Introduction to Bioinformatics - Tutorial no. 12

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Presentation transcript:

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

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

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

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

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

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

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?

K-Means Algorithm Randomly Initialize Clusters

K-Means Algorithm Assign data points to nearest clusters

K-Means Algorithm Recalculate Clusters

K-Means Algorithm Recalculate Clusters

K-Means Algorithm Repeat

K-Means Algorithm Repeat

K-Means Algorithm Repeat … until convergence

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

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

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

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

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

SGD – Expression database

SGD – Expression database

SGD – Expression database

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

+ - 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

+ - 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