1. imArray - An Automated High-Performance Microarray Scanner Software for Microarray Image Analysis, Data Management and Knowledge Mining Wei-Bang Chen and Chengcui Zhang Department of Computer and Information Sciences University of Alabama at Birmingham

2. 2 Microarray Introduction Tumor tissueNormal tissue Labeled with different fluorescent dye (Cy3 / Cy5) Microarray slide Mix & Pour onto slide Wash Hybridization Samples 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.

3. 3 Microarray slide image Microarray Scanner 532 nm / 635 nm Microarray Slide Microarray Slide Images 532 nm / 635 nm

4. 4 Microarray slide layout 1234 5678 9101112 13141516 17181920 21222324 25262728 29303132 33343536 37383940 41424344 45464748 This is a block 30 × 30 spots

5. 5 Gene expression level “Red / Green Intensity Ratio” represents the “Gene expression level”

6. 6 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

7. 7 Solutions imArray - Microarray Image Analysis system  Fully automatic image analysis Orientation Gridding 1 Segmentation 1  Robust and efficient data management Unstructured 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.

8. 8 UIMA Introduction Developed at IBM Component-based framework Analysis Engine (AE)  Primitive AE & Aggregate AE  Annotator  Component Descriptor Common Analysis Structure (CAS) Unstructured documents Structured information

9. 9 System overview Slide Information Module Slide Blocking Module Slide Gridding Module Slide Segmentation Module

10. 10 Slide Information Module Goal Retrieve 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

11. 11 Fully automatic spot addressing 1234 5678 9101112 13141516 17181920 21222324 25262728 29303132 33343536 37383940 41424344 45464748 Horizontal block boundaries Vertical block boundaries Horizontal gridlines Vertical gridlines

12. 12 Slide Blocking Module Signal/Noise detector Distinguish signal (foreground pixels) from noise (background pixels) by adopting a global-local thresholding technique 1 Tilt detector Identify and correct a tiled slide by first determining the tilted angle, and then rotate the entire slide with affine transformation Block boundary detector Discovers the repeated block patterns in a slide by detecting gaps between blocks and generates the horizontal and vertical block boundaries 1 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.

13. 13 Tilt detector Goal Identify and correct a tilted slide Implementation  Primitive analysis engine  Detect tilted angle by Principal Component Analysis (PCA)  Correct tilted slide with affine transformation

14. 14 Tilted angle detection on artificial slides

15. 15 Tilted angle detection on real slides

16. 16 Slide Gridding Module Goal Generates a grid within each block for separating spots, i.e. a cell in the grid contains only one spot 1 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.

17. 17 Spot addressing results Detecting blocks 1  Recall value: 100%  Precision value: 100% Gridding 1  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.

18. 18 Slide Segmentation Module Goal Refine the class label within the grid region Implementation  Primitive analysis engine  Determine local threshold – Otsu’s 2  Minimize Intra-class variance Between-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.

19. 19 Spot segmentation results Segmentation results for some sample spots 1 In each row, from left to right: 1.Original spot 2.Pre-labeled spot with the segment boundary 3.Spot segmentation results 4.GenePix

20. 20 Spot segmentation results Segmentation results 1 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.

21. 21 Conclusions Our proposed imArray system is fully automatic  Handle uneven background and severe noise  Detect tilted slide and correct its orientation  Detect block boundaries and generate grids  Spot segmentation method is simple and effective  Highly parallelizable method  Update annotation automatically

22. 22 Thank you !!