Introduction to Bioinformatics - Tutorial no. 12

1. Introduction to Bioinformatics - Tutorial no. 12 Expression Data Analysis: - Clustering - GEO - EPClust

2. Application of Microarrays • We only know the function of about 20% of the 30,000 genes in the Human Genome • Gene exploration • Faster and better • Applications: • Evolution • Behavior • Cancer Research

3. Microarray Analysis • Unsupervised Grouping: Clustering • Pattern discovery via grouping similarly expressed genes together • Three techniques most often used • k-Means Clustering • Hierarchical Clustering • Kohonen Self Organizing Feature Maps

4. Hierarchical Agglomerative Clustering Michael Eisen, 1998 • Cluster (algorithm) • TreeView (visualization) • Hierarchical Agglomerative Clustering • Step 1: Similarity score between all pairs of genes • Pearson Correlation • Euclidean distance • Step 2: Find the two most similar genes, replace with a node that contains the average • Builds a tree of genes • Step 3: Repeat

5. 2 4 5 3 1 1 3 2 4 5 Need to define the distance between thenew cluster and the other clusters. Single Linkage: distance between closest pair. Complete Linkage: distance between farthest pair. Average Linkage: average distance between all pairs or distance between cluster centers Agglomerative Hierarchical Clustering Distance between joined clusters The dendrogram induces a linear ordering of the data points Dendrogram

6. Results of Clustering Gene Expression • CLUSTER is simple and easy to use • De facto standard for microarray analysis • Limitations: • Hierarchical clustering in general is not robust • Genes may belong to more than one cluster

7. K-Means Clustering Algorithm • Randomly initialize k cluster means • Iterate: • Assign each genes to the nearest cluster mean • Recompute cluster means • Stop when clustering converges Notes: • Really fast • Genes are partitioned into clusters • How do we select k?

8. K-Means Algorithm • Randomly Initialize Clusters

9. K-Means Algorithm • Assign data points to nearest clusters

10. K-Means Algorithm • Recalculate Clusters

11. K-Means Algorithm • Recalculate Clusters

12. K-Means Algorithm • Repeat

13. K-Means Algorithm • Repeat

14. K-Means Algorithm • Repeat … until convergence

16. EPClust Input (1) Expression data matrix Extra annotation for gene rows Method of tabulation Name for further analysis

17. EPClust Input (2) Method of measuring distance between gene rows Cluster hierarchically Number k of means Cluster into k means

18. GEO: Gene Expression Omnibus • NCBI database for gene expression data • Founded at end of 2000

20. Querying GEO Browse records Search for entries containing a gene Search for experiments Search with Entrez

21. SGD – Expression database http://db.yeastgenome.org/cgi-bin/expression/expressionConnection.pl

22. SGD – Expression database

23. SGD – Expression database

24. SGD – Expression database

25. Two labs are running experiments on the APO1 gene. Suggest a method that would allow them to compare their results. • Gene grouping • Relative values

26. Explain how microarrays can be used as a basis for diagnostic

27. Explain how microarrays can be used as a basis for diagnostic