40,647 research outputs found

    Improvements on segment based contours method for DNA microarray image segmentation

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    DNA microarray is an efficient biotechnology tool for scientists to measure the expression levels of large numbers of genes, simultaneously. To obtain the gene expression, microarray image analysis needs to be conducted. Microarray image segmentation is a fundamental step in the microarray analysis process. Segmentation gives the intensities of each probe spot in the array image, and those intensities are used to calculate the gene expression in subsequent analysis procedures. Therefore, more accurate and efficient microarray image segmentation methods are being pursued all the time. In this dissertation, we are making efforts to obtain more accurate image segmentation results. We improve the Segment Based Contours (SBC) method by implementing a higher order of finite difference schemes in the partial differential equation used in our mathematical model. Therefore, we achieved two improved methods: the 4th order method and the 8th order method. The 4th order method could be applied to segment both the cDNA microarray images and the Affymetrix GeneChips, while the 8 th order method could be applied to segment only the cDNA microarray images, due to the limitation of the current image resolution. The mathematical derivation shows that both our 4th order method and 8th order method are better approximating the C-V model [Chan & Vese, 2001] than the SBC method, which means they will offer more accurate segmentation results than the SBC method. Besides mathematical proof, we do the practical experiments to double check the conclusion drawn from the mathematical derivation. Both the 4th order method and the 8th order method are used to segment microarray images, and the output segmentation results—the intensities of each probe cell in the microarray image—are being compared to the results from the SBC method and two other mainstream microarray image segmentation methods, the Globaly Optimal Geodesic Active Contours (GOGAC) method and the GeneChip Operating System (GCOS) software, for more valid evaluation. To give the ground true values of intensities as the standard for different segmentation methods comparison, a microarray image simulator is introduced to generate the simulated images used in our experiments. The simulated microarray images have all the characteristics that real microarray images have, and the true intensity values of each probe spot in the image are provided by this simulator. Intensity values segmented by those segmentation methods are compared to the true intensity values. Therefore, we could evaluate that one segmentation method is more accurate than the other methods if its intensity values are closer to the true values. We conduct several analysis procedures in the segmentation results comparison part to convince our analysis results. Intensity analysis, paired t-test and Unweighted Pair Group Method with Arithmetic Mean (UPGMA) hierarchy cluster experiments are applied to analyze intensity values of those methods. The segmentation output analysis results show that our 4th order method and the 8th order method could offer more accurate segmentation than the SBC method, the GCOS method and the GOGAC method on some kinds of the microarray images. There are accuracy improvements achieved with the 8 th order method over the 4th order method on the cDNA microarray image. On the Bovine type Affymetrix GeneChip image, there is no significant difference between the 4th order method and the 8th order method

    Improved processing of microarray data using image reconstruction techniques

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    Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy

    A multi-view approach to cDNA micro-array analysis

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    The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

    A novel neural network approach to cDNA microarray image segmentation

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    This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

    Transcription analysis of apple fruit development using cDNA microarrays

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    The knowledge of the molecular mechanisms underlying fruit quality traits is fundamental to devise efficient marker-assisted selection strategies and to improve apple breeding. In this study, cDNA microarray technology was used to identify genes whose expression changes during fruit development and maturation thus potentially involved in fruit quality traits. The expression profile of 1,536 transcripts was analysed by microarray hybridisation. A total of 177 genes resulted to be differentially expressed in at least one of the developmental stages considered. Gene ontology annotation was employed to univocally describe gene function, while cluster analysis allowed grouping genes according to their expression profile. An overview of the transcriptional changes and of the metabolic pathways involved in fruit development was obtained. As expected, August and September are the two months where the largest number of differentially expressed genes was observed. In particular, 85 genes resulted to be up-regulated in September. Even though most of the differentially expressed genes are involved in primary metabolism, several other interesting functions were detected and will be presented

    Conversion of cDNA differential display results (DDRT-PCR) into quantitative transcription profiles

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    BACKGROUND: Gene expression studies on non-model organisms require open-end strategies for transcription profiling. Gel-based analysis of cDNA fragments allows to detect alterations in gene expression for genes which have neither been sequenced yet nor are available in cDNA libraries. Commonly used protocols for gel-based transcript profiling are cDNA differential display (DDRT-PCR) and cDNA-AFLP. Both methods have been used merely as qualitative gene discovery tools so far. RESULTS: We developed procedures for the conversion of cDNA Differential Display data into quantitative transcription profiles. Amplified cDNA fragments are separated on a DNA sequencer and detector signals are converted into virtual gel images suitable for semi-automatic analysis. Data processing consists of four steps: (i) cDNA bands in lanes corresponding to samples treated with the same primer combination are matched in order to identify fragments originating from the same transcript, (ii) intensity of bands is determined by densitometry, (iii) densitometric values are normalized, and (iv) intensity ratio is calculated for each pair of corresponding bands. Transcription profiles are represented by sets of intensity ratios (control vs. treatment) for cDNA fragments defined by primer combination and DNA mobility. We demonstrated the procedure by analyzing DDRT-PCR data on the effect of secondary metabolites of oilseed rape Brassica napus on the transcriptome of the pathogenic fungus Leptosphaeria maculans. CONCLUSION: We developed a data processing procedure for the quantitative analysis of amplified cDNA fragments separated by electrophoresis. The system utilizes common software and provides an open-end alternative to DNA microarray analysis of the transcriptome. It is expected to work equally well with DDRT-PCR and cDNA-AFLP data and be useful particularly in reseach on organisms for which microarray analysis is not available or economical
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