DNA Microarray Image Analysis - University of Illinois at Urbana ...

February 4, 2005
DNA Microarray Image Analysis
Peter Bajcsy, PhD
Research Scientist, Automated Learning Group
National Center for Supercomputing Applications (NCSA)
Adjunct Assistant Professor, CS and ECE Departments
University of Illinois at Urbana-Champaign (UIUC)
pbajcsy@ncsa.uiuc.edu

Outline
• Microarray Problem – Introduction
• Microarray Technology
• Microarray Data Processing Workflow
• Microarray Image Analysis
— Grid Alignment Problem
— Foreground Separation
— Spot Quality Assessment
— Quantification and Normalization
• Microarray Data Fusion and Visualization
• Summary
2

Publications
• Journals:
— Bajcsy P. “GridLine: Automatic Grid Alignment in DNA
Microarray Scans,” IEEE Transactions on Image Processing,
VOL 13, NO 1, pp.15-25, January 2004. (accessible at URL:
http://alg.ncsa.uiuc.edu/do/documents/publications)
• Book chapters:
— Bajcsy P., L. Liu and M. Band, “DNA Microarray Image
Processing,” Chapter of the book "DNA Array Image
Analysis: Nuts&Bolts" by Gerda Kamberova, Ph.D. (Ed.)
published by DNA Press (in press).
— Bajcsy P., J. Han, L. Liu and J. Young, “Survey of Bio-Data
Analysis from Data Mining Perspective,” Chapter 2 of Jason
T. L. Wang, Mohammed J. Zaki, Hannu T. T. Toivonen, and
Dennis Shasha (eds.), Data Mining in Bioinformatics,
Springer Verlag, 2004, pp.9-39.
3

4
MICROARRAY PROBLEM

Microarray Problem: Major Objective
• Major Objective: Discover a comprehensive theory of
life’s organization at the molecular level
— The major actors of molecular biology: the nucleic
acids, DeoxyriboNucleic acid (DNA) and RiboNucleic
Acids (RNA)
— The central dogma of molecular biology
Proteins are very
complicated molecules with
20 different amino acids.
How to measure processes at the molecular level?
=> microarray technology
5

DNA Microarray Preparation
• DNA microarrays are typically composed of
thousands of DNA sequences, called probes,
fixed to a glass or silicon substrate. The DNA
sequences can be long (500-1500bp) cDNA
sequences or shorter (25-70 mer)
oligonucleotide sequences. Oligonucleotide
sequences can be presynthesized and
deposited with a pin or piezoelectric spray or
synthesized in situ by photolithographic or
ink-jet technologies.
• Relative quantitative detection of gene
expression or gene copy number can be
carried out between two samples on one array
or by single samples comparing multiple
arrays.
• Double-fluorescent technique uses samples
from two sources that are labeled with
different fluorescent molecules (Cy3 and Cy5,
or Alexa 555 and Alexa 647) and hybridized
together on the same array.
6

Input and Output of Microarray Data Analysis
• Input: Laser image scans (data) and underlying experiment
hypotheses or experiment designs (prior knowledge)
• Output:
— Conclusions about the input hypotheses or knowledge about
statistical behavior of measurements
7
— The theory of biological systems learnt automatically from
data (machine learning perspective)
– Model fitting, Inference process

Overview of Microarray Problem
Biology Application Domain
Validation
Experiment
Design and
Hypothesis
Microarray
Experiment
Data Analysis
Image
Analysis
Data Warehouse
Knowledge discovery
in databases (KDD)
Statistics
Data
Mining
Artificial
Intelligence (AI)
8

Types of Expected Microarray Data Mining and Analysis
Results
Hypothetical Examples:
• Binary answers using tests of hypotheses
— Drug treatment is successful with a confidence level x.
• Statistical behavior (probability distribution functions)
— A class of genes with functionality X follows Poisson distribution.
• Expected events
— As the amount of treatment will increase the gene expression
level will decrease.
• Relationships
— Expression level of gene A is correlated with expression level of
gene B under varying treatment conditions (gene A and B are part
of the same pathway).
• Decision trees
— Classification of a new gene sequence by a “domain expert”.
9

10
MICROARRAY DATA PROCESSING
WORKFLOW

11
Microarray Data Processing Workflow

Challenges
• Numerical Value Analysis
— Image analysis
— Expression level analysis
• Annotation Analysis
— Prior knowledge (e.g., experimental conditions)
— Metadata (e.g., gene annotation)
• Numerical and Text Data Fusion
— Representation
— Common Keys
• Analysis of Fused Data
— Algorithms for numerical, categorical and textual data
• Data Management
— Storage, retrieval, updates
• Computational Requirements
— Large search space & modeling complexity
12

DNA Microarray Image Analysis
• The goal of microarray image analysis steps is to extract
intensity descriptors from each spot that represent gene
expression levels and input features for further analysis.
Biological conclusions are then drawn based on the results from
data mining and statistical analysis of all extracted features.
• Components of DNA Microarray Analysis
— Grid Alignment Problem
— Foreground Separation
— Quality Assurance
— Quantification
— Normalization
• Data management - Minimal Information About Microarray
Experiments (MIAME) standard
13

14
MICROARRAY IMAGE PROCESSING
REQUIREMENTS

Ideal Microarray Image?
1. Ideal cDNA microarray image in terms of its image content:
• Deterministic grid geometry
• Known background intensity with zero uncertainty
• Pre-defined spot shape (morphology)
• Constant spot intensity that (a) is different from the
background, (b) is directly proportional to the biological
phenomenon (up- or –down-regulation), and (c) has zero
uncertainty for all spots.
Ideal cDNA microarray image content => utopia
2. Ideal cDNA microarray image in terms of statistical confidence:
• A very large number of pixels per spot (theoretically it would
reach infinity)
Constraints: Cost of experiments, image resolution (scanners),
storage of extremely high resolution images and other
specimen preparation issues
15

Sources of Microarray Image Variations
• The cDNA technology is a complex electrical-optical-chemical
process that spans cDNA slide fabrication, mRNA preparation,
fluorescence dye labeling, gene hybridization, robotic
spotting, green and red fluorophores excitation by lasers,
imaging using optics, slide scanning, analog to digital
conversion using either charge-coupled devices (CCD) or
photomultiplier tubes (PMT), and finally image storage and
archiving.
• Variations: technologies, microarray image channels, file
formats, data accuracy, grid geometry, background, spot
morphology, foreground and background intensity
• Computational Requirements: repeatability (parameter “free”
algorithms), sufficient storage and computational resources
16

Microarray Image Technologies
• Affymetrix chips
• Uses photolithography and solid-phase chemistry
to produce arrays containing hundreds of
thousands of oligonucleotide probes packed at
extremely high densities.
• Single-, double- or multi-fluorescent cDNA
microarray images
• Variable substrates: coated glass slides or nylon
membrane or 2D gel materials.
• Example: 532nm (red) and 632nm (green)
wavelengths forming two channels.
• Images obtained by other labeling schemes, for
example, with or without radio-isotopic labels lead
to images with bright background and dark spots.
17

Examples: Number of Channels, Multiple Technologies
Double-Fluorescent Dye
Radioactive Dye
18

Variations of Grid Geometry
• Rotation
• Multiple grids.
• Missing rows
19

Variations of Background
• Slide washing
• Examples of
background noise
that could be
modeled with PDF
models of noise.
(Normal PDF – left
and Student’s t PDF
–right).
20

Variation of Spot Morphology
• Spot morphologies other than
circular
• Spatial and morphological
variations of spots (from left to
right, top row first): (a) a regular
spot, (b) an inverse spot or a ghost
shape, (c) a spatially deviating
spot inside of a grid cell, (d) a spot
radius deviation, (e) a tapering
spot or a comet shape, (f) a spot
with a hole or a doughnut shape,
(g) a partially missing spot and (h)
a scratched spot.
21

Variations of Foreground and Background Intensity
Kansas
University
22

Examples: Spatially Varying Background Noise, Low SNR
http://stat-www.berkeley.edu/users/terry/zarray/Html/begin.html
University
of California
-Berkeley
Keck Center
UIUC
23

Other than cDNA Microarray Images
• Color changing chemicals for odor
detection
• Disease specific assays (in silicon)
— Cystic fibrosis carrier screening
(hybridization-based single nucleotite
polymorphism platform configured on a
multi-layered optically coated silicon
surface: gold appearance under white
light & blue appearance after binding of
anti-biotin to horse-radish peroxide due
to attenuation of wavelengths)
24

25
Examples: Spatial Resolution, Line Spacing, Grid Arrangement

26
Examples: Illumination, Noise, Data Type, Missing Spots

IMAGE ANALYSIS:
MICROARRAY GRID ALIGNMENT
(SPOT FINDING OR GRIDDING)
27

Microarray Grid Alignment: Objective
• The objective of the grid alignment step is to localize a twodimensional
(2D) array of spots in a microarray scan before any
information is extracted from the spots.
• Terminology: A 2D array of spots is also loosely denoted as a grid
of spots, while one array of spots among multiple 2D arrays in
one microarray scan is often denoted as a block or a sub-array of
spots
2D array of spots
Sub-array of spots
28

Grid Alignment: Application Domains
Application domains for the grid alignment problem:
• Animal science and plant science - DNA expression
level measurements (Microarray technology)
• Chemistry – olfactory sensors based on a number of
different chemically responsive dyes deposited on a
substrate (e.g., paper, film, silica). The color change of
the dyes is determined after exposure to different
analytes. The color change after exposure to different
analytes can be accurately measured to identify
unknown compounds and concentration levels of known
compounds.
• Crystallography – 2D and 3D structure measurements
using X-ray
29

Classification of Grid Alignment Methods
There are two views on microarray grid alignment:
• Automation of methods:
— Manual (a grid template of spots is manually adjusted)
— Semi-automated (manual grid initialization followed by
automated refinement)
— Fully automated (data driven without any human
intervention based on one-time human setup)
• Image analysis approach:
— Template-based
— Data-driven
— Affymetrix chips (special case)
30

31
Microarray Grid Alignment: Previous Work
Approaches:
• The Affymetrix chips approach –
— Pros: the alignment problem was simplified and hence the grid
alignment became more accurate.
— Cons: the Affymetrix technology has been much more expensive
than the technology with coated glass slides.
• Template-based approach: the most prevalent in existing software
packages, e.g., GenePix Pro by Axon Instruments, ScanAlyze or
GridOnArray by Scanalytics.
— Pros: Incorporates knowledge about ideal grid
— Cons: manual alignment
• Data-driven approach:
— image segmentation
— statistical analysis of 1D image projections.
— Pros: automatic alignment
— Cons: accuracy and robustness, e.g., missing spots, noise

Previous Work: Template-Based Grid Alignment
• Template-based alignment results obtained by visually
aligning the left two columns (left) or the right two columns
(right) of microarray spots
32

Microarray Grid Alignment: Previous Work
• Enhancements of template-based matching:
— automatic refinement search for a grid location given size and
spacing of spots
• Enhancements of data-driven matching:
— Adaptive threshold segmentation
• The problem of irregular grid:
— template-based approaches fail without manual adjustment
— data-driven approaches are capable of finding irregular grids
but are prone to misalignment due to spurious or missing spots
and are also dependent on many parameters.
• Some software packages: GenePix, QuantArray, Array Vision,
ScanAnalyze, Spot Image Analysis and Dapple.
33

34
Grid Alignment Requirements
Ideal grid alignment algorithm:
• Finds irregularly row- and column-spaced 2D arrays with translational and
rotational offsets.
• Motivation: The dipping pins bend over time and cause irregularity in a 2D
arrangement of the printed spots. Any rotational offset of a slide or dipping pins
will cause a rotated 2D grid in a microarray image with respect to the image
edge.
• Performs alignment on images with any number of input channels.
• Motivation: Many image acquisition types.
• Is color and spot size independent.
• Motivation: Many image acquisition types (color). Preparation steps (spot size).
• Is independent of any chosen primitive shape.
• Motivation: Technology development, e.g., CLONDIAG chip.
• Is parameter “free”.
• Motivation: Repeatability without any bias and optimal performance.
• Accommodates speed versus accuracy tradeoffs.
• Motivation: Inspection versus Analysis

35
Grid Alignment Algorithm Overview

Channel Fusion
How to Process Multi-Channel Images?
- Answer: Channel Fusion by Boolean OR Function
Input Two-Band
Image
OR =
36

Image Down-Sampling
• Design of real-time systems
• Accuracy versus computational requirements
• Accessing and processing millions of pixels is time consuming
• Sub-sampling versus Down-sampling
37

Line Score Computation
∑ ∑ ∑ ∑
row j=col+kernel row+kernel j=col+kernel
vertical
Edge ( row, col) = I( i, j) −
I( i, j)
i= row− kernel j=col-kernel i=
row j=col-kernel
∑ ∑ ∑ ∑
col i=row+kernel col+kernel i=row+kernel
horizontal
Edge ( row, col) = I( i, j) −
I( i, j)
j= col− kernel i=row-kernel j=
col i=row-kernel
Directional Edge Detection
Results of
Directional
Edge Detection
38

Vertical and Horizontal Line Score
Score( Line) = ∑ HistValue( Line, i)
i > Sensitivity
Score Definition
Horizontal and
Vertical
Line Score
Functions
39

Angular Optimization
∑
N 2FindRow N 2FindCol
TotalScore ( Angle ) = Score ( Row , Angle ) + Score ( Col , Angle )
∑
i= 0 i
j=
0
j
Total Score Used for Optimizing Grid Rotation
40

Optional Regularity Enforcement
• Desired Configurations
— Irregular Rows & Columns
— Regular Rows
— Regular Columns
— Regular Rows & Columns
• Approach
— Histogram line spacing
— Line with the max score serves as the reference line
41

Parameter Optimization
• Input Variables:
— N2FindRow or N2FindCol is the number of expected rows or
columns in each grid
— MinAngle and MaxAngle define the range of expected grid
rotations in degrees
— DownSamp represents the down-sampling ratio if it is desirable to
reduce computational requirements
— Accept is the minimum score value from the range [0,100] for
reporting a valid grid line
— Sensitivity is the threshold value for separating background noise
from signal
— Kernel defines the spatial extend of directional edge detection
42

Processing Multiple Grids
Line Discontinuity
Approach
Filtering Approach
43

Experimental Evaluations
Error Metric
Total Misalignment Error
Synthetic and Measured
Test Data
-Tested Variations
•shape
•amount of intensity blur
•2D array arrangements
•rotated arrays
•downsampled arrays
•arrays with missing spots.
44

Spot Size & Spot Density
45
•Radius=2, 5, 9 and 12
•Spacing=along rows from [17, 22]
and along columns from [18, 24].

Image Blur (Noise Level)
Low-Pass Kernel =5 Low-Pass Kernel =10
Varying amount of signal blur
•kernel values in [3,13]
Misalignment errors
•within the range [14.22%, 22.09%].
Radius=5
46

Missing Spots
The fewer the spots in a line, the smaller line discrimination
(or detection robustness).
47

Angular Accuracy
-100% accuracy with
synthetic data
-probably less than
100% accuracy for
measured data due to
pixel location round-offs
during image rotation
48

Down-sampling
•Experimental results: Comparison
of results without any downsampling
and with down-sampling
by a factor of 2 and 3.
DownSamp
Parameter
1 2 3
Misalignment Error 6.4% 14.6% 21.3%
Execution Time 175ms 65ms 45ms
49

Grid Regularity?
Grid alignment results without (left) and with (middle)
regularity requirements imposed, and the difference
between the corresponding two grid masks (right).
Score=15.5% misalignment
50

Grid Alignment Properties
Color Invariant
Grid Primitive Invariant
51

Grid Alignment: Semi-Automated vs. Fully-Automated
Single Grid: one or more bands
Rotation
Speed
Background Noise
52

Multiple Grids: Semi-Automated vs. Fully-Automated
Grid Regularity
Multiple Grids
Multiple Grid Setup
53

Grid Alignment: Summary
• A novel data-driven grid alignment algorithm
— detects irregularly row- and column-spaced spots in a 2D array
— is independent of spot color and spot size
— localizes a grid of other primitive shapes than the spot shapes
— performs grid alignment on any number of image channels
— reduces the number of free parameters to minimum by data
driven optimization of most algorithmic parameters
— has a built-in speed versus accuracy tradeoff mechanism to
accommodate user’s requirements on performance time and
accuracy of the results.
• Open issues and future work:
— Missing spots & robustness
— Parallel processing, FPGA implementation (SGI effort)
— Parameter optimization
54

55
MICROARRAY FOREGROUND
SEPARATION

Foreground Separation
The goal of foreground separation is to identify pixels that
belong to foreground (signal) of expected spot shape and to
background.
Foreground separation methods using:
• spatial templates
• intensity based clustering
• intensity based segmentation
• spatial and intensity information.
56

Foreground Separation Using Spatial Templates
• Separation using spatial co-centric circular templates
57

Foreground Separation Using Intensity Based Clustering
• Examples of accurate (left – original image, and second from
left - label image) and inaccurate (second from right –
original image, and right – label image) foreground
separation using intensity based clustering.
58

Foreground Separation Using Intensity Based Segmentation
• Frequently used seeded region growing and watershed
segmentation methods
• An example of pros and cons of foreground separation using
intensity based clustering and segmentation. Left – original
image, middle – segmentation result and right – clustering
result.
• Multiple interpretations of the original grid cell image on the
left side
59

Foreground Separation Using Spatial And Intensity Information
• Spatially constrained segmentation and clustering
• Mann-Whitney statistical testing
• Spatial and intensity trimming
A couple of grid cell examples where contamination pixels have to
be trimmed
60

Foreground Separation From Multi-Channel Microarray Images
• Four types of separation boundaries for spots versus
background.
— Hypersphere
— Volume
— Hyperplane
— Point in a projected space
61

Separating Spots from Multi-Channel Imagery - Assumptions
Boundary Type Statistical Assumptions Threshold Values
Hyperphere Gaussian Distribution (scalar)
µ + kσ
dist
dist
Volume Uniform Distribution (1 point)
V
min, pts
=
1
k
V
min,max
Hyperplane Linearly Correlated Distributions (2 points)
r
v = ( µ ,min); µ ∈Plane,min
⊥ Plane
Nonlinear Using Projections
Gaussian Distribution in Projected
Space
(scalar)
µ + kσ
projected
projected
62

Step 1: Separating Spots from Background - Example
Original image (400x400 image size, SHORT datum type)
Spot pixel counts
• Hypersphere: 15913
• Volume: 509
• Hyperplane: 15877
• Nonlinear after AND operation: 13735
• Nonlinear after OR operation: 16045
63

64
MICROARRAY SPOT QUALITY
ASSESSMENT

Goals and Objectives of Quality Assessment
The main goals of image-based spot quality assessment (or grid
screening) are:
• to identify grid cells that contain valid spots
• to eliminate invalid spots from further analysis
• The QA objective is to assess thoroughly a spot quality and
associate a quality score with each spot as a reliability
coefficient during further processing.
• The QA automation objective is to completely eliminate any
human interaction, and detect any systematic and unexpected
errors.
65

Spot Validity Criteria
Spot validity criteria:
• Assess foreground and background intensities.
— (a) absolute background and foreground levels,
— (b) background variation,
— (c) foreground saturation and
— (d) foreground-to-background intensity ratio (or signal-tonoise
ratio).
• Evaluate morphological properties of foreground
— spot shape and size irregularities
— spot location (position offset)
66

Quality Assessment: Spot Examples
• Spatial and topological variations of spots
— (a) a regular spot
— (b) an inverse spot or a ghost shape
— (c) a spatially deviating spot inside of a grid cell
— (d) a spot radius deviation
— (e) a tapering spot or a comet shape
— (f) a spot with a hole or a doughnut shape
— (g) a partially missing spot
— (h) a scratched spot.
67

Criteria for Assessing Background and Foreground Intensities
• Background intensity variations
• Foreground and background intensity uniformity
q
STAT
FRG
• Foreground intensity saturation
• Signal-to-noise ratio
q
µ
GLOBAL
GLOBAL
LOC& GLOB 1 BKG
LOC& GLOB 2
BKG
BKG
= ; q
LOCAL GLOBAL BKG
=
LOCAL GLOBAL
µ
BKG
+ µ
BKG
mBKG + mBKG
σ
FRG
ABS
= 1− ,max ,min ,max ,min
1 I FRG
− I FRG ABS
BKG BKG
FRG
;
BKG
1
I −
q = − q = −
I
µ
Range
q
FRG
CONT
SATURATION
count
= 1−
saturated
count
m
( ) ( )
Range
all
q
CATEG
SATURATION
=
1; if count < T %
saturated
0; if count ≥T
%
saturated
MEAN
MEDIAN
q = µ /( µ + µ ); q = m /( m + m )
SNR FRG FRG BKG SNR FRG FRG BKG
68

Criteria for Assessing Morphological Properties of Foreground
• Spot shape
— Area-based
q
A −A A−
A
= ; q = exp( − )
AREA1 0 AREA2
0
SHAPE
SHAPE
A0 A0
— Perimeter-based
q
AREA3
SHAPE
=
A−
A
A
0
*100%
q
PERIM
SHAPE
= 4π
A
C
2
— Diameter-based
q
L−L L−L
= ; q = exp( − )
X−SECTION1 0 X−SECTION2
0
SHAPE
SHAPE
L0 L0
• Spot location (spot displacement or position offset)
— Distance-based
69

µσ
Spot Screening Quality Criteria: GenePix and QuantArray
Inspection Criteria
Description
SNR for each channel
Signal and background variability
Excessively high background
µ /( µ + µ ); m /( m + m )
k
sig sig bkg sig sig bkg
* µ / σ ; k * µ / σ
sig sig sig bkg bkg bkg
µ Global /( µ Global + µ ); m Global /( m Global + m )
bkg bkg bkg bkg bkg bkg
Saturation
−
count
1
saturated
count
all
Proportion of signal above µ+k*σ of background
Spot Shape
Foreground and background uniformity
count
> k*
σ
count
4π
A
Perim
2
all
; if µ > µ + kσ
then count + 1
sig bkg bkg > k*
σ
( Isig ,max
− Isig ,min
) ( Ibkg ,max
− Ibkg
,min
)
1 −
;1−
Range
Range
70

I2K Selecting Valid Pixels – SNR Method
• SNR is computed directly from mean of background and
foreground pixels
• SNR criterion eliminates spots with
— no signal (SNR~1),
— very weak signal (1

I2K Selecting Valid Pixels – Location & Size Method
• Method:
— 1. Estimate background (sample mean and standard deviation ) and
compute threshold
— 2. Spatial centroid of pixels that are above threshold define a new spot
center.
— 3. Background estimates are iteratively improved
— 4. We identify spots that have a spot centroid outside of the range [grid
cell center ± 0.25*grid cell size]
• Loc & Size method eliminates tapering spots (comets) and partially missing
spots.
Original Image Mask Image – Location & Size Screening
72

I2K Selecting Valid Pixels – Topology Method
• Method:
— 1. Perform connectivity analysis of signal pixels
— 2. Estimate spot radius from the connected set of pixels.
— 3. If the estimated radius deviates from the expected
radius by more than a user specified percentage of the
expected spot radius then the spot is eliminated.
• Topology screening criterion is aimed at eliminating spots
with scratches.
Original Image
Mask Image – Topology Screening
73

I2K Selecting Valid Pixels – Statistics Method
• Method:
— Estimate probability distribution function
(PDF) model of spot pixels
— Perform histogram of PDFs for all grid cells.
— Eliminate spots that do not follow PDF of the
majority of grid cells.
• Statistics criterion eliminates saturated spots.
Original Image
Mask Image – Statistics PDF Model Screening
74

75
MICROARRAY DATA
QUANTIFICATION AND
NORMALIZATION

Data Quantification
• Data quantification (or spot feature extraction) refers to
extracting descriptive values of foreground and background
pixels for each spot.
• Ideally, extracted descriptors (also called features or
attributes) should be directly proportional to the mRNA
quantity in the solution that was deposited in a spot, and
should represent the deposited gene expression level.
• In reality, fluorescent intensity measurements in each
channel might be scaled or distorted differently according
to some linear or non-linear functions during data
preparation steps.
76

Spot Descriptors
• Volume of foreground intensity
FRG Volume = ( µ − µ )* A
FRG BKG FRG
• Logarithmic ratio
des
X
RATIO
=
X
X
CHANNEL 0
FRG
CHANNEL1
FRG
des
⎛ X − X ⎞
= ⎜ ⎟
⎝
⎠
CHANNEL 0 CHANNEL 0
X WRT BKG FRG BKG
LOG RATIO
log 2 CHANNEL1 CHANNEL1
XFRG
− XBKG
• Regression Ratios
77

Spot Descriptors
• Visualization of Spot Descriptors
• Selection of Spot Descriptors
• Improving Robustness of Spot Descriptors
78

Visualization of Spot Descriptors
Selection and Visualization of Spot Descriptors
Feature Selection
Mean Feature Image
79

Normalization
• The motivation for normalizing microarray images and/or
extracted descriptors comes from the fact that one would
like to compare results obtained from multiple slides,
scanners, or laboratories, and with multiple microarray
techniques. The difficulty of performing meaningful
comparisons arises from different slide preparations (e.g.,
amounts of mRNA), scanner settings, microarray protocols
or labeling specifics.
• The purpose of normalization is to adjust for these
variations, primarily for label efficiency and hybridization
efficiency, so that we can discover true biological variations
as defined by the microarray experimental studies.
80

Normalization
• Normalization using Statistical Descriptors
— Z-Transformation
I
NORM STAT
Z −TRANSFORM
( row, col)
=
Irowcol ( , ) − µ
σ
— Background Correction
des
⎛ X − X ⎞
= ⎜ ⎟
⎝
⎠
CHANNEL 0 CHANNEL 0
X WRT BKG FRG BKG
LOG RATIO
log 2 CHANNEL1 CHANNEL1
XFRG
− XBKG
81

Normalization
• Normalization using control spots
— insert spots of known intensities or genes of known expression level
into a microarray slide
• Normalization using regression analyses
— within-slide normalization (location or scale),
– (a) location global normalization (log(red/green) – normalization
factor)
– (b) location intensity dependent normalization (log(red/green) –
normalization factor as a function of spot intensity),
– (c) location within-print-tip-group normalization (log(red/green) –
grid dependent normalization factor as a function of spot
intensity) and
– (d) scale normalization (modeling spread of various print-tip
groups)
— paired-slide normalization (dye-swap),
— multiple slide normalization
82

83
MICROARRAY DATA FUSION,
ANALYSIS AND
VISUALIZATION

Open Problems
• Problem statements:
• Problem #1: Given gene annotations and tabular information
about expression level, fuse and visualize the information
• Problem #2: Given a data base of microarray experiments
(only numerical expression level information), find the best
match of a new microarray experiment in the data base.
• Problem #3: Incorporate multiple knowledge discovery
techniques into analyses
84

Visualization
• Data: 3D cubes,distribution charts, curves, surfaces, link
graphs, image frames and movies, parallel coordinates
• Results: pie charts, scatter plots, box plots, association rules,
parallel coordinates, dendograms, temporal evolution
Pie chart
Parallel coordinates
Temporal evolution
85

Visualization of Clustering Results
Class Labeling and Visualization
Isodata (K-means)
Clustering
Mean Feature Image
Label Image
86

Web-Based Documentation
87
http://alg.ncsa.uiuc.edu/tools/docs/i2k/manual/index.html

Summary
• Microarray Technology and Data Processing Workflow
• Microarray Image Analysis
— Grid Alignment Problem
— Foreground Separation
— Spot Quality Assessment
— Quantification and Normalization
— Additional Microarray Data Fusion and Visualization
• Needed
— Biologists to define a biologically meaningful
problems/scenarios/experiments
— Computer scientists to provide computational tools
— Analysts (statistics, artificial intelligence, knowledge discovery in
data bases) to introduce techniques
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