Clustering Analysis Techniques for Microarray Gene Expression Data

1. CZ5225: Modeling and Simulation in BiologyLecture 3: Clustering Analysis for Microarray Data IProf. Chen Yu ZongTel: 6874-6877Email: yzchen@cz3.nus.edu.sghttp://xin.cz3.nus.edu.sgRoom 07-24, level 7, SOC1, NUS

2. Clustering Algorithms • Be weary - confounding computational artifacts are associated with all clustering algorithms. -You should always understand the basic concepts behind an algorithm before using it. • Anything will cluster! Garbage In means Garbage Out.

3. Supervised vs. Unsupervised Learning • Supervised: there is a teacher, class labels are known • Support vector machines • Backpropagation neural networks • Unsupervised: No teacher, class labels are unknown • Clustering • Self-organizing maps

4. Gene Expression Data Gene expression data on p genes for n samples mRNA samples sample1 sample2 sample3 sample4 sample5 … 1 0.46 0.30 0.80 1.51 0.90 ... 2 -0.10 0.49 0.24 0.06 0.46 ... 3 0.15 0.74 0.04 0.10 0.20 ... 4 -0.45 -1.03 -0.79 -0.56 -0.32 ... 5 -0.06 1.06 1.35 1.09 -1.09 ... Genes Gene expression level of gene i in mRNA sample j Log (Red intensity/Green intensity) = Log(Avg. PM - Avg. MM)

5. -2 2 Expression Vectors Gene Expression Vectors encapsulate the expression of a gene over a set of experimental conditions or sample types. 1.5 -0.8 1.8 0.5 -0.4 -1.3 1.5 0.8 Numeric Vector Line Graph Heatmap

6. Expression Vectors As Points in ‘Expression Space’ t 1 t 2 t 3 G1 -0.8 -0.3 -0.7 G2 -0.7 -0.8 -0.4 G3 Similar Expression -0.4 -0.6 -0.8 G4 0.9 1.2 1.3 G5 1.3 0.9 -0.6 Experiment 3 Experiment 2 Experiment 1

7. Cluster Analysis • Group a collection of objects into subsets or “clusters” such that objects within a cluster are closely related to one another than objects assigned to different clusters.

8. How can we do this? • What is closely related? • Distance or similarity metric • What is close? • Clustering algorithm • How do we minimize distance between objects in a group while maximizing distances between groups?

9. Distance Metrics (5.5,6) • Euclidean Distance measures average distance • Manhattan (City Block) measures average in each dimension • Correlation measures difference with respect to linear trends (3.5,4) Gene Expression 2 Gene Expression 1

10. Clustering Gene Expression Data Expression Measurements • Cluster across the rows, group genes together that behave similarly across different conditions. • Cluster across the columns, group different conditions together that behave similarly across most genes. j Genes i

11. Clustering Time Series Data • Measure gene expression on consecutive days • Gene Measurement matrix • G1= [1.2 4.0 5.0 1.0] • G2= [2.0 2.5 5.5 6.0] • G3= [4.5 3.0 2.5 1.0] • G4= [3.5 1.5 1.2 1.5]

12. Euclidean Distance • Distance is the square root of the sum of the squared distance between coordinates

13. City Block or Manhattan Distance • G1= [1.2 4.0 5.0 1.0] • G2= [2.0 2.5 5.5 6.0] • G3= [4.5 3.0 2.5 1.0] • G4= [3.5 1.5 1.2 1.5] • Distance is the sum of the absolute value between coordinates

14. Correlation Distance • Pearson correlation measures the degree of linear relationship between variables, [-1,1] • Distance is 1-(pearson correlation), range of [0,2]

15. Similarity Measurements • Pearson Correlation Two profiles (vectors) and +1  Pearson Correlation  – 1

16. Similarity Measurements • Cosine Correlation +1  Cosine Correlation  – 1

17. Hierarchical Clustering • IDEA: Iteratively combines genes into groups based on similar patterns of observed expression • By combining genes with genes OR genes with groups algorithm produces a dendrogram of the hierarchy of relationships. • Display the data as a heatmap and dendrogram • Cluster genes, samples or both (HCL-1)

18. Hierarchical Clustering Venn Diagram of Clustered Data Dendrogram

19. Hierarchical clustering • Merging (agglomerative): start with every measurement as a separate cluster then combine • Splitting: make one large cluster, then split up into smaller pieces • What is the distance between two clusters?

20. Distance between clusters • Single-link: distance is the shortest distance from any member of one cluster to any member of the other cluster • Complete link: distance is the longest distance from any member of one cluster to any member of the other cluster • Average: Distance between the average of all points in each cluster • Ward: minimizes the sum of squares of any two clusters

21. Hierarchical Clustering-Merging • Euclidean distance • Average linking Distance between clusters when combined Gene expression time series

22. Manhattan Distance Distance between clusters when combined • Average linking Gene expression time series

23. Correlation Distance

24. Data Standardization • Data points are normalized with respect to mean and variance, “sphering” the data • After sphering, Euclidean and correlation distance are equivalent • Standardization makes sense if you are not interested in the size of the effects, but in the effect itself • Results are misleading for noisy data

25. Distance Comments • Every clustering method is based SOLELY on the measure of distance or similarity • E.G. Correlation: measures linear association between two genes • What if data are not properly transformed? • What about outliers? • What about saturation effects? • Even good data can be ruined with the wrong choice of distance metric

26. A B C D Hierarchical Clustering Initial Data Items Distance Matrix

27. A B C D Hierarchical Clustering Initial Data Items Distance Matrix

28. A B C D Hierarchical Clustering Single Linkage Current Clusters Distance Matrix 2

29. A B C D Hierarchical Clustering Single Linkage Current Clusters Distance Matrix

30. A B C D Hierarchical Clustering Single Linkage Current Clusters Distance Matrix

31. A B C D Hierarchical Clustering Single Linkage Current Clusters Distance Matrix 3

32. A B C D Hierarchical Clustering Single Linkage Current Clusters Distance Matrix

33. A B C D Hierarchical Clustering Single Linkage Current Clusters Distance Matrix

34. A B C D Hierarchical Clustering Single Linkage Current Clusters Distance Matrix 10

35. A B C D Hierarchical Clustering Single Linkage Final Result Distance Matrix

36. Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Hierarchical Clustering

37. Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Hierarchical Clustering

38. Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Hierarchical Clustering

39. Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Hierarchical Clustering

40. Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Hierarchical Clustering

41. Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Hierarchical Clustering

42. Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Hierarchical Clustering

43. Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Hierarchical Clustering

44. Hierarchical Clustering H L

45. Hierarchical Clustering Samples Genes The Leaf Ordering Problem: • Find ‘optimal’ layout of branches for a given dendrogram architecture • 2N-1 possible orderings of the branches • For a small microarray dataset of 500 genes, there are 1.6*E150 branch configurations

46. Hierarchical Clustering The Leaf Ordering Problem:

47. Hierarchical Clustering • Pros: • Commonly used algorithm • Simple and quick to calculate • Cons: • Real genes probably do not have a hierarchical organization

48. Using Hierarchical Clustering • Choose what samples and genes to use in your analysis • Choose similarity/distance metric • Choose clustering direction • Choose linkage method • Calculate the dendrogram • Choose height/number of clusters for interpretation • Assess results • Interpret cluster structure

49. Choose what samples/genes to include • Very important step • Do you want to include housekeeping genes or genes that didn’t change in your results? • How do you handle replicates from the same sample? • Noisy samples? • Dendrogram is a mess if everything is included in large datasets • Gene screening

50. No Filtering