Introduction to Microarray Introduction to Microarray

Gene ExpressionGene Expression

Shyamal D. PeddadaBiostatistics Branch

National Inst. Environmental Health Sciences (NIH)

Research Triangle Park, NC

Outline of the four talksOutline of the four talks

A general overview of microarray data

– Some important terminology and background– Various platforms– Sources of variation– Normalization of data

Analysis of gene expression data - Nominal explanatory variables

– Two types of explanatory variables– Scientific questions of interest– A brief discussion on false discovery rate (FDR)

analysis– Some existing methods of analysis.

Outline of the four talksOutline of the four talks

Analysis of ordered gene expression data

– Common experimental designs– Some existing statistical methods– An example– Demonstration of ORIOGEN– Some open research problems

Analysis of data from cell-cycle experiments

– Some background on cell-cycle experiments– Modeling the data– Data from multiple experiments– Some open research problem

Talk 1: An overview Talk 1: An overview of microarray dataof microarray data

To perform statistical analysis To perform statistical analysis of any given dataof any given data

It is important to understand all sources of (i) bias, (ii) variability.

– Some basic understanding of the underlying technology!

– Understand the sampling/experimental design

Some Important Terminology Some Important Terminology and Backgroundand Background

Central Dogma of Molecular Biology

Some background terminology:Some background terminology:DNA and RNADNA and RNA

DNA (Deoxyribonucleic acid) - Contains genetic code or instructions for the development and function living organisms. It is double stranded.

Four Nucleotides (building blocks of DNA)

– Adenine (A), Guanine (G), – Thymine (T), Cytosine (C)

Base pairs: (A, T) (G, C)

E.g. 5’ ---AAATGCAT---3’ 3’ ---TTTACGTA---5’

Some background terminology:Some background terminology:DNA and RNADNA and RNA

RNA (Ribonucleic acid) - transcribed (or copied) from DNA. It is single stranded. (Complimentary copy of one of the strands of DNA)

RNA polymerase - An enzyme that helps in the transcription of DNA to form RNA.

Four Nucleotides (building blocks of DNA)

– Adenine (A), Guanine (G), – Uracil (U), Cytosine (C)

Base pairs: (A, U) (G, C)

Some background terminology:Some background terminology:Types of RNATypes of RNA

Types of RNA - (transfer) tRNA, (ribosomal) rRNA, etc.

mRNA - messenger RNA. Carries information from DNA to ribosomes where protein synthesis takes place (less stable than DNA).

Some background terminology: Some background terminology: OligosOligos

Oligonucleotide - a short segment of DNA consisting of a few base pairs. In short it is commonly called “Oligo”.

“mer” - unit of measurement for an Oligo. It is the number of base pairs. So 30 base pair Oligo would be 30-mer long.

Some background terminology: Some background terminology: ProbesProbes

cDNA - complimentary DNA. DNA sequence that is complimentary to the given mRNA.

– Obtained using an enzyme called reverse transcriptase.

Probes - a short segment of DNA (about 100-mer or longer) used to detect DNA or RNA that compliments the sequence present in the probe.

Some background terminology:Some background terminology:“Blots” - Origins of Microarrays“Blots” - Origins of Microarrays

Southern blot (Edwin Southern, 1975 J. Molec. Biol.)

– A method used to identify the presence of a DNA sequence in a sample of DNA.

Western blot (immunoblot)

– to identify a specific protein from a tissue extract.

Some background terminologySome background terminology

Southwestern blot

– to identify and characterize DNA-binding proteins.

Northern blot

– A method used to study the gene expression from a sample of mRNA.

Microarrays …Microarrays …

Northern blot Vs MicroarrayNorthern blot Vs Microarray

Microarray Northern blot

Rate of expression analysis

Thousands of genes at a time(High throughput)

Few genes at a time

Automation Automation possible

Manual

Scope Allows to explore relationships among several 100’s of genes at the same time

Limited

What is a Microarray?

Sequences from thousands of different genes are immobilized, or attached, at fixed locations.

Spotted, or actually synthesized directly onto the support.

Microarray Technology

Two color dye array (Spotted array)

– Spotted cDNA microarrays– Spotted oligo microarrays

Single dye array

– In situ oligo microarrays

Microarray Technology

Spotted MicroarraysSpotted Microarrays

Spotted DNA Microarray

Spotted DNA array is typically “home made” so you need to think about:

– cDNA or Oligo– Location of the Oligo in a given gene– Oligo length - number of bp?

Spotted DNA Microarray

Gene expression:

– Y < 0; gene is over expressed in green labeled sample compared to red-labeled sample

– Y = 0; gene is equally expressed in both samples

– Y > 0; gene is over expressed in red-labeled sample compared to green labeled sample

Y log2Red

Green

Single Dye MicroarraysSingle Dye Microarrays

Major Commercial Platforms

More than 50 companies are currently offering various DNA microarray platforms, reagents and software

Affymetrix dominated the marker for many years

Manufacturer Code Protocol Platform # of Probes

Applied Biosystems ABI One-color microarray Human Genome Survey Microarray v2.0 32878

Affymetrix AFX One-color microarray HG-U133 Plus 2.0 GeneChip 54675

Agilent* AG1 One-color microarray Whole Human Genome Oligo Microarray, G4112A 43931

Eppendorf EPP One-color microarray DualChip Microarray 294

GE Healthcare GEH One-color microarray CodeLink Human Whole Genome, 300026 54359

Illumina ILM One-color microarray Human-6 BeadChip, 48K v1.0 47293

*Agilent has one and two-color microarray platform

Affymetrix GeneChip

Each gene is represented by 11 to 20 oligos of 25-mers

Probe: An oligo of 25-mer

Probe Pair: a PM and MM pair

Perfect match (PM): A 25-mer complementary to a reference sequence of interest (part of the gene)

Mismatch (MM): same as PM with a single base change for the middle (13th) base (G <-> C, A <-> T)

Probe set: a collection of probe-pairs (11 to 20) related to a fraction of gene

Affymetrix call for the presence of a signal

Affymetrix detection algorithm uses probe pair intensities to obtain detection p-value

Using this p-value they decide whether the signal

is– “ present”, “marginal” or “absent”

Affy call

Detection of p-value

– Calculate Kendall’s tau T for each probe pair

T = (PM-MM) / (PM+MM)

– Determine the statistical significance of the gene by computing the p-value.

Affy call

Ref: Affymetrix Technical Manual

Affymetrix Vs Illumina

Ref: Pan Du & Simon Lin

Microarray Data AnalysisMicroarray Data Analysis

Why Normalize Data?

To “calibrate”/adjust data so as to reduce or eliminate the effects arising from variation in technology and other sources rather than due to true biological differences between test groups.

Sources of bias/variationSources of bias/variation

Tissue or cell lines

mRNA

– It can degrade over time - so there is a potential batch effect if portions of experiment are performed at different times

– Purity and quantity

Dye color effect (spotted arrays)

Variation due to technology - is substantially reduced with improved technology

Etc.

A useful graphical representation of data

Data matrix:

Let

S :mm sample covariance matrix.

Xmxn , Rank(X) r min(m,n) n.

m :# genes,n # samples.

A useful graphical representation of data

Let its spectral decomposition be given by

where

S '

:mr matrix of eigenvectors

: rr diagonal matrix of non- zero eigenvalues

1 2 ... r 0.

A useful graphical representation of data

Then

Plot

Z ' X : rn matrix of " eigengenes"

Z i i ' X : i th eigengene.

Z1 vs Z2

Common Normalization Methods

Internal Control Normalization

Global Normalization

Linear Normalization (Spotted arrays)

Non-linear Normalization Method (Spotted arrays) - LOWESS curve.

ANOVA

COMBAT (for batch effect)

Internal control normalization(Housekeeping gene(s))

Expression of each gene is measured relative to the average of house keeping genes.

– Basic assumption: Expression of housekeeping genes does not change.

Disadvantage: – House keeping genes may be highly expressed

sometimes. Unexpected regulation of house keeping gene(s) leads to misinterpretation

Global Normalization

Basic assumption

– Mean/Median expression ratio of all monitored mRNAs is constant across a chip.

Regression of

In simple terms the log ratios are corrected by a common “mean” or “median”

This method can also be applied to single Dye data

logR

G

on a constant

Linear Normalization(for spotted arrays)

Basic assumption

– Mean/Median expression ratio of all monitored mRNAs depends upon the average intensity

Regression of

logR

G

on (1/2) log(RG)

Non-Linear Normalization(for spotted arrays)

Basic assumption

– Mean/Median expression ratio of all monitored mRNAs depends upon the average intensity

Regression of

Where is estimated by the robust scatter plot

smoother LOWESS (Locally WEighted Scatterplot Smoothing)

logR

G

on C(log(RG))

C(log(RG))

Analysis of Variance (ANOVA)

Standard Analysis of Variance model

– Response variable - Gene expression– Explanatory variables:

– Dye color

– Batch

– Other potential effects?

Advantage: Statistically significant genes can be identified while controlling for

the various experimental conditions/factors.

Some important experimental designs

Pooled Samples versus Separate samples

– Sometimes there may not be sufficient biological sample/specimen from a given animal. In such cases biological samples are pooled from several identical animals to form a sample.

An example of a pooling designAn example of a pooling design(for each treatment group)(for each treatment group)

Subjects Pool Observations

(Microarray chips)

The pooling designThe pooling design

Subjects Pool Observations

(Microarray chips)

9 3 6(3 per pool)

More generally:n p m

(r=n/p per pool)

The standard designThe standard design

Subjects # Pool Observations

(Microarray chips)

9 9 9(r=1)

More generally:n p=n m=n

(r=1)

Some issuesSome issues

• What are the underlying parameters?• Effect of pooling on power.• The basic assumption. Validity of the

assumption.

ParametersParameters

• Total variation in the expression of a gene can be decomposed in to:

– Biological variation– Technical variation

• Biological samples (n)• Number of pools (p)• Biological samples per pool (r=n/p)• Observed number of samples (e.g. microarrays) (m)

Some comments about poolingSome comments about pooling

Variance of the estimated mean expression of a gene depends on:

– number of pools (p) – number of bio samples per pool (r)– number of arrays (m)– biological variation– Technical variation.

Pooling works well when the biological variation in the gene expression is substantially larger than the technical variation.

Power comparisonsPower comparisons# Bio #Micro Pool size Power

5/group 5/group 1 (Standard design) 0.816/group 6/group 1 (Standard design) 0.95

6/group 3/group 2 (i.e 3 pools/group) 0.308/group 4/group 2 (i.e. 4 pools/group) 0.8010/group 5/group 2 (i.e. 5 pools/group) 0.98

- Zhang and Gant (2005)

Power comparisonsPower comparisons

Conditions of the simulation study:

Biological variation is 4 times the technical variation.

False positive rate is 0.001.

Detect 2-fold expression.

Data are normally distributed.

A fundamental assumptionA fundamental assumption

Biological averaging:

Suppose an experiment consists of pooling “r” samples. Then the expression of a gene in the pooled sample is assumed to be the average of the gene’s expression in the “r” samples.

This assumption need not be true especially if the expression values are transformed non-linearly.

Some important experimental designs

Reference designs (Spotted array)

– Each treatment sample is hybridized against a common reference control.

Loop designs (Spotted array)

– Suppose we have a control and three experimental groups A, B and C. Then hybridize Control and A, A with B, B with C and C with A.

Data Analysis - Preliminaries

Normalization

Transformation of data (usual methods)

– Perhaps first fit ANOVA and plot the residuals

Log transformation Square root More generally, Box-Cox family of transformations

Identify potential outliers in the data (again, perhaps use the residuals)

Data Analysis

Method of Analysis depends upon the scientific question of interest.

In the next three lectures we describe several general methods and illustrate some using real data!