Gene expression: Microarray data analysis. Outline: microarray data analysis Gene expression Microarrays Preprocessing normalization scatter plots Inferential

Gene expression:Microarray data analysis

Outline: microarray data analysis

Gene expression

Microarrays

Preprocessingnormalizationscatter plots

Inferential statisticst-testANOVA

Exploratory (descriptive) statisticsdistancesclusteringprincipal components analysis (PCA)

Compare gene expression in this cell type…

…after drug treatment

…at a later developmental time

…in a different body region

…after viral infection

…in samplesfrom patients

…relative to a knockout

• 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

Gene expression is context-dependent,and is regulated in several basic ways

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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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Gene expression

Microarrays




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.

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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 levelsof thousands of genes.

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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

Advantages of microarray experiments

Table 6-4Page 175

Cost ■ Some researchers can’t afford to do appropriate numbers of controls, replicates

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 represented 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

Disadvantages of microarray experiments

Table 6-5Page 176

Biological insight

Sampleacquisition

Dataacquisition

Data analysis

Data confirmation

Fig. 6.16Page 176

Fig. 6.16Page 176

Stage 1: Experimental design

Stage 3: Hybridization to DNA arrays

Stage 2: RNA and probe preparation

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

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Sample 1 Sample 2 Sample 3

Fig. 6.17Page 177

One sample per array(e.g. Affymetrix or radioactivity-based platforms)

Samples 1,2 Samples 1,3 Samples 2,3

Sample 1, pool Sample 2, poolSamples 2,1:switch dyes

Fig. 6.17Page 177

Two samples per array (competitive hybridization)

Stage 2: RNA preparation

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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 microarrayexperiments is artifacts associated with RNA isolation;be sure to create an appropriately balanced,randomized experimental design.

Stage 3: Hybridization to DNA arrays

Page 178-179

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 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

Fig. 6.18Page 179

Southern et al. (1999) Nature Genetics, microarray supplement

Stage 4: Image analysis

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RNA transcript levels are quantitated

Fluorescence intensity is measured with a scanner,or radioactivity with a phosphorimager

Rett

Control

Differential Gene Expression on a cDNA Microarray

B Crystallin is over-expressed in Rett Syndrome

Fig. 6.19Page 180

Fig. 6.20Page 181

Stage 5: Microarray data analysis

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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

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Microarray experiments can be thought of as“hypothesis-generating” experiments.

The differential up- or down-regulation of specific RNAtranscripts can be measured using independent assayssuch as

-- Northern blots-- polymerase chain reaction (RT-PCR)-- in situ hybridization

Stage 7: Microarray databases

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There are two main repositories:

Gene expression omnibus (GEO) at NCBI

ArrayExpress at the European Bioinformatics Institute (EBI)

MIAME

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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


Gene expression

Microarrays




Microarray data analysis

• begin with a data matrix (gene expression values versus samples)

Fig. 7.1Page 190

genes(RNA

transcriptlevels)



Typically, there aremany genes(>> 20,000) and few samples (~ 10)

Fig. 7.1Page 190



Preprocessing

Inferential statistics Descriptive statistics

Fig. 7.1Page 190

Microarray data analysis: preprocessing

Observed differences in gene expression could be due to transcriptional changes, or they could becaused 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 removethe systematic bias in the data as completely aspossible, while preserving the variation in geneexpression that occurs because of biologicallyrelevant changes in transcription.

A basic assumption of most normalization proceduresis that the average gene expression level does notchange in an experiment.

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Data analysis: global normalization

Global normalization is used to correct two or moredata sets. In one common scenario, samples arelabeled with Cy3 (green dye) or Cy5 (red dye) andhybridized to DNA elements on a microrarray. Afterwashing, probes are excited with a laser and detectedwith a scanning confocal microscope.

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Global normalization is used to correct two or moredata sets

Example: total fluorescence in Cy3 channel = 4 million unitsCy 5 channel = 2 million units

Then the uncorrected ratio for a gene could show2,000 units versus 1,000 units. This would artifactuallyappear to show 2-fold regulation.

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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 fromtwo 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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Gene expression

Microarrays




Inferential statistics

Inferential statistics are used to make inferencesabout a population from a sample.

Hypothesis testing is a common form of inferentialstatistics. A null hypothesis is stated, such as:“There is no difference in signal intensity for the geneexpression measurements in normal and diseasedsamples.” The alternative hypothesis is that thereis 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 to p < 0.05.

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A t-test is a commonly used test statistic to assessthe difference in mean values between two groups.

t = =

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?

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x1 – x2

SE

difference between mean values

variability (standard errorof the difference)


A t-test is a commonly used test statistic to assessthe difference in mean values between two groups.

t = =

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

x1 – x2

SE

difference between mean values

variability (standard errorof the difference)

disease vs normal

Error

t-test to determine statistical significance

difference between mean of disease and normalt statistic = variation due to error

Error

Error

Tissue type

ANOVA partitions total data variability

variation between DS and normalF ratio = variation due to error

Before partitioning After partitioning

Subjectdisease vs normal

disease vs normal


Paradigm Parametric test Nonparametric

Compare two unpaired groups Unpaired t-test Mann-Whitney test

Compare twopaired groups Paired t-test Wilcoxon test

Compare 3 or ANOVAmore groups

Table 7-2Page 198-200


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 andhope that any of them are up- or down-regulated. Butyou can expect to see 5% (500 genes) regulated at thep < 0.05 level by chance alone. To account for thethousands of repeated measurements you are making,some researchers apply a Bonferroni correction.The level for statistical significance is divided by thenumber 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. Page 199

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 # false discoveries0.1 100 100.05 45 30.01 20 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?

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Gene expression: Microarray data analysis. Outline: microarray data analysis Gene expression Microarrays Preprocessing normalization scatter plots Inferential