Simulation of cDNA Microarrays via a Parameterized Random Signal Model

Yoganand Balagurunathan,¹ Edward R. Dougherty,¹ Yidong Chen,² Michael L. Bittner,² and J. M. Trent,²

¹Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892

²Department of Electrical Engineering, Texas A&M University, College Station, TX 77843-3128

Corresponding authors: Edward R. Dougherty (e-dougherty@tamu.edu) and Yidong Chen (yidong@nhgri.nih.gov).

1. Abstract.

cDNA microarrays provide simultaneous expression measurements for thousands of genes that are the result of processing images to recover the average signal intensity from a spot composed of pixels covering the area upon which the cDNA detector has been put down. The accuracy of the signal measurement depends on using an appropriate algorithm to process the images. This includes determining spot locations and processing the data in such a way as to take into account spot geometry, background noise, and various kinds of noise that degrade the signal. This paper presents a stochastic model for microarray images. There are over twenty model parameters, each governed by a probability distribution, that control the signal intensity, spot geometry, spot drift, background effects, and the many kinds of noise that affect microarray images owing to the manner in which they are formed. The model can be used to analyze the performance of image algorithms designed to measure the true signal intensity because the ground truth (signal intensity) for each spot is known. The levels of foreground noise, background noise, and spot distortion can be set, and algorithms can be evaluated under varying conditions.

2. Figures.

• Figure 1. Simulation schematic diagram.
• Figure 2. Illustration of background noise.
• Figure 3. Illustration of inner holes.
• Figure 4. Microarray spot model.
• Figure 5. Illustration of spot size variation.
• Figure 6. Illustration of inter-spot spacing.
• Figure 7. Illustration of drifting variation.
• Figure 8. Illustration of different chord rate.
• Figure 9. Illustration of edge noise.
• Figure 10. Illustration of detection response characteristic functions.
• Figure 11. Variation scatter plots from simulated data.
• Figure 12. Illustration of spike noise.
• Figure 13. Illustration of scratch noise.
• Figure 14. Illustration of snake noise.
• Figure 15-16. Convolution kernel and effects of smoothing.
• Figure 17a, 17b. Good and noisy microarray image examples.
• Figure 18. Comparison of simulated signal vs. extracted signal.
• Figure 19. Example of background interference and software solution.