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

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Spotted Image

PreprocessingGene Exp. Mat.

StatisticalAnalysis

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.

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(B)

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speckles

Light Background

Shape

Raw array

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

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:Spot IntensityBackground IntensitySpot and background Std. Dev. Or CVSpot CircularitySignal to noise ratio

Use Scatter plot and scatter matrices to look for patterns, outliers.

Data example:ch1 Intensity Std Devch1 Background Std Devch1 Diameter ch1 Area ch1 Footprint ch1 Circularitych1 Spot Uniformitych1 Bkg. Uniformitych1 Signal Noise Ratio

1683.537231 88.588715 99.014877 5800 80.174225 0.883462 0.959335 0.99839 56.744021039.019409 45.566654 100.925308 5800 78.812447 0.895682 0.976974 0.999084 54.52414472.640381 58.376431 102.179092 5800 81.879074 0.93141 0.987099 0.99894 25.6764538.243103 70.611359 104.641586 5800 74.847275 0.912625 0.985878 0.998634 24.4041

1929.476563 47.217567 92.705818 5800 75.951424 0.909981 0.948647 0.999176 134.2162

Preprocessing the spot intensity values

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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

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Target InfoProbe Info = mrrayLayout : Structure,

construction marrayInfo: Probe Anotation Intensity Measures : maRf, maGf, maRb, maGb,

maW

Preprocessing the spot intensity values

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Systematic Effects

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

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Example of day Effect

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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

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Array QualityImage plot of a good array

Signal Background

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Image plot of a defective array

Signal Background

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.

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Algorithm:Step 1. Split the image into high intensity and low intensity spots:

Yrc=1 for high intensity Irc > cYrc=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.

Intensity

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Col profile.

High and Low

p-val= 0

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Microarray with an area problem

Row profile.

Col profile.

High and Low

p-val= 0

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Microarray with a ramp

Row profile.

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p-val= 0.328

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Good Microarray

Image Quality Graph

Row profile.

Col profile.

High and Low

p-val= 0.116

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Microarray Graph

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1.0C : 24 Genes

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