1. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Image Analysis on cDNA Microarray Data Demo of Spot Jean Yang October 24, 2000 Genetics & Bioinformatics Meetings

2. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, cDNA clones (probes) PCR product amplification purification printing microarray Hybridise target to microarray mRNA target) excitation laser 1laser 2 emission scanning analysis overlay images and normalise 0.1nl/spot

3. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research,Scanner Laser PMT Dye Glass Slide Objective Lens Detector lens Pinhole Beam-splitter

4. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Scanner Process Dye Photons ElectronsSignal LaserPMT A/D Convertor excitation amplification Filtering Time-space averaging

5. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, How to adjust for PMT? Cy3Cy5 1600600 2650600 3650650 4700650 5650700 6700700 7750750 saturated Very weak

6. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, After normalisation In addition, the ranking of the genes stays pretty much the same.

7. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Practical Problems 1 Comet Tails Likely caused by insufficiently rapid immersion of the slides in the succinic anhydride blocking solution.

8. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Practical Problems 2

9. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Practical Problems 3 High Background 2 likely causes: –Insufficient blocking. –Precipitation of the labeled probe. Weak Signals

10. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Practical Problems 4 Spot overlap: Likely cause: too much rehydration during post - processing.

11. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 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.

12. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 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

13. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Addressing 4 by 4 grids Within the same batch of print runs. Estimate the translation of grids Other problems: -- Mis-registration -- Rotation -- Skew in the array

14. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 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.

15. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Seeds

16. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Limitation of circular segmentation —Small spot —Not circular Results from SRG

17. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Information Extraction —Spot Intensities —mean (pixel intensities). —median (pixel intensities). —Background values —Local —Morphological opening —Constant (global) —None —Quality Information Take the average

18. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Local Backgrounds

19. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Statistical Software - R

20. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Who are we comparing? Spot (SRG) –valley –morph ScanAlzye (fixed circle) GenePix (adaptive circle) QuantArray –Fixed circle –Adaptive (Chen’s method) –Histogram

21. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, How are we comparing? Foreground and Background Intensities M vs A plot Within slide variability Between slide variability Ability to differentiate important genes from noise

22. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Foreground and Background comparison

23. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Does the image analysis matter? Spot.nbg Spot.morph Spot.valley ScanAlyze

24. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Background makes a difference Background methodSegmentation methodExp1 Exp2 S.nbg66 Gp.nbg76 SA.nbg66 No backgroundQA.fix.nbg76 QA.hist.nbg76 QA.adp.nbg1414 S.valley1721 GP1111 Local surroundingSA1214 QA.fix1823 QA.hist98 QA.adp2726 OthersS.morph99 S.const1414 Medians of the SD of log 2 (R/G) for 8 replicated spots multiplied by 100 and rounded to the nearest integer.

25. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Between slide variability

26. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, T

27. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Adjusted p-values

28. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Acknowledgments Terry Speed Michael Buckley Sandrine Dudoit Natalie Roberts Ben Bolstad CSIRO Image Analysis Group Ryan Lagerstorm Richard Beare Hugues Talbot Kevin Cheong Matt Callow (LBL) Percy Luu (USB) Dave Lin (USB) Vivian Pang (USB) Elva Diaz (USB) WEHI Bioinformatics group

29. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 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.

30. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Steps in Image Processing 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 3. Information Extraction

31. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, Addressing Registration

32. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, 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

33. Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, The Walter and Eliza Hall Institute of Medical Research, T