We present an algorithm for automatic spot localization for microarrayimages with rectangular spot and block packing. As an input, the algorithm requires only the common array design parameters: number of block rows ...
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We present an algorithm for automatic spot localization for microarrayimages with rectangular spot and block packing. As an input, the algorithm requires only the common array design parameters: number of block rows and columns and number of spot rows and columns within each block. It proved to be robust with respect to different types of contamination and can tolerate a high percentage of the missing spots. The validity of the developed algorithm has been tested and confirmed using a large set of images of various designs from different microarray platforms. Comparison with academic and commercial packages has shown that for uncontaminated images our algorithm performs similarly, whereas for certain problematic images it outperforms the other packages.
microarray is considered an important instrument and powerful new technology for large-scale gene sequence and gene expression analysis. One of the major challenges of this technique is the image processing phase. The...
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ISBN:
(纸本)9781424420292
microarray is considered an important instrument and powerful new technology for large-scale gene sequence and gene expression analysis. One of the major challenges of this technique is the image processing phase. The accuracy of this phase has an important impact on the accuracy and effectiveness of the subsequent gene expression and identification analysis. The processing can be organized mainly into four steps: gridding, spot isolation, segmentation, and quantification. Although several commercial software packages are now available, microarray image analysis still requires some intervention by the user, and thus a certain level of image processing expertise. This paper describes and compares four techniques that perform automatic gridding and spot isolation. The proposed techniques are based on template matching technique, standard deviation, sum, and derivative of these profiles. Experimental results show that the accuracy of the derivative of the sum profile is highly accurate compared to other techniques for good and poor quality microarrayimages.
A spot-adaptive compound clustering-enhancement-segmentation (CES) scheme was developed for the quantification of gene expression levels in microarrayimages. The CES-scheme employed 1/griding, for locating spot-regio...
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ISBN:
(纸本)9783540744825
A spot-adaptive compound clustering-enhancement-segmentation (CES) scheme was developed for the quantification of gene expression levels in microarrayimages. The CES-scheme employed 1/griding, for locating spot-regions, 2/Fuzzy C-means clustering, for segmenting spots from background, 3/background noise estimation and spot's center localization, 4/emphasizing of spot's outline by the CLAHE image enhancement technique, 5/segmentation by the SRG algorithm, using information from step 3, and 6/microarray spot intensity extraction. Extracted intensities by the CES-Scheme were compared against those obtained by the MAGIC TOOL's SRG. Kullback-Liebler metric's values for the CES-Scheme were on average double than MAGIC TOOL's, with differences ranging from 1.45bits to 2.77bits in 7 cDNA images. Coefficient-of-Variation results showed significantly higher reproducibility (p<0.001) for the CES-Scheme in quantifying gene expression levels. Processing times for 1024x1024 16-bit microarrayimages containing 6400 spots were 300 and 487 seconds for the CES-Scheme and MAGIC TOOL respectively.
Objectives: We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of ...
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Objectives: We characterize typical problems encountered in microarray image analysis and present algorithmic approaches dealing with background estimation, spot identification and intensity extraction. Validation of the quality of resulting measurements is discussed. Methods: We describe sources for errors in microarrayimages and present algorithms that have been specifically developed to deal with such experimental imperfections. Results: For the imageanalysis of hybridization experiments, discriminating spot regions from a background is the most critical step. Spot shape detection algorithms, intensity histogram methods and hybrid approaches have been proposed. The correctness of final intensity estimates is difficult to verify. Nevertheless, the application of sophisticated algorithms provides a significant reduction of the possible information loss. Conclusions. The initial analysis step for array hybridization experiments is the estimation of expression intensities. The quality of this process is crucial for the validity of interpretations from subsequent analysis steps.
Addressing spots in microarrayimages and deriving expression values for corresponding genes are fundamental tasks in microarray image analysis. Reliable expression values can be obtained only if the spot locations ar...
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ISBN:
(纸本)0819456454
Addressing spots in microarrayimages and deriving expression values for corresponding genes are fundamental tasks in microarray image analysis. Reliable expression values can be obtained only if the spot locations are accurately known. Here, a novel approach for spot addressing in microarrayimages based oil supervised learning is proposed. The aim is to locate each spot through classifying the image based oil local features into spot centers and background using support vector machine classifier. The resulting spot location information is complemented through image processing methods in the post-processing phase. Our method, through searching locations for individual spots, enables accurate segmentation and extraction of expression values. The benefit of searching individual spots becomes clear in case of misaligned spots or spot rows.
The reliability of the algorithms for ratio estimation in two-color microarray image analysis is very important, as these ratios build up the primary source of information for the subsequent analytical procedures (nor...
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Background: microarray-based comparative genomic hybridisation (array CGH) is a technique by which variation in relative copy numbers between two genomes can be analysed by competitive hybridisation to DNA microarrays...
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Background: microarray-based comparative genomic hybridisation (array CGH) is a technique by which variation in relative copy numbers between two genomes can be analysed by competitive hybridisation to DNA microarrays. This technology has most commonly been used to detect chromosomal amplifications and deletions in cancer. Dedicated tools are needed to analyse the results of such experiments, which include appropriate visualisation, and to take into consideration the physical relation in the genome between the probes on the array. Results: M-CGH is a MATLAB toolbox with a graphical user interface designed specifically for the analysis of array CGH experiments, with multiple approaches to ratio normalization. Specifically, the distributions of three classes of DNA copy numbers (gains, normal and losses) can be estimated using a maximum likelihood method. Amplicon boundaries are computed by either the fuzzy K-nearest neighbour method or a wavelet approach. The program also allows linking each genomic clone with the corresponding genomic information in the Ensembl database http://***. Conclusions: M-CGH, which encompasses the basic tools needed for analysing array CGH experiments, is freely available for academics http://***/similar tojunbaiw/mcgh, and does not require any other MATLAB toolbox.
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