Transcript lecture12_05

Microarrays and Gene
Expression Analysis
Gene Expression Data
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Microarray experiments
Applications
Data analysis
Gene Expression Databases
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DNA Microrray
A microarray is a tool for analyzing gene
expression in genomic scale.
The microarray consists of a small
membrane or glass slide containing samples
of many genes arranged in a regular pattern.
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DNA Microarrays
• First introduced in 1987
– Still undergoing much development
• Small piece of glass
– Thousands/millions of cells
• Each cell contains a DNA probe
– Millions of copies bound to glass
• Cells capture their reverse complement
– Ordinary DNA/RNA base pair hybridization
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Chips or Microarrays
Two types of microarray technologies
1. Spotted Microarray
Traditionally called DNA microarrays.
2. Affymetrix-Developed at Affymetrix, Inc.
Historically called DNA chips.
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Spotted Microarray
• probe cDNA
(50~5,000 bases long)
is immobilized to a
solid surface such as
glass using robot
spotting and exposed
to a set of targets
either separately or in
a mixture.
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http://www.bio.davidson.edu/biology/courses/genomics/chip/chip.html
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Experimental Protocol
1. Identify RNA/DNA sequences of interest
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Design probes that are sequence-specific
2. Extract molecules from cell environment
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Label molecules with fluorescent dye
3. Pour solution onto microarray
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Then wash off excess molecules
4. Shine laser light onto array
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Scan for presence of fluorescent dye
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Microarray Images
One tissue
or condition
One gene
or mRNA
Original Image
Summary
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The ratio of expression is indicated by the intensity of the color
Red= High mRNA abundance in the experiment sample
Green= High mRNA abundance in the control sample
Cy3
Cy5
Cy5
Cy5
log2
Cy3
Cy3
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Expression Data Format
Conditions
Genes / mRNAs
normal
cold
uch1
gut2
fip1
msh1
vma2
meu26
git8
sec7b
apn1
wos2
-2.0
0.398
0.225
0.676
0.41
0.353
0.47
0.39
0.681
0.902
0.0
0.402
0.225
0.685
0.414
0.286
0.47
0.395
0.636
0.904
hot
0.924
-1.329
-2.151
-0.564
-1.285
-1.503
-1.088
-1.358
-0.555
-0.149
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Microarray Applications
• Identify genes whose function is related
– Similar expression in group in many cases
• Find genes expressed in specific tissues
– Different expression in different cells
• Find genes affected by environment
– Different expression under different conditions
• Distinguish different forms of a disease
– Different expression in different patients
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Microarray Applications
Specific Examples
• Evolution
Chimpanzees and Human genomes are 98.7 %
identical and therefore phylogenetic analysis can
not separated them into different
species.
Most differences between human and chimpanzees
have been detected at the level of gene expression
in the brain.
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Microarray Applications
Specific Examples
• Behaviour
Gene Expression Profiles in the Brain Predict Behavior in Individual Honey
Bees
Charles W. Whitfield,1,2 Anne-Marie Cziko,1 Gene E. Robinson1,2* We
show that the age-related transition by adult honey bees from hive work to
foraging is associated with changes in messenger RNA abundance in the brain
for 39% of 5500 genes tested. This result, discovered using a highly replicated
experimental design involving 72 microarrays, demonstrates more extensive
genomic plasticity in the adult brain than has yet been shown. Experimental
manipulations that uncouple behavior and age revealed that messenger RNA
changes were primarily associated with behavior. Individual brain messenger
RNA profiles correctly predicted the behavior of 57 out of 60 bees, indicating a
robust association between brain gene expression in the individual and naturally
occurring behavioral plasticity.
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Microarray Applications
Specific Examples
Cancer Research
Hundreds of genes
that differentiate between
cancer tissues in different
stages of the tumor were found.
These different stages
were not detected by
histological or other
clinical parameters.
Ramaswamy et al, 2003
Nat Genet 33:49-54
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Microarray Analysis
GOALS
Sample classification
- What are the set of genes that differentiate
between two or more groups of treatments
Gene Classification
- What is the set of genes that have the same
expression profile along a set of treatments
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Microarray Analysis
How do we answer them ?
• Supervised Methods
-Analysis of variance
-Discriminate analysis
-Support Vector Machine (SVM)
• Unsupervised
-Partion Methods
K-means
SOM (Self Organizing Maps
-Hierarchical Clustering
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TO BE CONTINUED
• Rest of the slides from Lecture12_05 where
moved to Lecture13_05
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