Transcript Tutorial_7 (2016) - Gene Expressionx

Tutorial 8
Gene expression analysis
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Gene expression analysis
• How to interpret an expression
matrix
• Expression data DBs - GEO
• Clustering
– Hierarchical clustering
– K-means clustering
– Tools for clustering - EPCLUST
• Functional analysis
– Go annotation
– DAVID
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Gene expression data sources
Microarrays
RNA-seq experiments
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How to interpret an expression data matrix
Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 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
-1.1
-1
0.1
Gene 4
2.9
2.6
2.5
-2.3
-0.1
-2.3
Gene 5
0.1
1.9
2.6
2.2
2.7
-2.1
Gene 6
-2.9
-1.9
-2.4
-0.1
-1.9
2.9
• Each column represents all the gene expression levels from a
single sample.
• Each row represents the expression of a gene across all
experiments.
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Raw data pre-processing
Raw data – the data values that we get from the
microarray/ sequencer. Raw values are a general
term used for the raw measurements made by an
instrument.
In microarrays the raw data is probe intensities.
In sequencing the raw data is counts per gene.
• Raw data will almost always need to undergo some
kind of processing in order to be in adequate
quality and have a biological meaning.
– For example high throughput sequencing raw data are
the sequenced reads. They need to get mapped to the
genome, possibly filtered, and then variant calling is
done.
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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/
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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.
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Searching for expression profiles in the GEO
http://www.ncbi.nlm.nih.gov/geo/
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GEO accession IDs
GPL**** - platform ID
GSM**** - sample ID
GSE**** - series ID
GDS**** - dataset ID
• A Series record defines 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.
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Clustering
Statistical
analysis
Download dataset
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Raw data (soft file)
...
...
...
...
Probes
Genes
Expression values
per sample (GSM)
Gene annotations
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Clustering analysis
Zoom
in
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Clustering analysis – zoom in
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Clustering analysis – zoom in
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Viewing the expression levels
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Viewing the expression levels
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Clustering
Grouping together genes
with a similar signature
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Hierarchical Clustering
This clustering method is based on distances between
expression profiles of different genes. Genes with
similar expression patterns are grouped together.
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Rings a bell?...
• In both phylogenetic trees and in clustering we
create a tree based on distance matrix.
• When computing phylogenetic trees: We compute
distances between sequences.
• When computing clustering dendograms we
compute distances between expression values.
ATCTGTCCGCTCG
ATGTGTGCGCTTG
Score
Expr.1 Expr.2 Expr.3 Expr.4 Expr.5 Expr.6
Gene 1
Gene 2
Score
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Hierarchical clustering methods produce a tree
or a dendrogram.
They avoid specifying how many clusters are
appropriate. The partitions are obtained from
cutting the tree at different levels.
2 clusters
4 clusters
6 clusters
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The more clusters you want
the higher the similarity is
within each cluster.
http://discoveryexhibition.org/pmwiki.php23
/Entries/Seo2009
Hierarchical clustering results
You can cluster both samples and genes (separately)
http://www.spandidospublications.com/10.3892/ijo.2012.1644
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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 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.
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How should we determine K?
• Trial and error
• Take K as square root of gene number
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Tool for clustering - EPclust
http://www.bioinf.ebc.ee/EP/EP/EPCLUST/
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Choose distance metric
Choose algorithm
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Hierarchical clustering
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Zoom in by clicking on the nodes
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K-means clustering
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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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.
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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:
• Biological process
• Cellular component
• Molecular function
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 – a partial example
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
annotation
ID
conversion
Functional annotation - upload
Gene list you want to
explore (for example all
the genes in a certain
cluster)
What is the identifier?
(probes/ gene names/
gene IDs)
You can supply a
background list as well
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Functional annotation - results
Different kinds of
enrichments are
calculated
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Functional annotation - results
Charts for
each category
Genes from
your list
involved in
this category
Minimum number
of genes for
corresponding term
Source of term
Enriched terms
associated with
your genes
Maximum EASE
score/ E-value
Genes from your
list involved in this
category
P-Value
Adjusted
P-Value
Gene expression analysis
• How to interpret an expression
matrix
• Expression data DBs - GEO
• Clustering
– Hierarchical clustering
– K-means clustering
– Tools for clustering - EPCLUST
• Functional analysis
– Go annotation
– DAVID
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