Gene Expression 1

Methods –Unsupervised Clustering Hierarchical clustering K-means clustering Expression data –GEO –UCSC EPCLUST 2

Microarray - Reminder 3

Expression Data Matrix Each column represents all the gene expression levels from a single experiment. Each row represents the expression of a gene across all experiments. Exp1Exp 2Exp3Exp4Exp5Exp6 Gene Gene Gene Gene Gene Gene

Expression Data Matrix Each element is a log ratio: log 2 (T/R). T - the gene expression level in the testing sample R - the gene expression level in the reference sample Exp1Exp 2Exp3Exp4Exp5Exp6 Gene Gene Gene Gene Gene Gene

Microarray Data Matrix Black indicates a log ratio of zero, i.e. T=~R Green indicates a negative log ratio, i.e. T<R Red indicates a positive log ratio, i.e. T>R Grey indicates missing data 6

Exp Log ratio Exp Log ratio Microarray Data: Different representations T<R T>R 7

Microarray Data: Clusters 8

How to determine the similarity between two genes? (for clustering) Patrik D'haeseleer, How does gene expression clustering work?, Nature Biotechnology 23, (2005), 9

Microarray Data: Clustering Hierarchical Clustering 10

Hierarchical Clustering: genes with similar expression patterns are grouped together and are connected by a series of branches (dendrogram). Microarray Data: Clustering Leaves (the shapes in our case) represent genes and the length of the paths between leaves represents the distances between genes. Similar genes lie within the same sub-trees.

12 If we want a certain number of clusters we need to cut the tree at a level indicates that number (in this case - four). Hierarchical clustering finds an entire hierarchy of clusters.

Hierarchical clustering result 13 Five clusters

Microarray Data: Clustering K-mean clustering is an algorithm to classify the data into K number of groups. 14 K=4

Microarray Data: Clustering How? 15 The algorithm divides iteratively the genes into K groups and calculates the center of each group. The results are the optimal groups (center distances) for K clusters k initial "means" (in this casek=3) are randomly selected from the data set (shown in color). k clusters are created by associating every observation with the nearest mean The centroid of each of the k clusters becomes the new means. Steps 2 and 3 are repeated until convergence has been reached.

16 Different types of clustering – different results

17 How to search for expression profiles GEO (Gene Expression Omnibus) Human genome browser

Like Series, but further curated and suitable for analysis with GEO tools Expression profiles by gene Microarray experiments Probe sets Groups of related microarray experiments 18 Searching for expression profiles in the GEO

Download dataset Clustering Statistic analysis 19

20 The expression distribution for different lines in the cluster

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Searching for expression profiles in the Human Genome browser. 22

Keratine 10 is highly expressed in skin 23

24 What can we do with all the expression profiles? Clusters! How? EPCLUST

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Edit the input matrix: Transpose,Normalize,Randomize 30 Hierarchical clustering K-means clustering In the input matrix each column should represents a gene and each row should represent an experiment (or individual).

Graphical representation of the cluster Samples found in cluster 31

32 Initial seeds Final seeds

10 clusters, as requested 33

Gene Expression Methods –Unsupervised Clustering Hierarchical clustering K-means clustering Expression data –GEO –UCSC EPCLUST 34

–last day to decided on a project! 18,23,24/1- Presenting a proposed project in small groups A very short presentation (Max 5 minutes) Title- Background Main question Major tools you are planning to use to answer the questions 6.3 Final submission FINAL PROJECT- Key dates