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 Photons Electrons Signal
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 type=Multi-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 Laser Pinhole Detector lens Beam-splitter Objective Lens
Dye
Glass Slide

6. How to adjust for PMT? Cy3 Cy5 1 600 600 2 650 600 3 650 650 4 700 650
Very weak
Cy3 Cy5
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 Weak Signals 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 pixel’s 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
Cy3 area
mean 57
median 56 SD

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
S.valley GP
Local surrounding SA
QA.fix
QA.hist 9 8
QA.adp
Others S.morph 9 9
S.const
Medians of the SD of log2(R/G) for 8 replicated spots multiplied by 100
and rounded to the nearest integer.