Gene expression:Microarray data analysis
Compare gene expression
in this cell type…
…after viral infection
…in samplesfrom patients
…relative to a knockout
…after drug treatment
…at a later developmental time
…in a different body region
• by region (e.g. brain versus kidney)
• in development (e.g. fetal versus adult tissue)
• in dynamic response to environmental signals
Gene expression is context-dependent,and is regulated in several basic ways
(e.g. immediate-early response genes)
• in disease states
• by gene activity
DNA RNA protein DNA RNA protein
cDNA cDNA
UniGene
SAGE
microarray
next-generation sequencing!!!
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 UniGeneyou get information on its abundanceand its regional distribution.
Microarrays: tools for gene expression
A microarray is a solid support (such as a membraneor glass microscope slide) on which DNA of knownsequence is deposited in a grid-like array.
Microarrays: tools for gene expression
The most common form of microarray is used to measure gene expression. RNA is isolated from matched samples 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 levelsof thousands of genes.
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
Advantages of microarray experiments
to represent genes of interest
Easy Submit RNA samples to a core facility
Cheap! Chip representing 20,000 genes for $300
Cost ■ Some researchers can’t afford to doappropriate numbers of controls, replicates (x100)
RNA ■ The final product of gene expression is proteinsignificance ■ “Pervasive transcription” of the genome is
poorly understood (ENCODE project)■ There are many noncoding RNAs not yet
Disadvantages of microarray experiments
■ There are many noncoding RNAs not yetrepresented on microarrays
Quality ■ Impossible to assess elements on array surfacecontrol ■ Artifacts with image analysis
■ Artifacts with data analysis■ Not enough attention to experimental design■ Not enough collaboration with statisticians
Sampleacquisition
Dataacquisition
Biological insight
Data analysis
Data confirmation
Stage 1: Experimental design
Stage 3: Hybridization to DNA arrays
Stage 2: RNA and probe preparation
Stage 4: Image analysis Stage 4: Image analysis
Stage 5: Microarray data analysis
Stage 6: Biological confirmation
Stage 7: Microarray databases
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
Sample 1 Sample 2 Sample 3
One sample per array(e.g. Affymetrix or radioactivity-based platforms)
Samples 1,2 Samples 1,3 Samples 2,3
Two samples per array (competitive hybridization)
Sample 1, pool Sample 2, poolSamples 2,1:switch dyes
Stage 2: RNA preparation
For Affymetrix chips, need total RNA (about 5 ug)
Confirm purity by running agarose gelConfirm purity by running agarose gel
Measure a260/a280 to confirm purity, quantity
One of the greatest sources of error in microarrayexperiments is artifacts associated with RNA isolation;be sure to create an appropriately balanced,randomized experimental design.
Stage 3: Hybridization to DNA arrays
The array consists of cDNA or oligonucleotides
Oligonucleotides can be deposited by photolithographyOligonucleotides can be deposited by photolithography
The sample is converted to cRNA or cDNA
(Note that the terms “probe” and “target” may refer to theelement immobilized on the surface of the microarray, orto the labeled biological sample; for clarity, it may be simplest to avoid both terms.)
Microarrays: array surface
Southern et al. (1999) Nature Genetics, microarray supplement
Stage 4: Image analysis
RNA transcript levels are quantitated
Fluorescence intensity is measured with a scanner,Fluorescence intensity is measured with a scanner,or radioactivity with a phosphorimager
Control
Differential Gene Expression on a cDNA Microarray
Rett
αααα B Crystallin is over-expressed in Rett Syndrome
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?
Stage 6: Biological confirmation
Microarray experiments can be thought of as
“hypothesis-generating” experiments.
The differential up- or down-regulation of specific RNAThe 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
Stage 7: Microarray databases
There are two main repositories:
Gene expression omnibus (GEO) at NCBIGene expression omnibus (GEO) at NCBI
ArrayExpress at the European Bioinformatics Institute
(EBI)
Array Express at the European Bioinformatics Institutehttp://www.ebi.ac.uk/arrayexpress/
MIAME
In an effort to standardize microarray data presentationand analysis, Alvis Brazma and colleagues at 17institutions introduced Minimum Information About aMicroarray 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
Microarray data analysis
• begin with a data matrix (gene expression values
versus samples)
genes(RNA
transcriptlevels)
Microarray data analysis
• begin with a data matrix (gene expression values
versus samples)
Typically, there areTypically, there aremany genes(>> 20,000) and few samples (~ 10)
Microarray data analysis
• begin with a data matrix (gene expression values
versus samples)
PreprocessingPreprocessing
Inferential statistics Descriptive statistics
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
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.relevant changes in transcription.
A basic assumption of most normalization procedures
is that the average gene expression level does not
change in an experiment.
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.
Data analysis: global normalization
Global normalization is used to correct two or more
data sets
Example: total fluorescence in
Cy3 channel = 4 million units
Cy5 channel = 2 million unitsCy5 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.
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)
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
Brain Fibroblast
Differential Gene Expressionin Different Tissue and Cell Types
Astrocyte Astrocyte
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Expression level (sample 1)
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Log-log transformation
Scatter plots
Typically, data are plotted on log-log coordinates
Visually, this spreads out the data and offers symmetry
raw ratio log2 ratio
time behavior value valuetime behavior value value
t=0 basal 1.0 0.0
t=1h no change 1.0 0.0
t=2h 2-fold up 2.0 1.0
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You can make these plots in Excel…
…but for many bioinformatics applications use R.Visit http://www.r-project.org to download it.
Visit http://www.r-project.org to download it. See chapter 9 (2nd edition) for a tutorial on microarray data analysis.
M M
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After RMA (a normalization procedure), the median is near zero, and skewing is corrected.
Scatterplots display the effects of normalization.
SNOMAD converts array data to scatter plotshttp://www.snomad.org
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SNOMAD describes regulated genes in Z-scores
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Robust multi-array analysis (RMA)
• Developed by Rafael Irizarry, Terry Speed, and others• Available at www.bioconductor.org as an R package• Also available in various software packages (including
Partek, www.partek.com and Iobion Gene Traffic)• See Bolstad et al. (2003) Bioinformatics 19; Irizarry et al. (2003) Biostatistics 4
There are three steps:
[1] Background adjustment based on a normal plus exponential model (no mismatch data are used)
[2] Quantile normalization (nonparametric fitting of signal intensity data to normalize their distribution)
[3] Fitting a log scale additive model robustly. The model is additive: probe effect + sample effect
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Histograms of raw intensity values for 14 arrays (plotted in R) before and after RMA was applied.
RMA adjusts for the effect of GC content
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precision accuracyprecision with
accuracy
Good performance:reproducibility ofthe result
Good quality of the result(relative to agold standard)
Robust multi-array analysis (RMA)
RMA offers a large increase in precision (relative to Affymetrix MAS 5.0 software).
precision
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Robust multi-array analysis (RMA)
RMA offers comparable accuracy to MAS 5.0.
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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“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 α to p < 0.05.
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
difference between mean values
variability (standard errorof the difference)
Questions:
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?
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 errorof the difference)
Notes
• 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
Analyzing expression data in ExcelQuestion: for each of my 20,000 transcripts, decide whether
it is significantly regulated in some disease.
control disease
[1] Obtain a matrix of genes (rows) and expression values columns. Here there are 20,000 rows of genes of which the first six are shown. There are three control samples and three disease samples. You can also calculate the mean value for each gene (transcript) for the controls and the disease (experimental) samples.
Analyzing expression data in Excel
[2] You can calculate the ratios of control versus disease.
Note that you can use the formula =E5/I5 in this case to divide the mean control and disease values.
Also note that some ratios, such as 2.00, appear to be dramatic while others are not. Some researchers set a cut-off for changes of interest such as two-fold.
Analyzing expression data in Excel
[3] Perform a t-test. When you enter =TTEST into the function box above, a dialog box appears. Enter the range of values for controls and for disease samples, and specify a 1-or 2-tailed test.
Analyzing expression data in Excel
[3] Perform a t-test (continued). For a one-tailed test, your prior hypothesis is that the transcript in the disease group is up (or down) relative to controls; the change is unidirectional. For example, in Down syndrome samples you might hypothesize that chromosome 21 transcripts are significantly up-regulated because of the extra copy of chromosome 21.
Analyzing expression data in Excel
[3] Perform a t-test (continued). For a two-tailed test, you hypothesize that the two groups are different, but you do not know in which direction.
Analyzing expression data in Excel
[3] Note the results: you can have…
a small p value (<0.05) with a big ratio differencea small p value (<0.05) with a trivial ratio differencea large p value (>0.05) with a big ratio differencea large p value (>0.05) with a trivial ratio difference
Only the first group is worth reporting! Why?
disease
vs normal
t-test to determine statistical significance
Error
difference between mean of disease and normalt statistic =
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 ANOVA
more groups
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 normalF ratio =
variation due to error
Descriptive statistics
Microarray data are highly dimensional: there are
many thousands of measurements made from a small
number of samples.
Descriptive (exploratory) statistics help you to find
meaningful patterns in the data.meaningful patterns in the data.
A first step is to arrange the data in a matrix.
Next, use a distance metric to define the relatedness
of the different data points. Two commonly used
distance metrics are:
-- Euclidean distance
-- Pearson coefficient of correlation
What is a cluster?
A cluster is a group that has homogeneity (internal cohesion) and separation (external isolation). The relationships between objects being studied are assessed by similarity or dissimilarity measures.
Data matrix(20 genes and
3 time pointsgenes
samples (time points)
3 time pointsfrom Chu et al., 1998)
Software: S-PLUS package
genes
t=2.0
3D plot (using S-PLUS software)
t=0t=0.5
Descriptive statistics: clustering
Clustering algorithms offer useful visual descriptions
of microarray data.
Genes may be clustered, or samples, or both.
We will next describe hierarchical clustering.We will next describe hierarchical clustering.
This may be agglomerative (building up the branches
of a tree, beginning with the two most closely related
objects) or divisive (building the tree by finding the
most dissimilar objects first).
In each case, we end up with a tree having branches
and nodes.
Agglomerative clustering
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Adapted from Kaufman and Rousseeuw (1990)
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Divisive clustering
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Divisive clustering
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Divisive clustering
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Agglomerative and divisive clustering sometimes give conflictingresults, as shown here
Cluster and TreeView
clustering PCASOMK means
Cluster and TreeView
Two-way clusteringof genes (y-axis)
and cell lines(x-axis)(Alizadeh et al.,2000)
An exploratory technique used to reduce thedimensionality of the data set to 2D or 3D
For a matrix of m genes x n samples, create a new
Principal components analysis (PCA)
For a matrix of m genes x n samples, create a newcovariance matrix of size n x n
Thus transform some large number of variables intoa smaller number of uncorrelated variables calledprincipal components (PCs).
Principal components analysis (PCA): objectives
• to reduce dimensionality
• to determine the linear combination of variables
• to choose the most useful variables (features)• to choose the most useful variables (features)
• to visualize multidimensional data
• to identify groups of objects (e.g. genes/samples)
• to identify outliers
http://www.okstate.edu/artsci/botany/ordinate/PCA.htm
http://www.okstate.edu/artsci/botany/ordinate/PCA.htm
http://www.okstate.edu/artsci/botany/ordinate/PCA.htm
http://www.okstate.edu/artsci/botany/ordinate/PCA.htm
Copyright notice
Many of the images in this powerpoint presentationare from Bioinformatics and Functional Genomics
by Jonathan Pevsner (ISBN 0-471-21004-8). Copyright © 2003 by John Wiley & Sons, Inc.