Tutorial 7

1. Tutorial 7 Gene expression analysis

2. Gene expression analysis • How to interpret an expression matrix • Expression data DBs - GEO • General clustering methods Unsupervised Clustering • Hierarchical clustering • K-means clustering • Tools for clustering - EPCLUST • Functional analysis - Go annotation

3. Gene expression data sources Microarrays RNA-seq experiments

4. How to interpret an expression data matrix • Each column represents all the gene expression levels from: • In two-color array:from a single experiment. • In one-color array: from a single sample. • Each row represents the expression of a gene across all experiments.

5. How to interpret an expression data matrix Each element is a log ratio: • In two-color array:log2 (T/R). T - the gene expression level in the testing sample R - the gene expression level in the reference sample • In one-colorarray: log2(X) X - the gene expression level in the current sample

6. How to interpret an expression data matrix In two-color array: Scale In one-color array: Scale Bright green indicates a high expression value Red indicates a positive log ratio: T>R Black indicates a log ratio of zero: T=~R Green indicates a positive log ratio: T>R Black indicates no expression

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

8. How to analyze gene expression data

9. Expression profiles DBs • GEO (Gene Expression Omnibus) http://www.ncbi.nlm.nih.gov/geo/ • Human genome browser http://genome.ucsc.edu/ • ArrayExpress http://www.ebi.ac.uk/arrayexpress/

10. The current rate of submission and processing is over 10,000 Samples per month. In 2002 Nature journals announce requirement for microarray data deposit to public databases.

11. Searching for expression profiles in the GEO http://www.ncbi.nlm.nih.gov/geo/

12. GEO accession IDs GPL**** - platform ID GSM**** - sample ID GSE**** - series ID GDS**** - dataset ID • A Series record denes a set of related Samples considered to be part of a group. • A GDS record represents a collection of biologically and statistically comparable GEO samples. Not every experiment has a GDS.

13. Clustering Statistic analysis Download dataset

14. Clustering analysis

15. Clustering analysis – zoom in

16. Clustering analysis – zoom in

18. Viewing the expression levels

19. Viewing the expression levels

21. Clustering Grouping together “similar” genes

22. Clustering • Unsupervised learning: The classes are unknown a priori and need to be “discovered” from the data. • Supervised learning: The classes are predefined and the task is to understand the basis for the classification from a set of labeled objects. This information is then used to classify future observations. http://www.bioconductor.org/help/course-materials/2002/Seattle02/Cluster/cluster.pdf

23. Unsupervised Clustering • Hierarchical methods - These methods provide a hierarchy of clusters, from the smallest, where all objects are in one cluster, through to the largest set, where each observation is in its own cluster. • Partitioning methods - These usually require the specification of the number of clusters. Then a mechanism for apportioning objects to clusters must be determined. http://www.bioconductor.org/help/course-materials/2002/Seattle02/Cluster/cluster.pdf

24. Hierarchical Clustering This clustering method is based on distances between expression profiles of different genes. Genes with similar expression patterns are grouped together.

25. Rings a bell?... • In both phylogenetic trees and in clustering we create a tree based on distances matrix. • When computing phylogenetic trees: We compute distances between sequences. • When computing clustering dendogramswe compute distances between expression values. ATCTGTCCGCTCG ATGTGTGCGCTTG Score Score

26. How to determine the similarity between two genes? Patrik D'haeseleer, How does gene expression clustering work?, Nature Biotechnology23, 1499 - 1501 (2005) , http://www.nature.com/nbt/journal/v23/n12/full/nbt1205-1499.html

27. Hierarchical clustering methods produce a tree or a dendrogram. They avoid specifying how many clusters are appropriate by providing a partition for each K. The partitions are obtained from cutting the tree at different levels. 2 clusters 4 clusters 6 clusters

28. The more clusters you want the higher the similarity is within each cluster. http://discoveryexhibition.org/pmwiki.php/Entries/Seo2009

29. Hierarchical clustering results http://www.spandidos-publications.com/10.3892/ijo.2012.1644

30. Unsupervised Clustering – K-means clustering An algorithm to classify the data into K number of groups. K=4

31. How does it work? 1 2 3 4 The centroid of each of the k clusters becomes the new means. 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 Steps 2 and 3 are repeated until convergence has been reached. The algorithm iteratively divides the genes into K groups and calculates the center of each group. The results are the optimal groups (center distances) for K clusters.

32. How should we determine K? • Trial and error • Take K as square root of gene number

33. Tool for clustering - EPclust http://www.bioinf.ebc.ee/EP/EP/EPCLUST/

35. Choose distance metric Choose algorithm

36. Hierarchical clustering

37. Zoom in by clicking on the nodes

39. K-means clustering K-means clustering

40. Samples found in cluster Graphical representation of the cluster Graphical representation of the cluster

41. 10 clusters, as requested

42. Now what? Now that we have clusters – we want to know what is the function of each group. There is a need for some kind of generalization for gene functions.

43. Gene Ontology (GO) http://www.geneontology.org/ The Gene Ontology project provides an ontology of defined terms representing gene product properties. The ontology covers three domains:

44. Gene Ontology (GO) Cellular Component (CC) - the parts of a cell or its extracellular environment. Molecular Function (MF) -the elemental activities of a gene product at the molecular level, such as binding or catalysis. Biological Process (BP) - operations or sets of molecular events with a defined beginning and end, pertinent to the functioning of integrated living units: cells, tissues, organs, and organisms.

45. The GO tree

46. GO sources ISS Inferred from Sequence/Structural Similarity IDA Inferred from Direct Assay IPI Inferred from Physical Interaction TAS Traceable Author Statement NAS Non-traceable Author Statement IMP Inferred from Mutant Phenotype IGI Inferred from Genetic Interaction IEP Inferred from Expression Pattern IC Inferred by Curator ND No Data available IEA Inferred from electronic annotation

48. DAVID  http://david.abcc.ncifcrf.gov/ Functional Annotation Bioinformatics Microarray Analysis • Identify enriched biological themes, particularly GO terms • Discover enriched functional-related gene/protein groups • Cluster redundant annotation terms • Explore gene names in batch 

49. annotation classification ID conversion

50. Functional annotation Genes from your list involved in this category Upload Charts for each category Charts for each category Charts for each category