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Image Processing for cDNA Microarray Data

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Image Processing for cDNA Microarray Data Prepared with massive assistance from Yee Hwa Yang (Berkeley, WEHI), and reporting on work done jointly with her, Sandrine ... – PowerPoint PPT presentation

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Title: Image Processing for cDNA Microarray Data


1
Image Processing for cDNA Microarray Data
  • Prepared with massive assistance from Yee Hwa
    Yang (Berkeley, WEHI), and reporting on work done
    jointly with her, Sandrine Dudoit (Stanford) and
    Mike Buckley (CSIRO, Sydney).
  • References M Eisen and P Brown, Methods in
    Enzymology vol 303, 1999 Chapter 2, DNA
    Microarrays (ed M Schena, OUP 1999) by Mack J
    Schermer Chapter 13, Microarray Biochip
    Technology (ed M Schena, Eaton 2000) by Basarsky
    et al.

2
Scanner Process
A/D Convertor
Laser
PMT
Dye
Electrons
Signal
Photons
excitation
amplification
Filtering Time-space averaging
3
GenePix 4000a Microarray Scanner Protocol
  • 1. Turn on scanner.
  • 2. Slide scanner door open. Insert chip hyp side
    down and clip chip holder easily around the slide
  • 3 Set PMTs to 600 in both 635nm (Cy3) and 532
    (Cy5) channels.
  • 4. Perform low resolution PREVIEW SCAN to
    determine location of spots and initial hyb
    intensities
  • 5. Once scan location determined, draw a SCAN
    AREA marquis around the array
  • 6. Perform quick visual inspection of hyb and
    make initial adjustments to PMTs
  • 7. For gene expression hybs, raise or lower the
    red and green PMTs to achieve color balance
  • 8. Before you perform your data scan, change
    LINES TO AVERAGE to 2.
  • 9. Perform a high-resolution DATA-SCAN(ctd)

4
GenePix 4000a Microarray Scanner Protocol, ctd
  • 10. Observe the histograms and make adjustments
    to PMTs.
  • 11. Once the PMT level has been set so that the
    Intensity Ratio is near 1.00 perform a DATA
    SCAN over SCAN AREA and save the results.
  • 12. To save your image, select SAVE IMAGES.
  • 13. Save as typeMulti-image TIFF files.
  • 14. Once scanned and saved, you are ready to
    assign spot identities and calculate results.
  • Note For us, normalization is performed later
    during data analysis, see next lecture.

5
Scanner
PMT
Pinhole
Detector lens
Laser
Beam-splitter
Objective Lens
Dye
Glass Slide
6
How to adjust for PMT?
Very weak
Cy3 Cy5 1 600 600 2 650 600 3 650 650 4 700 650 5
650 700 6 700 700 7 750 750
saturated
7
After normalisation
In addition, the ranking of the genes stays
pretty much the same.
8
Practical Problems 1
  • Comet Tails
  • Likely caused by insufficiently rapid immersion
    of the slides in the succinic anhydride blocking
    solution.

9
Practical Problems 2
10
Practical Problems 3
  • High Background
  • 2 likely causes
  • Insufficient blocking.
  • Precipitation of the labeled probe.
  • Weak Signals

11
Practical Problems 4
Spot overlap Likely cause too much
rehydration during post - processing.
12
Practical Problems 5
Dust
13
Steps in Images Processing
1. Addressing locate centers
2. Segmentation classification of pixels either
as signal or background. using seeded region
growing).
3. Information extraction for each spot of the
array, calculates signal intensity pairs,
background and quality measures.
14
Steps in Image Processing
3. Information Extraction
  • Spot Intensities
  • mean (pixel intensities).
  • median (pixel intensities).
  • Pixel variation (IQR of log (pixel intensities).
  • Background values
  • Local
  • Morphological opening
  • Constant (global)
  • None
  • Quality Information

Signal
Background
15
Addressing
  • This is the process of assigning coordinates
    to each of the spots.
  • Automating this part of the procedure permits
    high throughput analysis.

4 by 4 grids 19 by 21 spots per grid
16
Addressing
  • Registration

Registration
17
Problems in automatic addressing
  • Misregistration of the red and green channels
  • Rotation of the array in the image
  • Skew in the array

Rotation
18
Segmentation methods
  • Fixed circles
  • Adaptive Circle
  • Adaptive Shape
  • Edge detection.
  • Seeded Region Growing. (R. Adams and L. Bishof
    (1994) Regions grow outwards from the seed
    points preferentially according to the difference
    between a pixels value and the running mean of
    values in an adjoining region.
  • Histogram Methods
  • Adaptive threshold.

19
Examples of algorithms and software implementation
20
Limitation of fixed circle method
SRG
Fixed Circle
21
Limitation of circular segmentation
  • Small spot
  • Not circular

Results from SRG
22
Information Extraction
  • Spot Intensities
  • mean (pixel intensities).
  • median (pixel intensities).
  • Background values
  • Local
  • Morphological opening
  • Constant (global)
  • None
  • Quality Information

Take the average
23
Local Backgrounds
24
Information
  • Quality
  • Area
  • Circularity
  • Signal to Noise ratio

25
Quality Measurements
  • Array
  • Correlation between spot intensities.
  • Percentage of spots with no signals.
  • Distribution of spot signal area.
  • Spot
  • Signal / Noise ratio.
  • Variation in pixel intensities.
  • Identification of bad spot (spots with no
    signal).
  • Ratio (2 spots combined)
  • Circularity

26
Quality of Array
Distribution of areas. - Judge by eye - Look at
variation. (e.g, SD)
  • Cy5 area
  • mean 59
  • median 57
  • SD 24.34
  • Cy3 area
  • mean 57
  • median 56
  • SD 20.67

27
Does the image analysis matter?
Spot.nbg
Spot.morph
Spot.valley
ScanAlyze
28
Background makes a difference
Background method Segmentation method Exp1
Exp2 S.nbg 6 6 Gp.nbg 7 6 SA.nbg 6
6 No background QA.fix.nbg 7 6 QA.hist.nbg
7 6 QA.adp.nbg 14 14 S.valley 17 21 GP
11 11 Local surrounding SA 12 14 QA.fix
18 23 QA.hist 9 8 QA.adp 27 26 Others
S.morph 9 9 S.const 14 14
Medians of the SD of log2(R/G) for 8 replicated
spots multiplied by 100 and rounded to the
nearest integer.
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