - puts items (genes) together in clusters based on similarity of expression across various conditions, either similarity of absolute expression levels or overall similarity in pattern

15. Pearson

17. Hierarchical Clustering

18. Divisive Agglomerative (Aggregative) Clustering Methods

19. Cluster Linkage Methods Nearest Neighbor or Single Linkage Furthest Neighbor or Complete Linkage Average Neighbors or Average Linkage

20. X Y Z

21. 1 2 3 K-Means Clustering and it’s relative Self-Organizing Maps (SOM) 1 2 3 1 2 3

23. Ranking Order Clustering

24. Cluster Playground 3

25. Applications of Gene Expression Profiling and Cluster Analysis Tissue or Tumor Classification Gene Classification Drug Classification Drug Target Identification

27. B-Cell Lymphoma NATURE 403, 503-511, 2000 Indistinguishable by histology Yet half responded well to therapy and half did not Where there differences in gene expression that correlate with drug response? Gene expression profiles showed half the lymphomas were of GC B-Cell lineage and the other of Activated B-Cell lineage A subset of genes predicts therapeutic outcome

31. M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 M16 M17 M18 D1D2D3D4D5D6 D7D8D9D100D11D12 D13D14D15D16D17D18 Gene Expression Profiling of Yeast Mutants and Drugs Cell 102, 109–126, 2000 Mutants Drugs M4 D17 Erg2Dyclonine Human sigma receptor

32. Validation of cdc28 Kinase Target Inhibition SCIENCE 281, 533-538, 1998 cdc28 - D1D2 } Cdc28-regulated genes } Phosphate metabolism genes Nucleotide analogs that block cdc28p D1 and D2 Pho85

33. Drug 1 23452345 Cells A B C D E -2 -1 0 -1.01 1 -1.5 2 0 -.5.4 0 1 1.2 0.7 2 1.9 1 0 -.5.5 -.8 COMPARE Clustering Drugs Based on Cell Line Sensitivities Nature Genetics 24: 236-244, 2000

34. T1 T2 A7 T2 A7 T1

35. Profiling

36. Clustering NCI 60 Cancer Cell Lines Nature Genetics 24: 227-238 6165 Genes 9 Types of Tissues/Tumors Breast CNS Colon Leukemia Lung Melanoma Ovarian Prostate Renal

37. Filtering Data Filter out data with the program Cluster, based on SD cuts