1. Scanning and image analysis Scanning -Dyes -Confocal scanner -CCD scanner Image File Formats Image analysis -Locating the spots -Segmentation -Evaluating data quality by H. Bjørn Nielsen

2. Sample Preparation Hybridization Array design Probe design Question Experimental Design Buy Chip/Array Statistical Analysis Fit to Model (time series) Expression Index Calculation Advanced Data Analysis ClusteringPCAClassification Promoter Analysis Meta analysisSurvival analysisRegulatory Network Comparable Gene Expression Data Normalization Image analysis The DNA Array Analysis Pipeline

3. The cDNA Microarray technology

4. Scanning Images technology II Washing The cDNA Microarray

5. Labeling dyes and their properties The two most common flour dyes used are: Cyanine3 (cy3, absorption = 554, emission = 568) Cyanine5 (cy5, absorption = 650, emission = 672) But Alexa dyes are also popular

6. Cyanine dye spectra ExcitationEmissionExcitationEmission excitation and emission

7. Alexa dyes comparison of excitation spectra

8. Confocal scanner diagram

9. The confocal scanner scans the slide

10. Affymetrix prototype scanner Anno 1989

11. CCD scanner CCD camera Emission filter Excitation filter Beamsplitter White light detects from an area

12. Microarray The most common file format is 16bit TIFF. A 16bit TIFF file describe each pixel in an image with an intensity ranging from 0-65535 The image resolution is commonly 5-10  m Normally two scans in different wavelengths result in two monochrome files that are overlaid Cy3 Channel Cy5 Channel Pseudo-color overlay image formats

13. Image analysis Locating the spots Segmentation Ensuring good data quality (flagging) Density Intensity overview

14. Locating A grid is laid over the image to aid the program in identifying the individual spots Most programs have some automation in this step. the spot features

15. Spot Segmentation Fixed circle ScanAlyze, GenePix, QuantArray Adaptive circle GenePix, Dapple Adaptive shape Spot, (a R package) Histogram method ImaGene, QuantArray DeArray Density Intensity Overview

16. Fixed Circle Fits a circle with a constant diameter to all spots in the image - The spots need to be of the same shape and size segmentation

17. Adaptive Circle The circle diameter is estimated separately for each spot –Problematic if spot exhibits non-circular shapes segmentation

18. Adaptive Shape Starts by Specifying a starting points (given by the gridding) Regions grow outwards from the starting point according to the difference between a pixel’s value and the running mean of values in an adjoining region. segmentation

19. Histogram Density Intensity Uses a target mask chosen to be larger than the spot Foreground and background intensity are determined from the histogram of pixel values for pixels within the masked area Example : Background : mean between 5th and 20th percentile Foreground : mean between 80th and 95th percentile segmentation

20. Background Estimation QuantArray ScanAlyze Spot examples

21. Examples of for Difficult microarray images

22. Some factors that may challenge proper segmentation - Dust grains - Background smear - Strange shaped spot (comet tails, donuts shaped spots, etc.) - Weak signals Spot Irregularity examples

23. Quality Measures Measures to pickup such irregularity - Intensity variability measures - Spot size deviation - Circularity deviation - Relative signal to background intensity -Position deviates from a rectangular grid With such measurements bad spots can be flagged examples

24. Feature Intensity calculation The average or median pixel value in the spot and background masks are calculated.

25. Output FieldMeta RowMeta ColumnRowColumnGene_IDFlagSignal Mean Background Mean A1112ZY03007604655463 A1113ZY030066015938405 A1114ZY02920907441390 A1115ZY03008901842399 A1116ZY03008406864401 A1117ZY0070032471481 A1118ZY00686908576447 A1119ZY00795404965405 A11110ZY00686602236374 A11111ZY00678202088355 A11112ZY00690704726342 A11113ZY00659304437338 A11114ZY006850 0917321 example