Tutorial 7

Tutorial 7

Gene expression analysis

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Gene expression analysis• Expression data

– GEO– UCSC– ArrayExpress

• General clustering methods– Unsupervised Clustering

• Hierarchical clustering• K-means clustering

• Tools for clustering– EPCLUST– Mev

• Functional analysis– Go annotation

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Gene expression data sources

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Microarrays RNA-seq experiments

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.

Exp1 Exp 2 Exp3 Exp4 Exp5 Exp6

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 -1.1 -1 0.1

Gene 4 2.9 2.6 2.5 -2.3 -0.1 -2.3

Gene 5 0.1 2.6 2.2 2.7 -2.1

Gene 6 -2.9 -1.9 -2.4 -0.1 -1.9 2.9

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Expression Data Matrix

Each element is a log ratio: log2 (T/R). T - the gene expression level in the testing sample

R - the gene expression level in the reference sample

Exp1 Exp 2 Exp3 Exp4 Exp5 Exp6

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 -1.1 -1 0.1

Gene 4 2.9 2.6 2.5 -2.3 -0.1 -2.3

Gene 5 0.1 2.6 2.2 2.7 -2.1

Gene 6 -2.9 -1.9 -2.4 -0.1 -1.9 2.9

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Expression 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

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Exp

Log

ratio

Exp

Log

ratio

Microarray Data:Different representations

T<R

T>R

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How to search for expression profiles

• GEO (Gene Expression Omnibus)http://www.ncbi.nlm.nih.gov/geo/

• Human genome browserhttp://genome.ucsc.edu/

• ArrayExpresshttp://www.ebi.ac.uk/arrayexpress/

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Datasets - suitable for analysis with GEO tools

Expression profiles by gene

Microarray experiments

Probe sets

Groups of related microarray experiments

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Searching for expression profiles in the GEO

Download dataset

Clustering

Statistic analysis

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Clustering analysis

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Download dataset

Clustering

Statistic analysis

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The expression distribution for different lines in the cluster

Searching for expression profiles in the Human Genome browser.

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Keratine 10 is highly expressed

in skin

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http://www.ebi.ac.uk/arrayexpress/

ArrayExpress

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How to analyze gene expression data

Unsupervised Clustering - Hierarchical Clustering

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genes with similar expression patterns are grouped together and are connected by a series of branches (dendrogram).

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352 4

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35 2 4

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Leaves (shapes in our case) represent genes and the length of the paths between leaves represents the distances between genes.

Hierarchical Clustering

How to determine the similarity between two genes? (for clustering)

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

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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

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An algorithm to classify the data into K number of groups.

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K=4

Unsupervised Clustering – K-means clustering

How does it work?

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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.

1 2 3 4

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.

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How should we determine K?

•Trial and error•Take K as square root of gene number

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http://www.bioinf.ebc.ee/EP/EP/EPCLUST/

Tools for clustering - EPclust

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Edit the input matrix: Transpose,Normalize,Randomize

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Hierarchical clustering

K-means clustering

In the input matrix each column should represents a gene and each row should represent an experiment (or individual).

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Hierarchical clustering

In the input matrix each column should represents a gene and each row should represent an experiment (or individual).

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Clusters

Data

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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

Graphical representation of the

cluster

Samples found in cluster

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10 clusters, as requested

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http://www.tm4.org/mev/

Tools for clustering - MeV

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1007_s_at1053_at117_at121_at1255_g_at1294_at1316_at1320_at1405_i_at1431_at1438_at1487_at1494_f_at1598_g_at

What can we learn from clusters?

Gene expression function analysis

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:

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• 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.

Gene Ontology (GO)

The GO tree

GO sources

ISS Inferred from Sequence/Structural SimilarityIDA Inferred from Direct AssayIPI Inferred from Physical InteractionTAS Traceable Author StatementNAS Non-traceable Author StatementIMP Inferred from Mutant PhenotypeIGI Inferred from Genetic InteractionIEP Inferred from Expression PatternIC Inferred by CuratorND No Data availableIEA Inferred from electronic annotation

Search by AmiGO

Results for alpha-synuclein

DAVID

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

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

ID conversion

annotation

classification

Functional annotationUpload

Annotation options

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Gene expression analysis• Expression data

– GEO– UCSC– ArrayExpress

• General clustering methods– Unsupervised Clustering

• Hierarchical clustering• K-means clustering

• Tools for clustering– EPCLUST– Mev

• Functional analysis– Go annotation

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