A multi-view approach to cDNA micro-array analysis

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

Automatic gridding of microarray images based on spatial constrained K-means and Voronoi diagrams

Images from complementary DNA (cDNA) microarrays need to be processed automatically due to the huge amount of information that they provide. In addition, automatic processing is also required to implement batch processes able to manage large image databases. Most of existing softwares for microarray image processing are semiautomatic, and they usually need user intervention to select several parameters such as positional marks on the grids, or to correct the results of different stages of the automatic processing. On the other hand, many of the available automatic algorithms fail when dealing with rotated images or misaligned grids. In this work, a novel automatic algorithm for cDNA image gridding based on spatial constrained K-means and Voronoi diagrams is presented. The proposed algorithm consists of several steps, viz., image denoising by means of median filtering, spot segmentation using Canny edge detector and morphological reconstruction, and gridding based on spatial constrained K-means and Voronoi diagrams computation. The performance of the algorithm was evaluated on microarray images from public databases yielding promising results. The algorithm was compared with other existing methods and it shows to be more robust to rotations and misalignments of the grids.Red de Universidades con Carreras en Informática (RedUNCI

Automatic gridding of microarray images based on spatial constrained K-means and Voronoi diagrams

Images from complementary DNA (cDNA) microarrays need to be processed automatically due to the huge amount of information that they provide. In addition, automatic processing is also required to implement batch processes able to manage large image databases. Most of existing softwares for microarray image processing are semiautomatic, and they usually need user intervention to select several parameters such as positional marks on the grids, or to correct the results of different stages of the automatic processing. On the other hand, many of the available automatic algorithms fail when dealing with rotated images or misaligned grids. In this work, a novel automatic algorithm for cDNA image gridding based on spatial constrained K-means and Voronoi diagrams is presented. The proposed algorithm consists of several steps, viz., image denoising by means of median filtering, spot segmentation using Canny edge detector and morphological reconstruction, and gridding based on spatial constrained K-means and Voronoi diagrams computation. The performance of the algorithm was evaluated on microarray images from public databases yielding promising results. The algorithm was compared with other existing methods and it shows to be more robust to rotations and misalignments of the grids.Red de Universidades con Carreras en Informática (RedUNCI

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays

Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri

Wavelet-based noise reduction of cDNA microarray images

The advent of microarray imaging technology has lead to enormous progress in the life sciences by allowing scientists to analyze the expression of thousands of genes at a time. For complementary DNA (cDNA) microarray experiments, the raw data are a pair of red and green channel images corresponding to the treatment and control samples. These images are contaminated by a high level of noise due to the numerous noise sources affecting the image formation. A major challenge of microarray image analysis is the extraction of accurate gene expression measurements from the noisy microarray images. A crucial step in this process is denoising, which consists of reducing the noise in the observed microarray images while preserving the signal information as much as possible. This thesis deals with the problem of developing novel methods for reducing noise in cDNA microarray images for accurate estimation of the gene expression levels. Denoising methods based on the wavelet transform have shown significant success when applied to natural images. However, these methods are not very efficient for reducing noise in cDNA microarray images. An important reason for this is that existing methods are only capable of processing the red and green channel images separately. In doing so. they ignore the signal correlation as well as the noise correlation that exists between the wavelet coefficients of the two channels. The primary objective of this research is to design efficient wavelet-based noise reduction algorithms for cDNA microarray images that take into account these inter-channel dependencies by 'jointly' estimating the noise-free coefficients in both the channels. Denoising algorithms are developed using two types of wavelet transforms, namely, the frequently-used discrete wavelet transform (DWT) and the complex wavelet transform (CWT). The main advantage of using the DWT for denoising is that this transform is computationally very efficient. In order to obtain a better denoising performance for microarray images, however, the CWT is preferred to DWT because the former has good directional selectivity properties that are necessary for better representation of the circular edges of spots. The linear minimum mean squared error and maximum a posteriori estimation techniques are used to develop bivariate estimators for the noise-free coefficients of the two images. These estimators are derived by utilizing appropriate joint probability density functions for the image coefficients as well as the noise coefficients of the two channels. Extensive experimentations are carried out on a large set of cDNA microarray images to evaluate the performance of the proposed denoising methods as compared to the existing ones. Comparisons are made using standard metrics such as the peak signal-to-noise ratio (PSNR) for measuring the amount of noise removed from the pixels of the images, and the mean absolute error for measuring the accuracy of the estimated log-intensity ratios obtained from the denoised version of the images. Results indicate that the proposed denoising methods that are developed specifically for the microarray images do, indeed, lead to more accurate estimation of gene expression levels. Thus, it is expected that the proposed methods will play a significant role in improving the reliability of the results obtained from practical microarray experiments

ATMAD : robust image analysis for Automatic Tissue MicroArray De-arraying

International audienceBackground. Over the last two decades, an innovative technology called Tissue Microarray (TMA),which combines multi-tissue and DNA microarray concepts, has been widely used in the field ofhistology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembledonto a single support – typically a glass slide – according to a design grid (array) layout, in order toallow multiplex analysis by treating numerous samples under identical and standardized conditions.However, during the TMA manufacturing process, the sample positions can be highly distorted fromthe design grid due to the imprecision when assembling tissue samples and the deformation of theembedding waxes. Consequently, these distortions may lead to severe errors of (histological) assayresults when the sample identities are mismatched between the design and its manufactured output.The development of a robust method for de-arraying TMA, which localizes and matches TMAsamples with their design grid, is therefore crucial to overcome the bottleneck of this prominenttechnology.Results. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD)approach dedicated to images acquired with bright field and fluorescence microscopes (or scanners).First, tissue samples are localized in the large image by applying a locally adaptive thresholdingon the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametricshape model is considered for segmenting ellipse-shaped objects at each detected position.Segmented objects that do not meet the size and the roundness criteria are discarded from thelist of tissue samples before being matched with the design grid. Sample matching is performed byestimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimateddeformation, the true tissue samples that were preliminary rejected in the early image processingstep are recognized by running a second segmentation step.Conclusions. We developed a novel de-arraying approach for TMA analysis. By combining waveletbaseddetection, active contour segmentation, and thin-plate spline interpolation, our approach isable to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background andnon-linear deformation of TMA grid. In addition, the deformation estimation produces quantitativeinformation to asset the manufacturing quality of TMAs

Επεξεργασία εικόνων μικροσυστοιχιών cDNA με εύρωστες τεχνικές αυτόματης ταξινόμησης

Στην παρούσα διπλωματική εργασία χρησιμοποιήθηκαν εικόνες που προέκυψαν από πειράματα μικροσυστοιχιών cDNA, ως πρότυπα για τη δημιουργία προσομοιωμένων εικόνων σε πέντε διαφορετικά επίπεδα θορύβου, για την αξιολόγηση των αλγορίθμων κατάτμησης που υλοποιήθηκαν. Επιπλέον, πραγματοποιήθηκε μία συστηματική εκτίμηση φίλτρων βασισμένα σε κυμάτια για την καταστολή του θορύβου, προκειμένου να επιτευχθεί βελτίωση των εικόνων μικροσυστοιχιών cDNA. Για το σκοπό αυτό έγινε χρήση του SWT με διάφορα είδη μητρικών κυματίων. Οι εικόνες που προέκυψαν μετά την καταστολή του θορύβου αναλύθηκαν και εφαρμόστηκε σε αυτές ο αλγόριθμος κατάτμησης τυχαίων πεδίων Μαρκόφ MRF, για την εκτίμηση του αντίκτυπου της διαδικασίας καταστολής θορύβου στο στάδιο της κατάτμησης. Σκοπός αυτής της διαδικασίας ήταν η επιλογή των κατάλληλων παραμέτρων, κυρίως όσον αφορά το είδος του κυματίου, ούτως ώστε να χρησιμοποιηθούν στην μετέπειτα ανάλυση. Επιπλέον υλοποιήθηκε ένας ημιαυτόματος αλγόριθμος δημιουργίας και ευθυγράμμισης πλέγματος. Όσον αφορά την κατάτμηση των εικόνων, υλοποιήθηκαν τρεις αλγόριθμοι: ο k-means, ο MRF και μία τροποποίηση του MRF με χρήση των κυματίων (Wavelet-MRF). Για ποσοτικοποίηση των αποτελεσμάτων, όσον αφορά την καταστολή του θορύβου μετρήθηκε το mean square error (MSE) και το signal/MSE. Όσον αφορά την απόδοση των τεχνικών κατάτμησης, υπολογίστηκαν ο παράγοντας ταυτοποίησης κατάτμησης SMF και ο συντελεστής προσδιορισμού r2.In this thesis, images derived from cDNA microarray experiments were used as templates for creating simulated images at five different levels of noise, for the evaluation of the implemented segmentation algorithms. Moreover, a systematic evaluation of filters based on wavelets was performed, in order to achieve the improvement of microarray cDNA images by means of noise suppression. For this purpose, SWT was used combined with various types of mother wavelets. The images obtained after the suppression of noise were analyzed and the MRF algorithm was applied to them in order to assess the impact of the noise suppression process to the stage of segmentation. The purpose of this procedure was the selection of the appropriate parameters, basically concerning the type of wavelet, in order to use them in later analysis. Moreover, a semiautomatic algorithm for the gridding of the images was implemented. Regarding the segmentation of images, three algorithms were implemented: the k-means, the MRF and a modification of the MRF using wavelets (Wavelet-MRF). For the quantification of the results in terms of noise suppression the mean square error (MSE), and signal/MSE were measured. Regarding the performance of segmentation techniques, the segmentation matching factor SMF and the coefficient of determination r2 were calculated