imArray - An Automated High-Performance Microarray Scanner Software for Microarray Image Analysis, Data Management and Knowledge Mining

Wei-Bang Chen and Chengcui ZhangDepartment of Computer and Information Sciences

University of Alabama at Birmingham

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Microarray Introduction

Tumor tissue Normal tissue

Labeled with different fluorescent dye (Cy3 / Cy5)

Microarray slide

Mix & Pour onto slide

Wash

HybridizationSamples compete the gene on the slide

If a gene in the sample is complementary to a gene on the slide, they will bind together

Microarray allows biologists to monitor gene expression level in parallel.

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Microarray slide image

Microarray Scanner532 nm / 635 nmMicroarray Slide

Microarray Slide Images532 nm / 635 nm

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Microarray slide layout

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This is a block

30 × 30 sp

ots

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Gene expression level

“Red / Green Intensity Ratio” represents the “Gene expression level”

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The challenge of microarray image analysis

Spot addressing problems Tilted slide Block detection Gridline detection

Segmentation problems Uneven background Inner holes (a donut, comet, or overlap) Scratch Noises

Data management problems Abundant information from unstructured documents

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Solutions

imArray - Microarray Image Analysis systemFully automatic image analysis

Orientation Gridding1

Segmentation1

Robust and efficient data managementUnstructured Information Management Architecture

(UIMA)

1. W.-B. Chen, C. Zhang, and W.-L. Liu, “An Automated Gridding and Segmentation Method for cDNA Microarray Image Analysis,” in Proc. of the 19th IEEE International Symposium on Computer-Based Medical Systems, pp. 893-898, 2006.

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UIMA Introduction

Developed at IBMComponent-based frameworkAnalysis Engine (AE)

Primitive AE & Aggregate AEAnnotatorComponent Descriptor

Common Analysis Structure (CAS)

Unstructured documents

Structured information

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System overview

Slide Information ModuleSlide Blocking ModuleSlide Gridding ModuleSlide Segmentation Module

Ap

plic

atio

n G

UI

Microarray Image Analysis System

App

licat

ion

GU

I

Slide Information(XML)

Microarray Images Channel 532 /635

CAS

Data (XMI)

Slide Gridding Module

SegmentationAnnotator

SegmentationAnnotator

Slide SegmentationModule

GridlinesAnnotator

GriddingAnnotator

Slide GriddingModule II

Bounding-Box Annotator

Bounding-BoxAnnotator

BlockingAnnotator

BlockingAnnotator

Slide Blocking Module

InformationAnnotator

InformationAnnotator

Slide InformationModule

CAS

CASSlide GriddingModule I

CAS

CAS CAS CAS CAS

CASCAS

Primitive Analysis Engine

AggregateAnalysis Engine

LegendComponent Descriptor Annotator

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Slide Information Module

GoalRetrieve information, such as probe set specification, in documents

Implementation Primitive Analysis Engine Analyze, parse, and retrieve information in XML documents Collaborate with agent-based automatic information retrieval

module for updating retrieved contents from online databases

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Fully automatic spot addressing

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Hor

izon

tal b

lock

bou

ndar

ies

Vertical block boundaries

Hor

izon

tal g

ridlin

es

Vertical gridlines

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Slide Blocking Module

Signal/Noise detectorDistinguish signal (foreground pixels) from noise (background pixels) by adopting a global-local thresholding technique1

Tilt detectorIdentify and correct a tiled slide by first determining the tilted angle, and then rotate the entire slide with affine transformation

Block boundary detectorDiscovers the repeated block patterns in a slide by detecting gaps between blocks and generates the horizontal and vertical block boundaries1

1. W.-B. Chen, C. Zhang, and W.-L. Liu, “An Automated Gridding and Segmentation Method for cDNA Microarray Image Analysis,” in Proc. of the 19th IEEE International Symposium on Computer-Based Medical Systems, pp. 893-898, 2006.

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Tilt detector

GoalIdentify and correct a tilted slide

Implementation Primitive analysis engine Detect tilted angle by Principal Component Analysis (PCA) Correct tilted slide with affine transformation

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Tilted angle detection on artificial slides

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Tilted angle detection on real slides

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Slide Gridding Module

GoalGenerates a grid within each block for separating spots, i.e. a cell in the grid contains only one spot1

Implementation Aggregate Analysis Engine including two primitive analysis engines: Bounding box generation Gridline detection

1. W.-B. Chen, C. Zhang, and W.-L. Liu, “An Automated Gridding and Segmentation Method for cDNA Microarray Image Analysis,” in Proc. of the 19th IEEE International Symposium on Computer-Based Medical Systems, pp. 893-898, 2006.

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Spot addressing results

Detecting blocks1

Recall value: 100%Precision value: 100%

Gridding1

Recall value: 99.97%Precision value: 100%

1. W.-B. Chen, C. Zhang, and W.-L. Liu, “An Automated Gridding and Segmentation Method for cDNA Microarray Image Analysis,” in Proc. of the 19th IEEE International Symposium on Computer-Based Medical Systems, pp. 893-898, 2006.

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Slide Segmentation Module

GoalRefine the class label within the grid region

Implementation Primitive analysis engine Determine local threshold – Otsu’s2

MinimizeIntra-class varianceBetween-class variance

2. V. R. Iyer, et al. "The transcriptional program in the response of human fibroblasts to serum,“ Science, v283, pp. 83-7, 1999.

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Spot segmentation results

Segmentation results for some sample spots1

In each row, from left to right:

1. Original spot2. Pre-labeled spot with

the segment boundary3. Spot segmentation

results4. GenePix

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Spot segmentation results

Segmentation results1

1. W.-B. Chen, C. Zhang, and W.-L. Liu, “An Automated Gridding and Segmentation Method for cDNA Microarray Image Analysis,” in Proc. of the 19th IEEE International Symposium on Computer-Based Medical Systems, pp. 893-898, 2006.

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Conclusions

Our proposed imArray system is fully automaticHandle uneven background and severe noiseDetect tilted slide and correct its orientationDetect block boundaries and generate gridsSpot segmentation method is simple and effectiveHighly parallelizable methodUpdate annotation automatically

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Thank you !!