Transcript Gene expression analysis

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
Microarrays
RNA-seq experiments
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Expression Data Matrix
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
-2.4
-0.1
-1.9
2.9
-1.9
• Each column represents all the gene expression levels from a
single experiment.
• Each row represents the expression of a gene across all
experiments.
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Expression Data Matrix
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
-2.4
-0.1
-1.9
2.9
-1.9
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
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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
Grey indicates missing data
Red indicates a positive log
ratio, i.e. T>R
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Microarray Data:
Different representations
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T>R
2
1
0
-1
1
2
3
4
5
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Log ratio
Log ratio
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-2
T<R
-3
-4
Exp
Exp
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How to search for expression profiles
• 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/
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Searching for expression profiles in the GEO
Datasets - suitable for
analysis with GEO tools
Expression profiles
by gene
Probe sets
Microarray
experiments
Groups of related microarray
experiments
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Clustering
Statistic
analysis
Download dataset
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Clustering analysis
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Clustering
Statistic
analysis
Download dataset
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The expression distribution for different lines
in the cluster
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Searching for expression profiles in the Human
Genome browser.
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Keratine 10 is
highly expressed
in skin
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ArrayExpress
http://www.ebi.ac.uk/arrayexpress/
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How to analyze gene expression data
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Unsupervised Clustering - Hierarchical Clustering
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Hierarchical Clustering
genes with similar expression patterns are grouped together and are
connected by a series of branches (dendrogram).
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1
2
6
3
3
5
4
5
6
2
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Leaves (shapes in our case) represent genes and the length of the paths
between leaves represents the distances between genes.
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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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Hierarchical clustering finds an entire hierarchy of clusters.
If we want a certain number of clusters we need to cut the tree
at a level indicates that number (in this case - four).
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Hierarchical clustering result
Five clusters
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Unsupervised Clustering – K-means clustering
An algorithm to classify the data into K number of groups.
K=4
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How does it work?
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k initial "means" (in
this casek=3) are
randomly selected
from the data set
(shown in color).
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k clusters are created by
associating every
observation with the
nearest mean
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4
The centroid of each
of the k clusters
becomes the new
means.
Steps 2 and 3 are repeated
until convergence has
been reached.
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.
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How should we determine K?
•Trial and error
•Take K as square root of gene number
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Tools for clustering - EPclust
http://www.bioinf.ebc.ee/EP/EP/EPCLUST/
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In the input matrix each column should represents a gene
and each row should represent an experiment (or
individual).
Hierarchical clustering
Edit the input matrix:
Transpose,Normalize,Randomize
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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).
Hierarchical clustering
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Data
Clusters
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In the input matrix each column should represents a gene
and each row should represent an experiment (or
individual).
K-means clustering
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Samples found in cluster
Graphical
representation of the
cluster
Graphical
representation of the
cluster
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10 clusters, as
requested
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Tools for clustering - MeV
http://www.tm4.org/mev/
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Gene expression function analysis
1007_s_at
1053_at
117_at
121_at
1255_g_at
1294_at
1316_at
1320_at
1405_i_at
1431_at
1438_at
1487_at
1494_f_at
1598_g_at
What can we learn from clusters?
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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.
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The GO tree
GO sources
ISS
IDA
IPI
TAS
NAS
IMP
IGI
IEP
IC
ND
IEA
Inferred from Sequence/Structural Similarity
Inferred from Direct Assay
Inferred from Physical Interaction
Traceable Author Statement
Non-traceable Author Statement
Inferred from Mutant Phenotype
Inferred from Genetic Interaction
Inferred from Expression Pattern
Inferred by Curator
No Data available
Inferred from electronic annotation
Search by AmiGO
Results for alpha-synuclein
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
Upload
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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