Microarray Data Analysis

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Microarray Data Analysis

• Stuart M. Brown
• NYU School of Medicine

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The Central Dogma of Molecular BiologyDNA is
transcribed into RNA which is then translated
into protein
Measured by Microarray
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What is a Microarray

• A simple concept Dot Blot Northern
• Reverse the hybridization - put the probes on the
  filter and label the bulk RNA
• Make probes for lots of genes - a massively
  parallel experiment
• Make it tiny so you dont need so much RNA from
  your experimental cells.
• Make quantitative measurements

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Microarrays are Popular

• At NYU Med Center we are now collecting about 3
  GB of microarray data per week (60 chips, 6-10
  different experiments)
• PubMed search "microarray" 13,948 papers
• 2005 4406
• 2004 3509
• 2003 2421
• 2002 1557
• 2001 834
• 2000 294

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A Filter Array
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DNA Chip Microarrays

• Put a large number (100K) of cDNA sequences or
  synthetic DNA oligomers onto a glass slide (or
  other subtrate) in known locations on a grid.
• Label an RNA sample and hybridize
• Measure amounts of RNA bound to each square in
  the grid
• Make comparisons
• Cancerous vs. normal tissue
• Treated vs. untreated
• Time course
• Many applications in both basic and clinical
  research

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cDNA Microarray Technologies

• Spot cloned cDNAs onto a glass microscope slide
• usually PCR amplified segments of plasmids
• Label 2 RNA samples with 2 different colors of
  flourescent dye - control vs. experimental
• Mix two labeled RNAs and hybridize to the chip
• Make two scans - one for each color
• Combine the images to calculate ratios of amounts
  of each RNA that bind to each spot

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Spot your own Chip (plans available for free
from Pat Browns website)
Robot spotter
Ordinary glass microscope slide
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Combine scans for Red Green
False color image is made from digitized
fluorescence data, not by superimposing scanned
images
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cDNA Spotted Microarrays
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Affymetrix Gene chip system

• Uses 25 base oligos synthesized in place on a
  chip (20 pairs of oligos for each gene)
• RNA labeled and scanned in a single color
• one sample per chip
• Can have as many as 20,000 genes on a chip
• Arrays get smaller every year (more genes)
• Chips are expensive
• Proprietary system black box software, can
  only use their chips

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Affymetrix Gene Chip
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Affymetrix Technology
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Affymetrix Pivot Table
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Data Acquisition

• Scan the arrays
• Quantitate each spot
• Subtract background
• Normalize
• Export a table of fluorescent intensities for
  each gene in the array

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Automate!!

• All of this can be done automatically by
  software.
• Much more consistent
• Mistakes will be made (especially in the spot
  quantitation) but you cant manually check
  hundreds of thousands of spots

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Affymetrix Software

• Affymetrix System is totally automated
• Computes a single value for each gene from 40
  probes - (using surprisingly kludgy math)
• Highly reproducible (re-scan of same chip or
  hyb. of duplicate chips with same labeled sample
  gives very similar results)
• Incorporates false results due to image artefacts
• dust, bubbles
• pixel spillover from bright spot to neighboring
  dark spots

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Goals of a Microarray Experiment

• Find the genes that change expression between
  experimental and control samples
• Classify samples based on a gene expression
  profile
• Find patterns Groups of biologically related
  genes that change expression together across
  samples/treatments

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Basic Data Analysis

• Fold change (relative increase or decrease in
  intensity for each gene)
• Set cutoff filter for low values (background
  noise)
• Cluster genes by similar changes - only really
  meaningful across multiple treatments or time
  points
• Cluster samples by similar gene expression
  profiles

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Streamlined Affy Analysis
Normalize
Filter
Present/AbsentMinimum valueFold change
Raw data
(RMA)
Classification
Significance
Clustering
PAM Machine learning
t-test SAM Rank Product
Gene lists
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Sources of Variability

• Image analysis (identifying and quantitating each
  spot on the array)
• Scanning (laser and detector, chemistry of the
  flourescent label))
• Hybridization (temperature, time, mixing, etc.)
• Probe labeling
• RNA extraction
• Biological variability

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Scatter plot of all genes in a simple comparison
of two control (A) and two treatments (B high
vs. low glucose) showing changes in expression
greater than 2.2 and 3 fold.
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Thomas Hudson, Montreal Genome Center
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Normalization

• Can control for many of the experimental sources
  of variability (systematic, not random or gene
  specific)
• Bring each image to the same average brightness
• Can use simple math or fancy -
• divide by the mean (whole chip or by sectors)
• LOESS (locally weighted regression)
• No sure biological standards

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RMA

• Robust Multichip Average
• Bolstad, B.M., Irizarry R. A., Astrand, M., and
  Speed, T.P. (2003), A Comparison of Normalization
  Methods for High Density Oligonucleotide Array
  Data Based on Bias and Variance. Bioinformatics
  19(2)185-193

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Are the Treatments Different?

• Analysis of microarray data has tended to focus
  on making lists of genes that are up or down
  regulated between treatments
• Before making these lists, ask the
  question "Are the treatments different?"
• Use standard statistical methods to evaluate
  expression profiles for each treatment (t-test or
  f-test)
• If there are differences, find the genes most
  responsible
• If there are not significant overall differences,
  then lists of genes with large fold changes may
  only reflect random variability.

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Statistics

• When you have variability in measurements, you
  need replication and statistics to find real
  differences
• Its not just the genes with 2 fold increase, but
  those with a significant p-value across
  replicates
• Non-parametric (i.e. rank) or paired value
  statistics may be more appropriate

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Multiple Comparisons

• In a microarray experiment, each gene (each probe
  or probe set) is really a separate experiment
• Yet if you treat each gene as an independent
  comparison, you will always find some with
  significant differences
• (the tails of a normal distribution)

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False Discovery

• Statisticians call false positives a "type 1
  error" or a "False Discovery"
• False Discovey Rate (FDR) is equal to the p-value
  of the t-test X the number of genes in the array
• For a p-value of 0.01 X 10,000 genes 100
  false different genes
• You cannot eliminate false positives, but by
  choosing a more stringent p-value, you can keep
  them manageable (try p0.001)
• The FDR must be smaller than the number of real
  differences that you find - which in turn depends
  on the size of the differences and varability of
  the measured expression values

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SAM

• Significance Analysis of Microarrays
• Tusher, Tibshirani and Chu (2001) Significance
  analysis of microarrays applied to the ionizing
  radiation response. PNAS 2001 98 5116-5121, (Apr
  24).

• Excel plugin
• Free
• Permutation based
• Most published method of microarray data analysis

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Higher LevelMicroarray data analysis

• Clustering and pattern detection
• Data mining and visualization
• Controls and normalization of results
• Statistical validatation
• Linkage between gene expression data and gene
  sequence/function/metabolic pathways databases
• Discovery of common sequences in co-regulated
  genes
• Meta-studies using data from multiple experiments

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Types of Clustering

• Herarchical
• Link similar genes, build up to a tree of all
• Self Organizing Maps (SOM)
• Split all genes into similar sub-groups
• Finds its own groups (machine learning)
• Principle Component
• every gene is a dimension (vector), find a single
  dimension that best represents the differences in
  the data

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Cluster by color difference
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GeneSpring
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SOM Clusters
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Classification

• How to sort samples into two classes based on
  gene expression data
• Cancer vs. normal
• Cancer sub-types (benign vs. malignant)
• Responds well to drug vs. poor response (i.e.
  tamoxifen for breast cancer)

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Support Vector Machines
Fat planes With an infinitely thin plane the
data can always be separated correctly, but not
necessarily with a fat one. Again if a large
margin separation exists, chances are good that
we found something relevant. Large Margin
Classifiers
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• PAM Prediction Analysis for Microarrays
• Class Prediction and Survival Analysis for
  Genomic Expression Data Mining
• Performs sample classification from gene
  expression data,
• via "nearest shrunken centroid method'' of
  Tibshirani, Hastie, Narasimhan and Chu (2002)
• "Diagnosis of multiple cancer types by shrunken
  centroids of gene expression"
• PNAS 2002 996567-6572 (May 14).

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BioConductor

• All of these normalization, statistical, and
  clustering methods are available in a free
  software package called BioConductor.
• www.bioconductor.org
• User hostile command line interface
• Uses scripts in the 'R' statistical language

gt data(SpikeIn) gt pms lt- pm(SpikeIn) gt mms lt-
mm(SpikeIn) gt par(mfrow c(1, 2)) gt
concentrations lt- matrix(as.numeric(sampleNames(Sp
ikeIn)), 20, 12, byrow TRUE) gt
matplot(concentrations, pms, log "xy", main
"PM", ylim c(30, 20000)) gt lines(concentration
s1, , apply(pms, 2, mean), lwd 3) gt
matplot(concentrations, mms, log "xy", main
"MM", ylim c(30, 20000)) gt lines(concentration
s1, , apply(mms, 2, mean), lwd 3)
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Functional Genomics

• Take a list of "interesting" genes and find their
  biological relationships
• Gene lists may come from significance/classficatio
  n analysis of microarrays, proteomics, or other
  high-throughput methods
• Requires a reference set of "biological
  knowledge"

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Genome Ontology

• How to organize biological knowledge?
• Biologists work on a variety of different
  research organisms yeast, fruit fly, mouse,
  human
• the same gene can have very different functions
  (antennapedia)
• and very different names (sonic hedgehog)

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GO

• Biologists got together a few years ago and
  developed a sensible system called Genome
  Ontology (GO)
• 3 hierarchical sets of terminology
• Biological Process
• Cellular Component (location within cell)
• Molecular Function
• about 1000 categories of functions

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Biological Pathways
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Microarray Databases

• Large experiments may have hundreds of individual
  array hybridizations
• Core lab at an institution or multiple
  investigators using one machine - data archive
  and validate across experiments
• Data-mining - look for similar patterns of gene
  expression across different experiments

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Public Databases

• Gene Expression data is an essential aspect of
  annotating the genome
• Publication and data exchange for microarray
  experiments
• Data mining/Meta-studies
• Common data format - XML
• MIAME (Minimal Information About a Microarray
  Experiment)

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GEO at the NCBI
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Array Express at EMBL
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Gene ExpressionTechnologies

• cDNA (EST) libraries
• SAGE
• Microarray
• rt-PCR
• RNA-seq

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The Cancer Genome Anatomy Project

• CGAP has collected a large amount of cDNA and
  related data online
• http//cgap.nci.nih.gov/
• cDNA libraries from various tissues
• search for genes
• compare expression levels

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SAGE

• Serial Analysis of Gene Expression is a
  technology that sequences very short fragments of
  mRNA (10 or 17 bp) that have been randomly
  ligated together
• The short tags are assigned to genes and then
  relative counts for each gene are computed for
  cDNA libraries from various tissues

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SAGE Genie

• SAGE Anatomic Viewer
• SAGE Digital Gene Expression Displayer
• Digital Northern
• SAGE Experiment Viewer

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Microarray

• GEO database at NCBI
• Microarray experiments
• Defined arrays
• Published results
• Also lots of inconclusive experiments
• Tools to search for specific genes
• Unreliable to search for tissue or disease in
  experiment description text

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RNA-seq

• Next Generation DNA seqencing
• NYU currently has one Illumina Genome Analyser
• generates more than 1 million RNA sequences per
  sample
• Currently seeking funding for a Roche/454
• produces 100K reads of 250-400 bp

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Count Transcripts

• Techology exists to accurately count transcripts
  and compare samples
• Digital Gene Expression
• Can also identify alternate isoforms, splice
  variants, etc.