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**Introduction to Bioinformatics - Tutorial no. 12**

Expression Data Analysis: - Clustering - GEO - EPClust

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**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

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**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

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**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

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**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

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**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

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**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?

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K-Means Algorithm Randomly Initialize Clusters

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K-Means Algorithm Assign data points to nearest clusters

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K-Means Algorithm Recalculate Clusters

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K-Means Algorithm Recalculate Clusters

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K-Means Algorithm Repeat

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K-Means Algorithm Repeat

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K-Means Algorithm Repeat … until convergence

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**EPClust Input (1) Expression data matrix**

Extra annotation for gene rows Method of tabulation Name for further analysis

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**EPClust Input (2) Method of measuring distance between gene rows**

Cluster hierarchically Number k of means Cluster into k means

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**GEO: Gene Expression Omnibus**

NCBI database for gene expression data Founded at end of 2000

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**Querying GEO Browse records Search for entries containing a gene**

Search for experiments Search with Entrez

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**SGD – Expression database**

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**SGD – Expression database**

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**SGD – Expression database**

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**SGD – Expression database**

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**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

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**+ - 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

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**+ - 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

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