Transcript Microarray

Gene expression:
Microarray data analysis
Outline: microarray data analysis
Gene expression
Microarrays
Preprocessing
normalization
scatter plots
Inferential statistics
t-test
ANOVA
Exploratory (descriptive) statistics
distances
clustering
principal components analysis (PCA)
Compare gene expression
in this cell type…
…after viral
infection
…relative
to a knockout
…in samples
from patients
…after drug
treatment
…at a later
developmental time
…in a different
body region
Gene expression is context-dependent,
and is regulated in several basic ways
• by region (e.g. brain versus kidney)
• in development (e.g. fetal versus adult tissue)
• in dynamic response to environmental signals
(e.g. immediate-early response genes)
• in disease states
• by gene activity
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UniGene: unique genes via ESTs
• Find UniGene at NCBI:
www.ncbi.nlm.nih.gov/UniGene
• UniGene clusters contain many ESTs
• UniGene data come from many cDNA libraries.
Thus, when you look up a gene in UniGene
you get information on its abundance
and its regional distribution.
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Outline: microarray data analysis
Gene expression
Microarrays
Preprocessing
normalization
scatter plots
Inferential statistics
t-test
ANOVA
Exploratory (descriptive) statistics
distances
clustering
principal components analysis (PCA)
Microarrays: tools for gene expression
A microarray is a solid support (such as a membrane
or glass microscope slide) on which DNA of known
sequence is deposited in a grid-like array.
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Microarrays: tools for gene expression
The most common form of
microarray is used to measure
gene expression. RNA is isolated
from matched samples
of interest. The RNA is typically
converted to cDNA,
labeled with fluorescence (or
radioactivity), then hybridized
to microarrays in order to
measure the expression levels
of thousands of genes.
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Advantages of microarray experiments
Fast
Data on >20,000 transcripts in ~2 weeks
Comprehensive
Entire yeast or mouse genome on a chip
Flexible
Custom arrays can be made
to represent genes of interest
Easy
Submit RNA samples to a core facility
Cheap?
Chip representing 20,000 genes for $300
Table 6-4
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Disadvantages of microarray experiments
Cost
■ Some researchers can’t afford to do
appropriate numbers of controls, replicates
RNA
■ The final product of gene expression is protein
significance ■ “Pervasive transcription” of the genome is
poorly understood (ENCODE project)
■ There are many noncoding RNAs not yet
represented on microarrays
Quality
control
■ Impossible to assess elements on array surface
■ Artifacts with image analysis
■ Artifacts with data analysis
■ Not enough attention to experimental design
■ Not enough collaboration with statisticians
Table 6-5
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Sample
acquisition
Data
acquisition
Data
analysis
Data
confirmation
Biological insight
Fig. 6.16
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Stage 1: Experimental design
Stage 2: RNA and probe preparation
Stage 3: Hybridization to DNA arrays
Stage 4: Image analysis
Stage 5: Microarray data analysis
Stage 6: Biological confirmation
Stage 7: Microarray databases
Fig. 6.16
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Stage 1: Experimental design
[1] Biological samples: technical and biological replicates:
determine the data analysis approach at the outset
[2] RNA extraction, conversion, labeling, hybridization:
except for RNA isolation, routinely performed at core facilities
[3] Arrangement of array elements on a surface:
randomization can reduce spatially-based artifacts
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One sample per array
(e.g. Affymetrix or radioactivity-based platforms)
Sample 1
Sample 2
Sample 3
Fig. 6.17
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Two samples per array (competitive hybridization)
Samples 1,2
Samples 1,3
Sample 1, pool Sample 2, pool
Samples 2,3
Samples 2,1:
switch dyes
Fig. 6.17
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Stage 2: RNA preparation
For Affymetrix chips, need total RNA (about 5 ug)
Confirm purity by running agarose gel
Measure a260/a280 to confirm purity, quantity
One of the greatest sources of error in microarray
experiments is artifacts associated with RNA isolation;
be sure to create an appropriately balanced,
randomized experimental design.
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Stage 3: Hybridization to DNA arrays
The array consists of cDNA or oligonucleotides
Oligonucleotides can be deposited by photolithography
The sample is converted to cRNA or cDNA
(Note that the terms “probe” and “target” may refer to the
element immobilized on the surface of the microarray, or
to the labeled biological sample; for clarity, it may be
simplest to avoid both terms.)
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Microarrays: array surface
Southern et al. (1999) Nature Genetics, microarray supplement
Fig. 6.18
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Stage 4: Image analysis
RNA transcript levels are quantitated
Fluorescence intensity is measured with a scanner,
or radioactivity with a phosphorimager
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Differential Gene Expression on a cDNA Microarray
Control
Rett
a B Crystallin
is over-expressed
in Rett Syndrome
Fig. 6.19
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Fig. 6.20
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Stage 5: Microarray data analysis
Hypothesis testing
• How can arrays be compared?
• Which RNA transcripts (genes) are regulated?
• Are differences authentic?
• What are the criteria for statistical significance?
Clustering
• Are there meaningful patterns in the data (e.g. groups)?
Classification
• Do RNA transcripts predict predefined groups, such as
disease subtypes?
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Stage 6: Biological confirmation
Microarray experiments can be thought of as
“hypothesis-generating” experiments.
The differential up- or down-regulation of specific RNA
transcripts can be measured using independent assays
such as
-- Northern blots
-- polymerase chain reaction (RT-PCR)
-- in situ hybridization
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Stage 7: Microarray databases
There are two main repositories:
Gene expression omnibus (GEO) at NCBI
ArrayExpress at the European Bioinformatics Institute
(EBI)
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MIAME
In an effort to standardize microarray data presentation
and analysis, Alvis Brazma and colleagues at 17
institutions introduced Minimum Information About a
Microarray Experiment (MIAME). The MIAME
framework standardizes six areas of information:
►experimental design
►microarray design
►sample preparation
►hybridization procedures
►image analysis
►controls for normalization
Visit http://www.mged.org
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Outline: microarray data analysis
Gene expression
Microarrays
Preprocessing
normalization
scatter plots
Inferential statistics
t-test
ANOVA
Exploratory (descriptive) statistics
distances
clustering
principal components analysis (PCA)
Microarray data analysis
• begin with a data matrix (gene expression values
versus samples)
genes
(RNA
transcript
levels)
Fig. 7.1
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Microarray data analysis
• begin with a data matrix (gene expression values
versus samples)
Typically, there are
many genes
(>> 20,000) and
few samples (~ 10)
Fig. 7.1
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Microarray data analysis
• begin with a data matrix (gene expression values
versus samples)
Preprocessing
Inferential statistics
Descriptive statistics
Fig. 7.1
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Microarray data analysis: preprocessing
Observed differences in gene expression could be
due to transcriptional changes, or they could be
caused by artifacts such as:
• different labeling efficiencies of Cy3, Cy5
• uneven spotting of DNA onto an array surface
• variations in RNA purity or quantity
• variations in washing efficiency
• variations in scanning efficiency
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Microarray data analysis: preprocessing
The main goal of data preprocessing is to remove
the systematic bias in the data as completely as
possible, while preserving the variation in gene
expression that occurs because of biologically
relevant changes in transcription.
A basic assumption of most normalization procedures
is that the average gene expression level does not
change in an experiment.
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Data analysis: global normalization
Global normalization is used to correct two or more
data sets. In one common scenario, samples are
labeled with Cy3 (green dye) or Cy5 (red dye) and
hybridized to DNA elements on a microrarray. After
washing, probes are excited with a laser and detected
with a scanning confocal microscope.
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Data analysis: global normalization
Global normalization is used to correct two or more
data sets
Example: total fluorescence in
Cy3 channel = 4 million units
Cy 5 channel = 2 million units
Then the uncorrected ratio for a gene could show
2,000 units versus 1,000 units. This would artifactually
appear to show 2-fold regulation.
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Data analysis: global normalization
Global normalization procedure
Step 1: subtract background intensity values
(use a blank region of the array)
Step 2: globally normalize so that the average ratio = 1
(apply this to 1-channel or 2-channel data sets)
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Scatter plots
Useful to represent gene expression values from
two microarray experiments (e.g. control, experimental)
Each dot corresponds to a gene expression value
Most dots fall along a line
Outliers represent up-regulated or down-regulated genes
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Outline: microarray data analysis
Gene expression
Microarrays
Preprocessing
normalization
scatter plots
Inferential statistics
t-test
ANOVA
Exploratory (descriptive) statistics
distances
clustering
principal components analysis (PCA)
Inferential statistics
Inferential statistics are used to make inferences
about a population from a sample.
Hypothesis testing is a common form of inferential
statistics. A null hypothesis is stated, such as:
“There is no difference in signal intensity for the gene
expression measurements in normal and diseased
samples.” The alternative hypothesis is that there
is a difference.
We use a test statistic to decide whether to accept or
reject the null hypothesis. For many applications,
we set the significance level a to p < 0.05.
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Inferential statistics
A t-test is a commonly used test statistic to assess
the difference in mean values between two groups.
t=
x1 – x2
SE
Questions
difference between mean values
=
variability (standard error
of the difference)
Is the sample size (n) adequate?
Are the data normally distributed?
Is the variance of the data known?
Is the variance the same in the two groups?
Is it appropriate to set the significance level to p < 0.05?
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Inferential statistics
A t-test is a commonly used test statistic to assess
the difference in mean values between two groups.
t=
Notes
x1 – x2
SE
difference between mean values
=
variability (standard error
of the difference)
• t is a ratio (it thus has no units)
• We assume the two populations are Gaussian
• The two groups may be of different sizes
• Obtain a P value from t using a table
• For a two-sample t test, the degrees of freedom is N -2.
For any value of t, P gets smaller as df gets larger
t-test to determine statistical significance
disease
vs normal
Error
difference between mean of disease and normal
t statistic =
variation due to error
ANOVA partitions total data variability
Before partitioning
After partitioning
Subject
disease
vs normal
disease
vs normal
Error
Error
Tissue type
variation between DS and normal
F ratio =
variation due to error
Inferential statistics
Paradigm
Parametric test
Nonparametric
Compare two
unpaired groups
Unpaired t-test
Mann-Whitney test
Compare two
paired groups
Paired t-test
Wilcoxon test
Compare 3 or
more groups
ANOVA
Table 7-2
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Inferential statistics
Is it appropriate to set the significance level to p < 0.05?
If you hypothesize that a specific gene is up-regulated,
you can set the probability value to 0.05.
You might measure the expression of 10,000 genes and
hope that any of them are up- or down-regulated. But
you can expect to see 5% (500 genes) regulated at the
p < 0.05 level by chance alone. To account for the
thousands of repeated measurements you are making,
some researchers apply a Bonferroni correction.
The level for statistical significance is divided by the
number of measurements, e.g. the criterion becomes:
p < (0.05)/10,000 or p < 5 x 10-6
The Bonferroni correction is generally considered to be
too conservative.
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Inferential statistics: false discovery rate
The false discovery rate (FDR) is a popular multiple
corrections correction. A false positive (also called a type
I error) is sometimes called a false discovery.
The FDR equals the p value of the t-test times the
number of genes measured (e.g. for 10,000 genes and a
p value of 0.01, there are 100 expected false positives).
You can adjust the false discovery rate. For example:
FDR # regulated transcripts
0.1
100
0.05
45
0.01
20
# false discoveries
10
3
1
Would you report 100 regulated transcripts of which 10
are likely to be false positives, or 20 transcripts of which
one is likely to be a false positive?