Gene expression analysis Tutorial 7 Gene expression analysis

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

Gene expression data sources Microarrays RNA-seq experiments

How to interpret an expression data matrix Exp1 /Sample 1 Exp2 /Sample 2 Exp3 /Sample 3 Exp4 /Sample 4 Exp5 /Sample 5 Exp6 /Sample 6 Gene 1 -1.2 -2.1 -3 -1.5 1.8 2.9 Gene 2 2.7 0.2 -1.1 1.6 -2.2 -1.7 Gene 3 -2.5 1.5 -0.1 -1 0.1 Gene 4 2.6 2.5 -2.3 Gene 5 2.2 Gene 6 -2.9 -1.9 -2.4 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.

How to interpret an expression data matrix Exp1 /Sample 1 Exp2 /Sample 2 Exp3 /Sample 3 Exp4 /Sample 4 Exp5 /Sample 5 Exp6 /Sample 6 Gene 1 -1.2 -2.1 -3 -1.5 1.8 2.9 Gene 2 2.7 0.2 -1.1 1.6 -2.2 -1.7 Gene 3 -2.5 1.5 -0.1 -1 0.1 Gene 4 2.6 2.5 -2.3 Gene 5 2.2 Gene 6 -2.9 -1.9 -2.4 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-color array: log2(X) X - the gene expression level in the current sample

How to interpret an expression data matrix In two-color array: Scale In one-color array: Scale Red indicates a positive log ratio: T>R Bright green indicates a high expression value Black indicates a log ratio of zero: T=~R Green indicates a positive log ratio: T>R Black indicates no expression Expr.1 Expr.2 Expr.3 Expr.4 Expr.5 Expr.6 Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Samp 1 Samp 2 Samp 3 Samp 4 Samp 5 Samp 6 Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6

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

How to analyze gene expression data

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/

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.

Searching for expression profiles in the GEO http://www.ncbi.nlm.nih.gov/geo/ *further curated= statistically comparable datasets

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.

Clustering Statistic analysis Download dataset

Clustering analysis

Clustering analysis – zoom in

Clustering analysis – zoom in

Viewing the expression levels

Viewing the expression levels

Grouping together “similar” genes Clustering Grouping together “similar” genes

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

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

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

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 dendograms we compute distances between expression values. Expr.1 Expr.2 Expr.3 Expr.4 Expr.5 Expr.6 Gene 1 Gene 2 ATCTGTCCGCTCG ATGTGTGCGCTTG Score Score

How to determine the similarity between two genes? Patrik D'haeseleer, How does gene expression clustering work?, Nature Biotechnology 23, 1499 - 1501 (2005) , http://www.nature.com/nbt/journal/v23/n12/full/nbt1205-1499.html

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

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

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

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

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.

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

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

Choose distance metric Choose algorithm

Hierarchical clustering

Zoom in by clicking on the nodes

K-means clustering K-means clustering

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

10 clusters, as requested

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.

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:

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.

The GO tree

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

DAVID http://david.abcc.ncifcrf.gov/   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 

annotation classification ID conversion

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

Minimum number of genes for corresponding term Maximum EASE score/ E-value Genes from your list involved in this category Genes from your list involved in this category Enriched terms associated with your genes Source of term E-Value

A group of terms having similar biological meaning due to sharing similar gene members

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