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. Agglomerative Hierarchical Clustering
Need to define the distance between the new 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 5 2 4 3 1 4 2 5 1 3
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

22. SGD – Expression database

23. SGD – Expression database

24. SGD – Expression database

25. Gene grouping Relative values
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
Sample 1
Sample 2
Sample 3
sample4
Sample 5
Gen1 + -
Gen2
Gen3
Gen4
Gen5

27. + - Explain how microarrays can be used as a basis for diagnostic
Sample 1
Sample 2
sample4
Sample 3
Sample 5
Gen1 + -
Gen2
Gen3
Gen4
Gen5