Microarray

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Microarray

• Yuki Juan
• NTUST
• May 26, 2003

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Content

• Biology background of microarray
• Design of microarray
• The workflow of microarray
• Image analysis of microarray
• Data analysis of microarray
• Discussion

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The Biology Background of Microarray

• The central dogma of life forms
• DNA
• RNA
• Monitoring the expression of genes

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Central Dogma

• DNA Replication
• --ACGCGA--
• --TGCGCT--
• RNA Transcription
• --UGCGCU--
• Protein Translation
• --CYSALA--

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DNA
replication
transcription
translation
DNA
RNA
Protein
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DNA

• The double helix
• stable
• Nucleotide
• A, T, G, C
• Base pair
• A T
• G C
• Oligonucleotide
• short DNA (tens of nucleotides, or bps)

(http//www.nhgri.nih.gov/)
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DNA Strand

• DNA has canonical orientation
• read from 5 to 3
• antiparallel one strand has direction opposite
  to its complements
• 5 TACTGAA 3
• 3 ATGACTT 5

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Hydrogen Bond Makes DNA Binding Specifically
Hydrogen bond
5
3
5
3
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Hydrogen Bond Makes DNA Binding Specifically

• The force between base pair is hydrogen bond,
  This force let
• A-T(U), C-G can specifically match together.

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RNA
replication
transcription
translation
DNA
RNA
Protein
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RNA

• Types
• messenger RNA
• ribosomal RNA (rRNA)
• transfer RNA (tRNA)

• Gene is expressed by transcribing DNA
• into single-stranded mRNA

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RNA (Detailed)
(http//www.nhgri.nih.gov/)
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Reverse Transcription
replication
transcription
translation
DNA
RNA
Protein
Reverse Transcription
By reverse transcriptase, we can convert RNA into
cDNA.
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The Southern Blot

• Basic DNA detection technique that has been used
  for over 30 years, known as Southern blots
• A known strand of DNA is deposited on a solid
  support (i.e. nitocellulose paper)
• An unknown mixed bag of DNA is labelled
  (radioactive or flourescent)
• Unknown DNA solution allowed to mix with known
  DNA (attached to nitro paper), then excess
  solution washed off
• If a copy of known DNA occurs in unknown
  sample, it will stick (hybridize), and labeled
  DNA will be detected on photographic film

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mRNA Represent Gene Function

• When measure the level of a mRNA, we are
  monitoring the activity of a gene.
• Thus, if we can understand all the level of
  mRNAs, we can study the expression of whole
  genome.
• Microarray takes the advantage of getting over
  10000 of blotting data in a single experiment,
  which makes monitoring the genome activity
  possible.

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Content

• Biology background of microarray
• Design of microarray
• The workflow of microarray
• Image analysis of microarray
• Data analysis of microarray
• Discussion

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Design of Microarray

• Microarray in different context
• The idea of microarray
• Main type of array chips

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mRNA Levels Compared in Many Different Contexts

• Different tissues, same organism (brain v.
  liver)
• Same tissue, same organism (tumor v. non-tumor)
• Same tissue, different organisms (wt v. mutant)
• Time course experiments (development)
• Other special designs (e.g. to detect spatial
  patterns).

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Idea of Microarray
Cell A
Cell B
Labeled cDNA from geneX
Hybridizaton to chip
Spot of geneX with complementary sequence of
colored cDNA
This spot shows red color after scanning.
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Over 10,000 Hybridization Could Be Down at One
Time
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Several Types of Arrays

• Spotted DNA arrays
• Developed by Pat Browns lab at Stanford
• PCR products of full-length genes (gt100nt)
• Affymetrix gene chips
• Photolithography technology from computer
  industry allows building many 25-mers
• Ink-jet microarrays from Agilent
• 25-60-mers printed directly on glass slides
• Flexible, rapid, but expensive

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Array Fabrication Spotting

• Use PCR to amplify DNA
• Robotic "pen" deposits DNA at defined coordinates
• approximately 1-10 ng per spot
• Experimentation with oligos (40, 70 bp)

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This machine can make 48 microarrays
simultaneously.
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Array Fabrication Photolithography

• Light activated synthesis
• synthesize oligonucleotides on glass slides
• 107copies per oligo in 24 x 24 um square
• Use 20 pairs of different 25-mers per gene
• Perfect match and mismatch

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Array Fabrication Photolithography
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Affymetrix Microarrays
Raw image
1.28cm
107 oligonucleotides, half perfectly match mRNA
(PM), half have one mismatch (MM) Raw gene
expression is intensity difference PM - MM
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Agilent cDNA microarray and oligonucelotides
microarray

• Agilent delivering printed 60-mer microarrays in
  addition to 25-mer formats.
• The inkjet process uses standard phosphoramidite
  chemistry to deliver extremely small volumes
  (picoliters) of the chemicals to be spotted.

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Content

• Biology background of microarray
• Design of microarray
• The workflow of microarray
• Image analysis of microarray
• Data analysis of microarray

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The Workflow of Microarray
sample
Plate
Plate Preparation
RNA extraction
Array Fabrication
cDNA synthesis and labeled
Array
Hybridization
Labeled cDNA
Hybridized Array
Scanning
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cDNA Synthesis And Directly Labeling
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Cy3 and Cy5 cDNA Hybridization On To The Chip
e.g. treatment / control normal / tumor
tissue
Sample loading
1.Loading from the corner of the cover slip It is
time consuming and easily producing bubbles.
1
2. Loading sample at the center of array then put
the slip smoothly Faster, and have lower chance
of bubble producing then the last one.
2
Sample loading
3. Loading sample at the side of the array then
put the slip on. Solution would attach to the
slip right after the slip contact with it, and
would diffuse with the movement of slip when we
slowly move down.
3
Sample loading
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Scan
Green down regulate Red up regulate Yellow
equal level
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Content

• Biology background of microarray
• Design of microarray
• The workflow of microarray
• Image analysis of microarray
• Data analysis of microarray
• Discussion

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Image analysis

• To find a spot
• Convert feature into numeric data
• Image normalization

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The Algorithms

• 1. Find spots Finds the location of each spot on
  the microarray.
• 2. Cookie cutter algorithm
• (1).Suppose the distribution of pixels vs
  intensity is Gaussian curve
• (2).Using SD or IQR to identify the feature and
  background of each spot
• (3).Calculates statistics for the pixel
  population

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Interquartile Range(IQR)
D
KIQR/2
1.42 IQR
50
75
25
Boundary for rejection
Boundary for rejection
IQR
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Feature or cookie
D
Local background
Exclusion zone
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Data Quality

• Irregular size or shape
• Irregular placement
• Low intensity

• Saturation
• Spot variance
• Background variance

artifact
miss alignment
bad print
indistinguishable
saturated
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Convert Feature Into Numeric Value
Green background
Green b.g.-corrected
Red b.g.-corrected
(R. b.g.-c)/(G. b.g.-c)
Red intensity
Green intensity
Systematic name
Red b.g.
Gene function
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Data Normalization

• Normalize data to correct for variances
• Dye bias
• Location bias
• Intensity bias
• Pin bias
• Slide bias
• Control vs. non-control spots

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Data Normalization
Calibrated, red and green equally detected
Uncalibrated, red light under detected
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Data Normalization

• Assumptions
• Overall mean average ratio should be 1
• Most genes are not differentially expressed
• Total intensity of dyes are equivalent

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Intensity Dependent Normalization
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After Normalization
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Additional Normalization

• Pin dependent
• Similar to intensity dependent fit.
• Compute individual lowess fits for each pin group
• Within slide normalization
• After pin dependent normalization, log ratios for
  each pin are centered around 0
• Scale variance for each pin
• Uses MAD (median absolute deviation)

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Additional Normalization

• Dye swap
• Combine relative expression levels without
  explicit normalization
• Compute lowess fit for
• log2(RR/GG)/2 vs. log2(A A)/2
• Normalized ratio is
• log2(R/G) - c(A)
• where c(A) is the lowess prediction

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Content

• Biology background of microarray
• Design of microarray
• The workflow of microarray
• Image analysis of microarray
• Data analysis of microarray
• Discussion

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Data analysis

• Data filtering
• Fold change analysis
• Classification
• Clustering
• Future direction

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Microarray Data Classification
Microarray chips
Images scanned by laser
Gene Value D26528_at
193 D26561_cds1_at -70 D26561_cds2_at
144 D26561_cds3_at 33 D26579_at
318 D26598_at 1764 D26599_at
1537 D26600_at 1204 D28114_at
707
Datasets
New sample
Data Mining and analysis
Prediction
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The Threshold of Spots

• Filtering - remove genes with insufficient
  variation
• Remove insufficient spot
• saturated, None uniform, too high background
• Remove extreme signal
• e.g. MaxVal - MinVal lt 500 and MaxVal/MinVal lt
  5
• Statistical filtering (e.g. p-valuelt0.01)
• biological reasons
• feature reduction for algorithmic

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Microarray Data Analysis Types

• Different gene expression
• Fold change analysis
• Classification (Supervised)
• identify disease
• predict outcome / select best treatment
• Clustering (Unsupervised)
• find new biological classes / refine existing
  ones
• exploration

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Differential Gene Expression

• n-fold change
• n typically gt 2
• May hold no biological relevance
• Often too restrictive
• 2? expression
• Calculate standard deviation ?
• Genes with expression more than 2? away are
  differentially expressed

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Fold Changes-Scatter Plot
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Fold Changes Table
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Classification Multi-Class

• Similar Approach
• select top genes most correlated to each class
• select best subset using cross-validation
• build a single model separating all classes
• Advanced
• build separate model for each class vs. rest
• choose model making the strongest prediction

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Popular Classification Methods

• Decision Trees/Rules
• find smallest gene sets, but also false positives
• Neural Nets -
• work well if number of genes is reduced
• SVM
• good accuracy, does its own gene selection, hard
  to understand
• K-nearest neighbor - robust for small number
  genes
• Bayesian nets - simple, robust

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Multi-class Data Example

• Brain data, Pomeroy et al 2002, Nature (415), Jan
  2002
• 42 examples, about 7,000 genes, 5 classes
• Selected top 100 genes most correlated to each
  class
• Selected best subset by testing 1,2, , 20 genes
  subsets, leave-one-out x-validation for each

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Classification Other Applications

• Combining clinical and genetic data
• Outcome / Treatment prediction
• Age, Sex, stage of disease, are useful
• e.g. if Data from Male, not Ovarian cancer

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Clustering

• Goals
• Find natural classes in the data
• Identify new classes / gene correlations
• Refine existing taxonomies
• Support biological analysis / discovery
• Different Methods
• Hierarchical clustering, SOM's, etc

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SOM clustering

• SOM - self organizing maps
• Preprocessing
• filter away genes with insufficient biological
  variation
• normalize gene expression (across samples) to
  mean 0, st. dev 1, for each gene separately.
• Run SOM for many iterations
• Plot the results

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SOM K Mean By GeneSpring
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Hierarchical Clustering

• The most popular hierarchical clustering method
  used in microarray data analysis is the so called
  agglomerative method
• works with the data in a bottom-up manner.
• Initially, each data point forms a cluster and
  the algorithm works through the cluster sets by
  repeatedly merging the two which are the most
  similar or have the shortest distance.
• algorithm involves the computation of the
  distance or similarity matrix
• O(N2) complexity and thus is not very efficient.

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Hierarchical clustering
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Future directions

• Algorithms optimized for small samples (the no.
  of samples will remain small for many tasks)
• Integration with other data
• biological networks
• medical text
• protein data
• cost-sensitive classification algorithms
• error cost depends on outcome (dont want to miss
  treatable cancer), treatment side effects, etc.

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Integrate biological knowledge when analyzing
microarray data (from Cheng Li, Harvard SPH)
Right picture Gene Ontology tool for the
unification of biology, Nature Genetics, 25, p25
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Content

• Biology background of microarray
• Design of microarray
• The workflow of microarray
• Image analysis of microarray
• Data analysis of microarray
• Discussion

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Microarray Potential Applications

• Biological discovery
• new and better molecular diagnostics
• new molecular targets for therapy
• finding and refining biological pathways
• Mutation and polymorphism detection
• Recent examples
• molecular diagnosis of leukemia, breast cancer,
  ...
• appropriate treatment for genetic signature
• potential new drug targets

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Microarray Limitations

• Cross-hybridization of sequences with high
  identity
• Chip to chip variation
• True measure of abundance?
• Does mRNA levels reflect protein levels?
• Generally, do not prove new biology - simply
  suggest genes involved in a process, a hypothesis
  that will require traditional experimental
  verification.
• What fold change has biological relevance?
• Need cloned EST or some sequence knowledge --
  rare messages may be undetected
• Expensive!! Not every lab can afford experiment
  repeat.
• The real limitation is Bioinformatics

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Additional Information

• Review papers on microarray
• Genomics, gene expression and DNA arrays (Nature,
  June 2000)
• Microarray - technology review (Natural Cell
  Biology, Aug. 2001)
• Magic of Microarray (Scientific American, Feb.
  2002)
• Molecular biology tutorial
• http//www.lsic.ucla.edu/ls3/tutorials/

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Biological data retrieval systems Entrez
http//www.ncbi.nlm.nih.gov/Database/index.html

• A retrieval system for searching a number of
  inter-connected databases at the NCBI. It
  provides access to
• PubMed The biomedical literature (Medline)
• Genbank Nucleotide sequence database
• Protein sequence database
• Structure three-dimensional macromolecular
  structures
• Genome complete genome assemblies
• PopSet population study data sets
• OMIM Online Mendelian Inheritance in Man
• Taxonomy organisms in GenBank
• Books online books
• ProbeSet gene expression and microarray datasets
• 3D Domains domains from Entrez Structure
• UniSTS markers and mapping data
• SNP single nucleotide polymorphisms
• CDD conserved domains
• 2. Entrez allows users to perform various
  searches.