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Low Level Statistics and Quality Control Javier Cabrera

Low Level Statistics and Quality Control Javier Cabrera. Outline. Processing Steps. Spotting the Raw Image Panel quality Array quality Area problems. Data processing steps. Preprocessing Gene Exp. Mat. Statistical Analysis. Scanned Image. Spotted Image. Preprocessing steps.

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Low Level Statistics and Quality Control Javier Cabrera

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  1. Low Level Statistics and Quality Control Javier Cabrera

  2. Outline • Processing Steps. • Spotting the Raw Image • Panel quality • Array quality • Area problems

  3. Data processing steps Preprocessing Gene Exp. Mat. Statistical Analysis Scanned Image Spotted Image

  4. Preprocessing steps • Microarrays are scanned • The image is converted into spot intensities for analysis. • Spot intensity: Reflects the amount of labeled probe that hybridized to it. • Run a series of quality checks on the data. • The spot intensity is adjusted for background effects.

  5. speckles (B) (A) Light Background (C) Shape Raw array

  6. Gridding: where are the spots? • Edge detection: Cho, Meer, Cabrera (1997) • Segmentation: which pixels correspond to the spot (signal) and which to background? • Measurement: what is the intensity at each spot? Spot intensity = average pixel intensity within the spot Background intensity = average pixel intensity immediately around the spot Spotting the Raw image

  7. Segmentation • Fixed circle segmentation: by fitting a circle with a constant diameter to all the spots. • Adaptive circle segmentation: fitting circles with different diameters to different spots • Seeded region growing algorithm (this works better) • Seed specification. • Region Growing. • Stopping Rule.

  8. Segmentation • Fixed circle segmentation: by fitting a circle with a constant diameter to all the spots. • Adaptive circle segmentation: fitting circles with different diameters to different spots • Seeded region growing algorithm (this works better) • Seed specification. • Region Growing. • Stopping Rule.

  9. Preprocessing the spot intensity values Spot quality: Assess the quality of the spots: • Spot Intensity • Background Intensity • Spot and background Std. Dev. Or CV • Spot Circularity • Signal to noise ratio Use Scatter plot and scatter matrices to look for patterns, outliers. Data example:

  10. Exploring Channel 1,2 Intensities, Background

  11. Spot quality -Spot Intensity -Background I -Circularity -Uniformity -Signal to Noise The graphs show some outliers but there are not very dramatic.

  12. Preprocessing the spot intensity values Panel and Array quality: Assess the quality of the panels and the entire arrays - Image plot - Boxplots of panels - Quality diagnostic plot from Bioconductor.

  13. Preprocessing the spot intensity values Target Info Probe Info = mrrayLayout : Structure, construction marrayInfo: Probe Anotation Intensity Measures : maRf, maGf, maRb, maGb, maW

  14. Systematic Effects • Changes in measurement scale due to: • Time: Day effect or other order • Operator • Batch

  15. Example of day Effect Day Two Day One

  16. Removal of Systematic Effects Day effect Day effect removed via linear model + normalized

  17. Area Effects Array quality: Assess the quality of the array - area problems

  18. Array Quality Image plot of a good array Signal Background

  19. Image plot of a defective array Signal Background

  20. Area Problems • Large spots covering a good part of the area of the background image. These spots show higher or lower intensities than the rest of the image. • Vertical or horizontal strips on the background image that show higher or lower intensities. • Diagonal strips again showing higher or lower background intensities. • A ramp in the background intensities going across the array. • Bleeding in the spotted image showing sequences of consecutive.

  21. Algorithm: Step 1. Split the image into high intensity and low intensity spots: Yrc=1 for high intensity Irc> c Yrc=0 for low intensity Irc  c c = (I0.05 + I0.95)/2 Step 2. Fit a quadratic discriminant function to the binary response Yrc using the spot coordinates (r,c) on the microarray as predictors. Step 3. Let q=proportion of correct classifications. Step 4. Generate M=300 images by randomly permute the rows and columns of {Irc} and for each image calculate the corresponding q1,…,q300. Step 5 Use prop{qi >q } as a quality measure of the array or p-value.

  22. Histogram of intensities Low High

  23. Microarray with an area problem

  24. Microarray with a ramp

  25. Good Microarray

  26. Image Quality Graph

  27. Microarray Graph

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