Introduction to Microarray and Data Analysis

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Introduction to Microarray andData Analysis

• By
• Han-Yu Chuang

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Biological background Molecular Biology
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The Central Dogmaof Molecular Biology
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Basic principles in physics, chemistry and
biology
Principles Known?
Physics Matter
Chemistry Compound
Biology Organism
Elementary Particles Yes
Genes No
Elements Yes
Every biological rule has exceptions!
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Measuring Gene Expression
Idea measure the amount of mRNA to see which
genes are being expressed in (used by) the cell.
Measuring protein would be more direct, but is
currently harder.
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Microarrays provide a means to measure gene
expression
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How to measure gene expression?
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A simple idea Northern Blot
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Technology Advanced
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What is Microarray?

• Put a large number (100K) of cDNA sequences or
  synthetic DNA oligomers onto a glass slide (or
  other substrate) in known locations on a grid.
• Label an RNA sample and hybridize
• Measure amounts of RNA bound to each square in
  the grid

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Imagination on Microarray
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Basic principles

• Main novelty is one of scale
• hundreds or thousands of probes rather than tens
• Probes are attached to solid supports
• Robotics are used extensively
• Informatics is a central component at all stages

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Major technologies

• cDNA probes (gt 200 nt), usually produced by PCR,
  attached to either nylon or glass supports
• Oligonucleotides (25-80 nt) attached to glass
  support
• Oligonucleotides (25-30 nt) synthesized in situ
  on silica wafers (Affymetrix)
• Probes attached to tagged beads

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Areas Being Studied with Microarrays

• Differential gene expression between two (or
  more) sample types
• Similar gene expression across treatments
• Tumor sub-class identification using gene
  expression profiles
• Classification of malignancies into known classes
• Identification of marker genes that
  characterize different tumor classes
• Identification of genes associated with clinical
  outcomes (e.g. survival)

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Applications

• Pathway Inference
• (Gene regulatory network prediction)

• Disease detection
• (probe detection classification)

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Principal uses of chips

• Genome-scale gene expression analysis
• Differentiation
• Responses to environmental factors
• Disease processes
• Effects of drugs
• Detection of sequence variation
• Genetic typing
• Detection of somatic mutations (e.g. in
  oncogenes)
• Direct sequencing

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cDNA chips

• Probes are cDNA fragments, usually amplified by
  PCR
• Probes are deposited on a solid support, either
  positively charged nylon or glass slide
• Samples (normally poly(A) RNA) are labelled
  using fluorescent dyes
• At least two samples are hybridized to chip
• Fluorescence at different wavelengths measured by
  a scanner

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Standard protocol for comparative hybridization
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cDNA microarray experiments

• mRNA levels compared in many different contexts
• Different tissues, same organism (brain v.
  liver)
• Same tissue, same organism (ttt v. ctl, tumor v.
  non-tumor)
• Same tissue, different organisms (wt v. ko, tg,
  or mutant)
• Time course experiments (effect of ttt,
  development)
• Other special designs (e.g. to detect spatial
  patterns).

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Web animation of a cDNA microarray experiment
http//www.bio.davidson.edu/courses/genomics/chip/
chip.html DNA Microarray Technique
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Yeast genome on a chip
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Brief outline of steps for producing a microarray

• cDNA probes attached or synthesized to solid
  support
• Hybridize targets
• Scan array

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• Using Microarray with cDNA or oligonucleotide

Building the Chip
MASSIVE PCR
PCR PURIFICATION and PREPARATION
PREPARING SLIDES
PRINTING
Preparing RNA
Hybing the Chip
POST PROCESSING
CELL CULTURE AND HARVEST
ARRAY HYBRIDIZATION
RNA ISOLATION
DATA ANALYSIS
PROBE LABELING
cDNA PRODUCTION
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cDNA microarrays
cDNA clones
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cDNA microarrays

• Compare the genetic expression in two samples of
  cells

PRINT cDNA from one gene on each spot
SAMPLES cDNA labelled red/green
e.g. treatment / control normal / tumor
tissue
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HYBRIDIZE Add equal amounts of labelled cDNA
samples to microarray.
SCAN
Laser
Detector
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Quantification of expression

• For each spot on the slide we calculate
• Red intensity Rfg - Rbg
• (fg foreground, bg background) and
• Green intensity Gfg - Gbg
• and combine them in the log (base 2) ratio
• Log2( Red intensity / Green intensity)

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Gene Expression Data

• On p genes for n slides p is O(10,000), n is
  O(10-100), but growing,

Slides
slide 1 slide 2 slide 3 slide 4 slide 5 1
0.46 0.30 0.80 1.51 0.90 ... 2 -0.10 0.49
0.24 0.06 0.46 ... 3 0.15 0.74 0.04 0.10
0.20 ... 4 -0.45 -1.03 -0.79 -0.56 -0.32 ... 5 -0.
06 1.06 1.35 1.09 -1.09 ...
Genes
Gene expression level of gene 5 in slide 4

Log2( Red intensity / Green intensity)
These values are conventionally displayed on a
red (gt0) yellow (0) green (lt0) scale.
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cDNA chip design

• Probe selection
• Non-redundant set of probes
• Includes genes of interest to project
• Corresponds to physically available clones
• Chip layout
• Grouping of probes by function
• Correspondence between wells in microtitre plates
  and spots on the chip

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Glass chip manufacturing

• Choice of coupling method
• Physical (charge), non-specific chemical,
  specific chemical (modified PCR primer)
• Choice of printing method
• Mechanical pins flat tip, split tip, pin ring
• Piezoelectric deposition (ink-jet)
• Robot design
• Precision of movement in 3 axes
• Speed and throughput
• Number of pins, numbers of spots per pin load

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Labeling and hybridization

• Targets are normally prepared by oligo(dT) primed
  cDNA synthesis
• Probes should contain 3 end of mRNA
• Need CoT1 DNA as competitor
• Specific activity will limit sensitivity of assay
• Alternative protocol is to make ds cDNA
  containing bacterial promoter, then cRNA
• Can work with smaller amount of RNA
• Less quantitative
• Hybridization usually under coverslips

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Scanning the arrays

• Laser scanners
• Excellent spatial resolution
• Good sensitivity, but can bleach fluorochromes
• Still rather slow
• CCD scanners
• Spatial resolution can be a problem
• Sensitivity easily adjustable (exposure time)
• Faster and cheaper than lasers
• In all cases, raw data are images showing
  fluorescence on surface of chip

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Microarray data on the Web

• Many groups have made their raw data available,
  but in many formats
• Some groups have created searchable databases
• There are several initiatives to create unified
  databases
• EBI ArrayExpress
• NCBI Gene Expression Omnibus
• Companies are beginning to sell microarray
  expression data (e.g. Incyte)

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Bioinformatics of microarrays

• Experimental(Array) design choice of sequences
  to be used as probes
• Analysis of scanned images
• Spot detection, normalization, quantitation
• Primary analysis of hybridization data
• Basic statistics, reproducibility, data
  scattering, etc.
• Comparison of multiple samples
• Clustering, SOMs, classification
• Sample tracking and databasing of results

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Biological question Differentially expressed
genes Sample class prediction etc.
Experimental design
Microarray experiment
16-bit TIFF files
Image analysis
(Rfg, Rbg), (Gfg, Gbg)
Normalization
R, G
Estimation
Testing
Clustering
Discrimination
Biological verification and interpretation
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Microarray Image Analysis

• Quantitation of fluorescence signals

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Scanner
PMT
Pinhole
Detector lens
Laser
Beam-splitter
Objective Lens
Dye
Glass Slide
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Images from scanner

• Resolution
• standard 10?m currently, max 5?m
• 100?m spot on chip 10 pixels in diameter
• Image format
• TIFF (tagged image file format) 16 bit (65536
  levels of grey)
• 1cm x 1cm image at 16 bit 2Mb (uncompressed)
• other formats exist e.g.. SCN (used at Stanford
  University)
• Separate image for each fluorescent sample
• channel 1, channel 2, etc.

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Images examples
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Practical Problems 1

• Comet Tails
• Likely caused by insufficiently rapid immersion
  of the slides in the succinic anhydride blocking
  solution.

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Practical Problems 2
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Practical Problems 3

• High Background
• 2 likely causes
• Insufficient blocking.
• Precipitation of the labeled probe.
• Weak Signals

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Practical Problems 4
Spot overlap Likely cause too much
rehydration during post - processing.
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Practical Problems 5
Dust
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Processing of images

• Addressing or gridding
• Assigning coordinates to each of the spots
• Segmentation
• Classification of pixels either as foreground or
  as background
• Intensity determination for each spot
• Foreground fluorescence intensity pairs (R, G)
• Background intensities
• Quality measures

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Addressing

• The measurement process depends on the addressing
  procedure
• Addressing efficiency can be enhanced by allowing
  user intervention (slow!)
• Most software systems now provide for both manual
  and automatic gridding procedures

Registration
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Problems in automatic addressing

• Misregistration of the red and green channels
• Rotation of the array in the image
• Skew in the array

Rotation
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Segmentation

• Segmentation methods
• Fixed circle segmentation
• Adaptive circle segmentation
• Adaptive shape segmentation
• Histogram segmentation

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Information Extraction

• Spot Intensities
• mean (pixel intensities).
• median (pixel intensities).
• Background values
• Local
• Morphological opening
• Constant (global)
• None
• Quality Information
• Area
• Circularity
• Signal to Noise ratio

Take the average
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Quantification of expression

• For each spot on the slide we calculate
• Red intensity Rfg - Rbg
• fg foreground, bg background, and
• Green intensity Gfg - Gbg
• and combine them in the log (base 2) ratio
• Log2( Red intensity / Green intensity)

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Microarray Data Normalization

• Why?
• To correct for systematic differences between
  samples on the same slide, or between slides,
  which do not represent true biological variation
  between samples.
• How do we know it is necessary?
• By examining self-self hybridizations, where no
  true differential expression is occurring.
• We find dye biases which vary with overall spot
  intensity, location on the array, plate origin,
  pins, scanning parameters,.
• Goals
• - Reduces systematic (not random) effects
• - Makes it possible to compare several arrays

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• Intensity-dependent normalization
• Here, run a line through the middle of the MA
  plot, shifting the M value of the pair (A,M) by
  cc(A), i.e.
• log2 R/G -gt log2 R/G - c (A)
• One estimate of c(A) is made using the LOWESS
  function of Cleveland (1979) LOcally WEighted
  Scatterplot Smoothing.

A(log Rlog G)/2 M(log R-log G)/2
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Normalization by controlsMicroarray Sample Pool
titration series
Pool the whole library
Control set to aid intensity- dependent
normalization Different concentrations in
titration series Spotted evenly spread across the
slide in each pin-group
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Differential Expression Which genes have changed?

• Goal
• Identify genes associated with covariate or
  response of interest
• Examples
• Qualitative covariates or factors treatment,
  cell type, tumor class
• Quantitative covariate dose, time
• Responses survival, cholesterol level
• Any combination of these!

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cDNA gene expression data

• Data on G genes for n samples

mRNA samples
sample1 sample2 sample3 sample4 sample5 1
0.46 0.30 0.80 1.51 0.90 ... 2 -0.10 0.49
0.24 0.06 0.46 ... 3 0.15 0.74 0.04 0.10
0.20 ... 4 -0.45 -1.03 -0.79 -0.56 -0.32 ... 5 -0.
06 1.06 1.35 1.09 -1.09 ...
Genes
Gene expression level of gene i in mRNA sample j

(normalized) Log( Red intensity / Green intensity)
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An expression profile like this for each gene
mRNA Cy5/Cy3 r
_
5
down-regulation repression
up-regulation induction
_
1
0
time / h
Start of experiment
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Co-Regulation -- Inference of function Genes
belonging to the same pathway are often showing
the same regulatory patterns (profiles) for a
variety of biological situations (or in a time
series). Hence, as a hypothesis, genes of unknown
function showing similar regulatory behaviour as
some genes of known function may have a similar
function.

• Which genes are differentially expressed ?
• Which genes are expressed in a similar way
  when comparing to expression profiles of genes
  with known function?
• (co-regulation)
• patterns of expression (diagnostic
  Fingerprinting)
• Reverse Engineering of genetic networks

Differential expression Comparing the
Transcriptomes for two different biological
samples (e.g. control, heat-shock) you are
interested in the subset of genes which are
expressed on different levels (up-/
down-regulated).
Expression-Fingerprinting Often in medical
applications it is of interest to characterize
the biological status of cells, e.g. the
severeness of tumor cells, to be able to respond
with the right therapy.
Reverse Engineering Using expression data to
infer regulatory interactions between a number of
genes responsible for a certain adaptation
process or developmental process.
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Common methods

• t-Test
• Fisher
• Golub

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• TNOM
• Wilcoxon
• WEPO

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Weighted Punishment on Overlap (WEPO)

• Combine heuristics from para- and non-parametric
  methods.
• If a gene is differentially expressed, the
  expression value of different groups should come
  from quite different distributions.

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The better the genes, the less the overlap

• Score each gene via estimating the overlapped
  regions of these classes.
• To prevent information loss and maintain
  robustness.

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Formula of Weighted Punishment
Where
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An Example
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Another Example
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Other Higher AdvancedMicroarray data analysis

• Clustering and pattern detection
• Data mining and visualization
• Controls and normalization of results
• Statistical validation
• 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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Cluster analysis

• Used to find groups of objects when not already
  known
• Unsupervised learning
• Associated with each object is a set of
  measurements (the feature vector)
• Aim is to identify groups of similar objects on
  the basis of the observed measurements

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Clustering Gene Expression Data

• Can cluster genes (rows), e.g. to (attempt to)
  identify groups of co-regulated genes
• Can cluster samples (columns), e.g. to identify
  tumors based on profiles
• Can cluster both rows and columns at the same time

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Clustering Gene Expression Data

• Leads to readily interpretable figures
• Can be helpful for identifying patterns in time
  or space
• Useful (essential?) when seeking new subclasses
  of samples
• Can be used for exploratory purposes

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Types of Clustering

• Herarchical
• Link similar genes, build up to a tree of all
• Kmeans
• - Partition genes into a prespecified number
  of groups K
• 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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Hierarchical Clustering
3 clusters?
2 clusters?
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K-means Clustering
The intended clusters are found.
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Hierarchical clustering
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Hierarchical clustering (continued)
To transform the genesexp matrix into
genesgenes matrix, use a gene similarity
metric. (Eisen et al. 1998 PNAS 9514863-14868)
Exactly same as Pearsons correlation except the
underline
Where Gi equal the (log-transformed) primary data
for gene G in condition i. For any two genes X
and Y observed over a series of N conditions.
Goffset is set to 0, corresponding to
fluorescence ratio of 1.0
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Hierarchical clustering (continued)
Pearsons correlation example
What if genome expression is clustered based on
negative correlation?
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Hierarchical clustering (continued)
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K-means clustering
This method differs from the hierarchical
clustering in many ways. In particular, - There
is no hierarchy, the data are partitioned. You
will be presented only with the final cluster
membership for each case. - There is no role for
the dendrogram in k-means clustering. - You must
supply the number of clusters (k) into which the
data are to be grouped.
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K-means clustering(continued)
Step 1 Transform n (genes) m (experiments)
matrix into n(genes) n(genes) distance matrix
Step 2 Cluster genes based on a k-means
clustering algorithm
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K-means clustering(continued)
To transform the nm matrix into nn matrix, use
a similarity (distance) metric.
(Tavazoie et al. Nature Genetics. 1999
Jul22(3)281-5)
Euclidean distance
Where any two genes X and Y observed over a
series of M conditions.
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K-means clustering(continued)
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K-means clustering algorithm
Step 1 Suppose distance of genes expression
patterns are positioned on a two dimensional
space based a distance matrix
Step 2 The first cluster center(red) is chosen
randomly and then subsequent centers are
by finding the data point farthest from the
centers already chosen. In this example, k3.
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K-means clustering algorithm(continued)
Step 3 Each point is assigned to the
cluster associated with the closest
representative center
Step 4 Minimizes the within-cluster sum of
squared distances from the cluster mean by
moving the centroid (star points), that is
computing a new cluster representative
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K-means clustering algorithm(continued)
Step 5 Repeat step 3 and 4 with a new
representative
Run step 3, 4 and 5 until no further changes
occur.
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Web links

• Leming Shis Gene-Chips.com page very rich
  source of basic information and commercial and
  academic links
• DNA chips for dummies animation
• A step by step description of a microarray
  experiment by Jeremy Buhler
• The Big Leagues Pat Brown and NHGRI microarray
  projects

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Mini-Review How to make a cDNA microarray
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Glass Slide Array of bound cDNA probes 4x4
blocks 16 print-tip groups
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Microarray Experiment
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HybridizationBinding cDNA samples (targets) to
cDNA probes on slide
cover slip
Hybridise for 5-12 hours
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Quantification of expression

• For each spot on the slide we calculate
• Red intensity Rfg - Rbg
• fg foreground, bg background, and
• Green intensity Gfg - Gbg
• and combine them in the log (base 2) ratio
• Log2( Red intensity / Green intensity)

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Some Considerations for cDNA Microarray
Experiments (I)

• Scientific (Aims of the experiment)
• Specific questions and priorities
• How will the experiments answer the questions
• Practical (Logistic)
• Types of mRNA samples reference, control,
  treatment, mutant, etc
• Source and Amount of material (tissues, cell
  lines)
• Number of slides available

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Some Considerations for cDNA Microarray
Experiments (II)

• Other Information
• Experimental process prior to hybridization
  sample isolation, mRNA extraction, amplification,
  labelling,
• Controls planned positive, negative, ratio,
  etc.
• Verification method Northern, RT-PCR, in situ
  hybridization, etc.

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Experimental Design

• Ensure questions of interest can be answered
  accurately, under some constraints
• Cost, number of slides
• Biological material, availability of mRNA

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Combining data across slides

• Data on m genes for n hybridizations

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The design issue here

• Determine which mRNAs are to be labeled with
  which fluor, and which are to be hybridized
  together on the same slide.
• i.e, How the samples are paired onto arrays.

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Graphical Representation

• Multi-digraph
• Vertices mRNA samples
• Edges hybridization
• Direction dye assignment

Cy3 sample
Cy5 Sample
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Comparing K treatments

• Common reference design
• Extensibility
• All-pairs design
• Better in precision
• Comparison within slides

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On Graphical Representation

• 2 mRNA samples can be compared if there is a path
• The precision depends on the number of paths
• Direct comparisons within slides more precise
  than indirect ones

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Treatment vs Control

• Two samples
• e.g. KO vs. WT or mutant vs. WT

Indirect
Direct
T
Ref
T
C
C
Ref
average (log (T/C))
log (T / Ref) log (C / Ref )
?2 /2
2?2
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Common reference

A
B
C
Ref
All pairs
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The problem

• We suppose comparison between all pairs of
  varieties are of equal interest.
• For the number of arrays budgeted for an
  experiment, which design should we use to gain
  more precision?

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V5 , S8
2
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1
?
?
?
5
3
?
4
V6 , S8
?
?
?
?
?
?
?
?
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?
?
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V7 , S9
?
?
?
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?
?
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From puppy (2002)
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Traditional ways

• Generate the full sets of non-isomorphic
  connected designs of given the number of arrays
  and samples.
• Then calculate each average variance.

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Difficulties

• 11,716,571 non-isomorphic connected graphs on 10
  nodes. (almost 58 hrs)
• 1,006,700,565 on 11
• 164,059,830,476 on 12
• Its too time-consuming by using such strategy.

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Our strategy

• Using GA to be a smart search method, we dont
  need to explore all designs but get the optimal
  one.
• For v 5, a 8 -gt 1.6 secs
• v 6, a 8 -gt 2.1 secs
• v 7, a 9 -gt 2.7 secs
• v 10, a 20 -gt 129.416 secs
• v 12 , a 14 -gt 136.481 secs
• v 13 , a 15 -gt 175.388 secs

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1
?
2
12
?
?
11
3
?
?
4
10
?
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?
?
5
9
?
?
6
8
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7
V12 , S24
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Statistical model
For any particular gene
B
A
Sample
Expression level
Intensity
G
R
Intensity ? Expression level
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Example Time course
T1
T2
T3
T4
t1
t2
t3
t4
t1, t2, t3, t4 true expression levels
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T1 VS. T2
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T1 VS. T2
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T3 VS. T4
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