cDNA Microarray Basics
cDNA Microarray Imaging
cDNA Image Noise
cDNA Characteristics
cDNA Image Denoising
cDNA Data Normalization
cDNA Spot Localization
cDNA Spot Segmentation


cDNA Microarray Basics

cDNA microarray technology is considered one of the most important and powerful tools used to extract and interpret genomic information. The cDNA microarray experiment requires to isolate Ribonucleic Acid (RNA) from both control (known) and experimental (patient) samples. The reverse transcription process is used to convert the extracted RNAs into cDNAs, which are further labeled with fluorescent probes, usually Cy3 for the control and Cy5 for the experimental channel. After subsequent hybridization and washing procedures, cDNA microarrays are scanned at the ~540 nm (green) for the control and ~630 nm (red) for the experimental channel respectively. The scanning procedure produces two 16-bit monochromatic images, which are further registered into a two-channel, Red-Green image. Analysis of cDNA microarray data helps in monitoring the expression levels of thousands of genes simultaneously and provides information relevant to cell activity. Therefore, cDNA microarrays have found applications in toxicological research, gene and drug discovery, and disease diagnosis (e.g., cancer, diabetes, and genetic diseases).

The spots of cDNA image constitute the foreground information which is essential for microarray image analysis and gene expression tasks. Red spots determine the presence of RNA from the experimental population of cells, green spots indicate the presence of RNA from the control population of cells, and yellow spots determine that RNAs originate from both experimental and control populations. Arrays of cDNA spots, usually up to 80 000 probes per 2x4 cm^2 area, are commonly referred to as microarrays. The vast amount of data and calculations needed to obtain the relative expression levels of the genes from the fluorescence intensity at each spot necessitates the development of automated data processing solutions.


bulletR. Lukac and K.N. Plataniotis, "cDNA Microarray Image Segmentation Using Root Signals,"  International Journal of Imaging Systems and Technology, vol. 16, no. 2, pp. 51-64, April 2006.
bulletR. Lukac, K.N. Plataniotis, B. Smolka, and A.N. Venetsanopoulos, "cDNA Microarray Image Processing Using Fuzzy Vector Filtering Framework," Fuzzy Sets and Systems, Special Issue on Fuzzy Sets and Systems in Bioinformatics, vol. 152, no. 1, pp.17-35, May 2005.
bulletR. Lukac, B. Smolka, K. Martin, K.N. Plataniotis, and A.N. Venetsanopoulos, "Vector Filtering for Color Imaging," IEEE Signal Processing Magazine, Special Issue on Color Image Processing, vol. 22, no. 1, pp. 74-86, January 2005.
bulletR. Lukac, K.N. Plataniotis, B. Smolka, and A.N. Venetsanopoulos, "A Multichannel Order-Statistic Technique for cDNA Microarray Image Processing," IEEE Transactions on NanoBioscience, vol. 3, no. 4, pp. 272-285, December 2004.

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Last update: 10/15/06

2006 Rastislav Lukac