Introduction to Microarray Gene Expression

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Introduction to Microarray Gene Expression

• Shyamal D. PeddadaBiostatistics Branch
• National Inst. Environmental
• Health Sciences (NIH)Research Triangle Park, NC

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Outline 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.

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Outline 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

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Talk 1 An overview of microarray data
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To perform statistical analysis of 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

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Some Important Terminology and Background
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Central Dogma of Molecular Biology
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Some background terminologyDNA 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

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Some background terminologyDNA 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)

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Some background terminologyTypes 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).

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Some background terminology Oligos

• 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.

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Some background terminology Probes

• 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.

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Some background terminologyBlots - 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.

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Some 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.

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Microarrays
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Northern 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 100s of genes at the same time Limited
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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.

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Microarray Technology

• Two color dye array (Spotted array)
• Spotted cDNA microarrays
• Spotted oligo microarrays
• Single dye array
• In situ oligo microarrays

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Microarray Technology
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Spotted Microarrays
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Spotted DNA Microarray

• Slides carrying spots of target DNA are
  hybridized to fluorescently labeled cDNA from
  experimental and control cells and the arrays are
  imaged at two or more wavelengths
• Expression profiling involves the hybridization
  of fluorescently labeled cDNA, prepared from
  cellular mRNA, to microarrays carrying thousands
  of unique sequences.

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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?

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Spotted DNA Microarray

• Gene expression
• Y lt 0 gene is over expressed in green labeled
  sample compared to red-labeled sample
• Y 0 gene is equally expressed in both samples
• Y gt 0 gene is over expressed in red-labeled
  sample compared to green labeled sample

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Single Dye Microarrays
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Major Commercial Platforms

• More than 50 companies are currently offering
  various DNA microarray platforms, reagents and
  software
• Affymetrix dominated the marker for many years

Agilent has one and two-color microarray platform
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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 lt-gt C, A lt-gt
  T)
• Probe set a collection of probe-pairs (11 to 20)
  related to a fraction of gene

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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

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Affy call

• Detection of p-value
• Calculate Kendalls tau T for each probe pair
• T (PM-MM) / (PMMM)
• Determine the statistical significance of the
  gene by computing the p-value.

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Affy call
Ref Affymetrix Technical Manual
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Affymetrix Vs Illumina
Ref Pan Du Simon Lin
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(No Transcript)
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Which Platform to Choose?

• Every platform has its unique feature
• Choose platform based on
• Nature of the study
• Amount of available RNA
• Cost
• Platform comparison in MAQC study

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MAQC Project

• Objective To generate a set of quality control
  tools for microarray research community
• 137 participants representing 51 organizations
• Gene expression from two distinct RNA samples
  (total 4 samples)
• Sample A Universal Human Reference
  RNA(UHRR)100
• Sample B Human Brain Reference RNA(HBRR) 100
• Sample C 75 UHRR 25 HBRR
• Sample D 25 UHRR 75 HBRR

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Microarray Data Analysis
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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.

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Sources 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.

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A useful graphical representation of data

• Data matrix
• Let

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A useful graphical representation of data

• Let its spectral decomposition be given by
• where

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A useful graphical representation of data

• Then
• Plot

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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)

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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

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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

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Linear Normalization(for spotted arrays)

• Basic assumption
• Mean/Median expression ratio of all monitored
  mRNAs depends upon the average intensity
• Regression of

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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)

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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.

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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.

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

• Subjects Pool Observations
  
• 
  (Microarray chips)

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The pooling design

• Subjects Pool Observations
  
• 
  (Microarray chips)
• 9 3 6
• (3 per pool)
• More generally
• n p m
• (rn/p per pool)

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The standard design

• Subjects Pool Observations
  
• 
  (Microarray chips)
• 9 9 9
• (r1)
• More generally
• n pn mn
• (r1)

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Some issues

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

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Parameters

• 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 (rn/p)
• Observed number of samples (e.g. microarrays) (m)

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Some 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.

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Power comparisons

• Bio Micro Pool size Power
• 5/group 5/group 1 (Standard design) 0.81
• 6/group 6/group 1 (Standard design) 0.95
• 6/group 3/group 2 (i.e 3 pools/group)
  0.30
• 8/group 4/group 2 (i.e. 4 pools/group)
  0.80
• 10/group 5/group 2 (i.e. 5 pools/group)
  0.98
• Zhang and Gant (2005)

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Power 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.

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A 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 genes expression in the
  r samples.
• This assumption need not be true especially if
  the expression
• values are transformed non-linearly.

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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.

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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)

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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!