Microarrays

1. Microarrays

2. OUTLINE • Microarrays • Processing Microarray Data • K- Means Clustering • Hierarchical Clustering • SOM

3. Microarrays Gene Expression: We see difference between cels because of differential gene expression, Gene is expressed by transcribing DNA intosingle-stranded mRNA, mRNA is later translated into a protein, Microarrays measure the level of mRNA expression

4. Microarrays Gene Expression: mRNA expression represents dynamic aspects of cell, mRNA is isolated and labeled using a fluorescent material, mRNA is hybridized to the target; level of hybridization corresponds to light emission which is measured with a laser

5. Microarrays

6. Microarrays

7. Microarrays

8. Microarrays

9. Microarrays Animation (by A. Malcolm Campbell): http://www.bio.davidson.edu/Courses/genomics/chip/chip.html

10. Microarrays Sample Application (oncotypeDX): http://www.oncotypedx.com/en-US/Breast.aspx

11. Microarrays Sample Application (oncotypeDX): VIDEO http://www.oncotypedx.com/en-US/Breast.aspx

12. Processing Microarray Data Problems: Extract data from microarrays, Analyze the meaning of the multiple arrays.

13. Processing Microarray Data

14. Processing Microarray Data Differentiating gene expression: R = G not differentiated R > G  up-regulated R < G  down regulated

15. Processing Microarray Data Problems: Extract data from microarrays, Analyze the meaning of the multiple arrays.

16. Processing Microarray Data Characteristics of microarray data: Experiment = (gene1, gene2,…, geneN ) Gene = (experiment1, experiment2, …, experimentM) N is often on the order of 104 M is often on the order of 101

17. Processing Microarray Data Microarray data:

18. Processing Microarray Data Data Analysis: Clustering: What genes have similar functions, Subdivide genes or experiments into meaningful classes. Classification: Can we correctly classify an unknown experiment or gene into a known class? FOR EXAMPLE: Can we make better treatment decisions for a cancer patient based on gene expression profile?

19. Processing Microarray Data Clustering: Find classes in the data, Identify new classes, Identify gene correlations, Methods: K-means clustering, Hierarchical clustering, Self Organizing Maps (SOM)

20. Processing Microarray Data Distance Measures: Euclidean Distance: Manhattan Distance:

21. Processing Microarray Data K-means Clustering: Break the data into K clusters, Start with random partitioning, Improve it by iterating.

22. Processing Microarray Data K-means Clustering: Select # of clusters, say k, Repeat Select k random centroids, {m1, m2,…, mk}, Assign points (genes in this case) to the cluster of closest centroid by using a distance measure, Compute new centroids, {m1, m2,…, mk}, until no change to any centroid.

23. Processing Microarray Data K-means Clustering:

24. Processing Microarray Data K-means Clustering: Select # of clusters, say k, Therea are some methods to determine the optimum k, Assume k is given.

25. Processing Microarray Data K-means Clustering: Select k random centroids, {m1, m2,…, mk}, Just randomly assign the centroids.

26. Processing Microarray Data K-means Clustering: Assign points (genes in this case) to the cluster of closest centroid by using a distance measure, Use the following formula to find the closest centroid to the gene gi: Then assign gene gi to the closest centroid.

27. Processing Microarray Data K-means Clustering: Compute new centroids, {m1, m2,…, mk}, Find the average in the cluster: Where: mc: centroid of the cluster c, Nc: the number of points in cluster c, gi: the points in cluster c.

28. Processing Microarray Data K-means Clustering: Repeat until no change to any centroid. Centroids are in the proper places, We can not observe any other improvement in centroids, Therefore STOP.

29. Processing Microarray Data K-means Clustering DEMO: Our points:

30. Processing Microarray Data K-means Clustering DEMO: Centroids (4 centroids, squares):

31. Processing Microarray Data K-means Clustering DEMO: Assign each point to the closest centroid:

32. Processing Microarray Data K-means Clustering DEMO: Re evaluate the centroids:

33. Processing Microarray Data K-means Clustering DEMO: Iterate until no change.

34. Processing Microarray Data K-means Clustering DEMO 2:

35. Processing Microarray Data Hierarchical Clustering: Similar to costruction of phylogenetic tree, A distance matrix for all genes are constructed based on distances between their expression profiles. Neighbor-joining or UPGMA can be applied on this matrix to get a hierarchical cluster. Single linkage, complete linkage, average linkage clustering

36. Processing Microarray Data Hierarchical Clustering: Single linkage: the distance between two clusters is given by the value of the shortest link between the clusters

37. Processing Microarray Data Hierarchical Clustering: Complete linkage: the distance between two clusters is given by the value of the longest link between the clusters

38. Processing Microarray Data Hierarchical Clustering: Average linkage: the distance between two clusters is defined as the average of distances between all pairs of objects like UPGMA

39. Processing Microarray Data Hierarchical Clustering: Linkage Criteria:

40. Processing Microarray Data Agglomerative Hierarchical Clustering:

41. Processing Microarray Data Agglomerative Hierarchical Clustering:

42. Processing Microarray Data Agglomerative Hierarchical Clustering DEMO:

43. Processing Microarray Data Agglomerative Hierarchical Clustering DEMO:

44. Processing Microarray Data Agglomerative Hierarchical Clustering DEMO:

45. Processing Microarray Data Agglomerative Hierarchical Clustering DEMO:

46. Processing Microarray Data Agglomerative Hierarchical Clustering DEMO:

47. Processing Microarray Data Agglomerative Hierarchical Clustering DEMO:

48. Processing Microarray Data Agglomerative Hierarchical Clustering DEMO:

49. Processing Microarray Data Agglomerative Hierarchical Clustering:

50. Processing Microarray Data Agglomerative Hierarchical Clustering: