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

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research 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 Perform a high-resolution “DATA-SCAN”……(ctd)

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research 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. GenePix 4000a Microarray Scanner Protocol, ctd

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research How to adjust for PMT? Cy3Cy saturated Very weak

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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.

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research Practical Problems 5 Dust

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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.

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research Problems in automatic addressing Misregistration of the red and green channels Rotation of the array in the image Skew in the array Rotation

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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.

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research Examples of algorithms and software implementation

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research Limitation of fixed circle method SRGFixed Circle

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research Information Quality –Area –Circularity –Signal to Noise ratio

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, Walter and Eliza Hall Institute of Medical Research Quality of Array Distribution of areas. - Judge by eye - Look at variation. (e.g, SD) Cy3 area mean 57 median 56 SD Cy5 area mean 59 median 57 SD 24.34

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

Department of Statistics, University of California, Berkeley, and Division of Genetics and Bioinformatics, 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.