Low Level Statistics and Quality Control Javier Cabrera

Outline 1.Processing Steps. 2.Spotting the Raw Image 3.Panel quality 4.Array quality 5.Area problems

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

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.

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

u Gridding: where are the spots? u Edge detection: Cho, Meer, Cabrera (1997) u Segmentation: which pixels correspond to the spot (signal) and which to background? u 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

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) (i)Seed specification. (ii)Region Growing. (iii)Stopping Rule.

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) (i)Seed specification. (ii)Region Growing. (iii)Stopping Rule.

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

Exploring Channel 1,2 Intensities, Background

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

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

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

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

Example of day Effect Day Two Day One

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

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

Array Quality Image plot of a good array SignalBackground

Image plot of a defective array SignalBackground

Area Problems (i)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. (ii)Vertical or horizontal strips on the background image that show higher or lower intensities. (iii) Diagonal strips again showing higher or lower background intensities. (iv) A ramp in the background intensities going across the array. (v)Bleeding in the spotted image showing sequences of consecutive.

Algorithm: Step 1. Split the image into high intensity and low intensity spots: Y rc =1 for high intensity I rc > c Y rc =0 for low intensity I rc  c c = (I I 0.95 )/2 Step 2. Fit a quadratic discriminant function to the binary response Y rc 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 {I rc } and for each image calculate the corresponding q 1,…,q 300. Step 5 Use prop{q i >q } as a quality measure of the array or p-value.

Low High Histogram of intensities

Microarray with an area problem

Microarray with a ramp

Good Microarray

Image Quality Graph

Microarray Graph