An FPGA-based genetic microarray processing device

Gridding and spot segmentation are two critical steps in microarray gene expression data analysis. However, the problems of noise contamination and donut-shaped spots often make signal extraction process a laborintensive task. In this paper, we propose a three-step method for automatic gridding and spot segmentation. The method starts with a background removal and noise eliminating step, and then proceeds in two steps. The first step applies a fully unsupervised method to extract blocks and grids from the cleaned data. The second step applies a simple, progressive spot segmentation method to deal with inner holes and noise in spots. We tested its performance on real microarray images against a widely used software GenePix. Our results show that the proposed method deals effectively with poor-conditioned microarray images in both gridding and spot segmentation.

The accuracy of gene expression measurements generated using cDNA microarrays is dependent on the quality of the image generated following hybridization of fluorescently labelled cDNA. It is not known how this image is influenced by sample preparation factors which such as RNA quality, cDNA synthesis and labelling efficiency. In this study we used a simple metric based on the ratio of the total feature (F) and background (B) fluorescence, which correlates with the visual assessment of 60 microarray images, to determine the influence of sample preparation on image quality. Results indicate that RNA purity (A260/A280) and integrity (18S:28S ratio) do not strongly influence microarray image quality. cDNA having an nucleotide to dye ratio greater than 100 produced poor microarray images, however, cDNA labelled more efficiently was not a guarantee of a better image. The data also indicate that the array image quality is not improved by loading more cDNA into the hybridization mixture however poor image quality did result from a disproportionate amounts of Cy5 and Cy3 labelled cDNA. This study provides insight into the source of variation in microarray image analysis introduced during sample preparation and will assist in the standardisation of cDNA glass slide microarray protocols.

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

Microarray image compression: SLOCO and the effect of information loss

cDNA microarray technology enables whole genome study of gene expressions by measuring the differential expression of genes in microarray images. An important first step in analyzing microarray image is the accurate delineation of the cDNA spots in the image. We report here a fully automated spot segmentation algorithm for cDNA microarray images. The algorithm makes use of morphological operations, adaptive multi-level thresholding, and statistical intensity modeling to perform automatic grid addressing and spot segmentation. Our algorithm is robust for even poor quality cDNA microarray images.

Background/Objectives: This study presents an external and internal prior guided patch based filter for minimizing Gaussian noise in complementary deoxyribonucleic acid (cDNA) microarray images. Methods/Statistical analysis: The proposed denoising filter is capable of taking into consideration both external and internal prior. It employs combined prior guided patch based denoising considering various distance based patch-matching methods. Findings: Experimental results demonstrate that the combined prior guided patch based filter outperforms the existing well-known filters in minimizing noise of cDNA microarray images. The outcome of the proposed scheme found to offer better peak signalto-noise ratio and structural similarity index in contrast to existing filtering techniques. Effectiveness of the proposed denoising method is also assessed by estimating the spot intensities of cDNA microarray image that reflects the effect of noise reduction in the image. Application/Improvements: Minimization of noise is a crucial step of cDNA microarray image processing and it aids in microarray analysis by extracting valid and good quality gene expression measurements. Keywords: Denoising, External Prior, Internal Prior, K- nearest Neighbor, Microarray Image, Singular Value Decomposition

BackgroundThe quality of cDNA microarray data is crucial for expanding its application to other research areas, such as the study of gene regulatory networks. Despite the fact that a number of algorithms have been suggested to increase the accuracy of microarray gene expression data, it is necessary to obtain reliable microarray images by improving wet-lab experiments. As the first step of a cDNA microarray experiment, spotting cDNA probes is critical to determining the quality of spot images.ResultsWe developed a governing equation of cDNA deposition during evaporation of a drop in the microarray spotting process. The governing equation included four parameters: the surface site density on the support, the extrapolated equilibrium constant for the binding of cDNA molecules with surface sites on glass slides, the macromolecular interaction factor, and the volume constant of a drop of cDNA solution. We simulated cDNA deposition from the single model equation by varying the value of the parameters. The morphology of the resulting cDNA deposit can be classified into three types: a doughnut shape, a peak shape, and a volcano shape. The spot morphology can be changed into a flat shape by varying the experimental conditions while considering the parameters of the governing equation of cDNA deposition. The four parameters were estimated by fitting the governing equation to the real microarray images. With the results of the simulation and the parameter estimation, the phenomenon of the formation of cDNA deposits in each type was investigated.ConclusionThis study explains how various spot shapes can exist and suggests which parameters are to be adjusted for obtaining a good spot. This system is able to explore the cDNA microarray spotting process in a predictable, manageable and descriptive manner. We hope it can provide a way to predict the incidents that can occur during a real cDNA microarray experiment, and produce useful data for several research applications involving cDNA microarrays.

An automated method for gridding and clustering-based segmentation of cDNA microarray images

cDNA microarrays is one of the most fundamental and powerful tools in biotechnology. Despite its relatively late discovery in 1995, it has since been utilized in many biomedical applications such as cancer research, infectious disease diagnosis and treatment, toxicology research, pharmacology research, and agricultural development. The reason for its broad use is that it enables scientists to analyze simultaneously the expression levels of thousands of genes over different samples (Leung et al., 2003). More precisely, the process of a microarray experiment (Campbell et al., 2007) starts with the selection of a set of DNA probes that are of particular interest. A robot places the selected DNA probes on a glass slide, creating an invisible array of DNA dots. Two distinct populations of mRNAs (messenger RNAs) are then isolated from a control sample (i.e a cell developed under normal conditions) and a test sample (i.e. a cell developed under a specific treatment). The mRNA populations are reversely transcribed into cDNA (complementary DNA) populations which in turn are colored with separate fluorescent dyes of different wavelengths (i.e. Cy3 and Cy5). The dyed cDNA populations are mixed with purified water and the solution is placed on the glass slide in order for the cDNA populations to be hybridized with the slide’s DNA dots. Finally, the hybridized glass slide is fluorescently scanned twice; one scan for each dye’s wavelength. Hence, two digital images are produced, one for each population of mRNA. Each digital image contains a number of spots (corresponding to the DNA-cDNA dots) of various fluorescence intensities. Given that the intensity of each spot is proportional to the hybridization level of the cDNAs and the DNA dots, the gene expression information is obtained by analyzing the digital images. As stated by Yang et al (Yang et. al, 2002), the process of analyzing a microarray image can be divided into three main phases, namely: “Gridding”, “Spot-Segmentation” and “SpotIntensity extraction”. During the 1st phase, the microarray image is segmented into numerous compartments, each containing one individual spot and background. During the 2nd phase each compartment is individually segmented into a spot area and a background area, while during the 3rd phase the brightness of each spot is calculated. The expressionlevels of the genes in these spots are a direct result of their individual brightness. Amongst the stages of the microarray-image analysis, spot-segmentation remains the most challenging one. Ideally, the existing spots inside the microarray image are aligned in 2D array layouts. These ‘ideal spots’ also have a circular 2D shape with fixed diameters, while

Complementary DNA microarray technology is a powerful tool in many areas. Usually a two channel microarray red-green (RG) image is obtained. Due to the nature of cDNA microarray technology, a number of impairments affect the cDNA microarray image before the analysis such as identification of differentially expressed genes. Microarray image processing plays a crucial role in the extraction and quantitative analysis of the relative abundance of the DNA product. In this paper, a method combined K-means clustering algorithm and mathematical morphology is presented. Mathematical morphology is a useful tool for extracting image components. K-means clustering algorithm has a good performance in the segmentation of microarray image processing. The result of the experiment shows that the method presented in this paper is accurate, automatic and robust.

Using fuzzy logic and particle swarm optimization to design a decision-based filter for cDNA microarray image restoration

A wavelet-based Markov random field segmentation model in segmenting microarray experiments

Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission requires compression that utilizes the microarray image's structure and unique analysis goals. We have developed a progressive compression scheme for microarray images which can be either lossy or lossless. Our scheme has a coded data structure that allows fast decoding and reprocessing of image subsets, and includes summary statistics and image segmentation information. Since visual fidelity is not the end goal for microarray images, we introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. We find that a lossy compression ratio of 8:1 for cDNA microarrays minimally affects downstream processing. The average lossless compression ratio is 1.83:1 for cDNA images and 2.43:1 for inkjet images, comparable to state-of-the-art lossless schemas, yet with added flexibility and information.

Current gene-expression microarrays carry enormous amounts of information. Compression is necessary for efficient distribution and storage. This paper examines JPEG2000 compression of cDNA microarray images and addresses the accuracy of classification and feature selection based on decompressed images. Among other options, we choose JPEG2000 because it is the latest international standard for image compression and offers lossy-to-lossless compression while achieving high lossless compression ratios on microarray images. The performance of JPEG2000 has been tested on three real data sets at different compression ratios, ranging from lossless to 45:1. The effects of JPEG2000 compression/decompression on differential expression detection and phenotype classification have been examined. There is less than a 4% change in differential detection at compression rates as high as 20:1, with detection accuracy suffering less than 2% for moderate to high intensity genes, and there is no significant effect on classification at rates as high as 35:1. The supplementary material is available at http://gsp.tamu.edu/web2/Compression.

A microarray-image model is used that takes into account many factors, including spot morphology, signal strength, background fluorescent noise, and shape and surface degradation. The model yields synthetic images whose appearance and quality reflect that of real microarray images. The model is used to link noise factors to the fidelity of signal extraction with respect to a standard image-extraction algorithm. Of particular interest is the identification of the noise factors and their interactions that significantly degrade the ability to accurately detect the true gene-expression signal. This study uses statistical criteria in conjunction with the simulation of various noise conditions to better understand the noise influence on signal extraction for cDNA microarray images. It proposes a paradigm that is implemented in software. It specifically considers certain kinds of noise in the noise model and sets these at certain levels; however, one can choose other types of noise or use different noise levels. In sum, it develops a statistical package that can work in conjunction with the existing image simulation toolbox.