Copasetic analysis: a framework for the blind analysis of microarray imagery

The official published version can be found at the link below.From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePix® when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis

Spot Detection and Image Segmentation in DNA Microarray Data

Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance

Microarray spot partitioning by autonoumsly organising maps thorugh contour model

In cDNA microarray image analysis, classification of pixels as forefront area and the area covered by background is very challenging. In microarray experimentation, identifying forefront area of desired spots is nothing but computation of forefront pixels concentration, area covered by spot and shape of the spots. In this piece of writing, an innovative way for spot partitioning of microarray images using autonomously organizing maps (AOM) method through C-V model has been proposed. Concept of neural networks has been incorpated to train and to test microarray spots.In a trained AOM the comprehensive information arising from the prototypes of created neurons are clearly integrated to decide whether to get smaller or get bigger of contour. During the process of optimization, this is done in an iterative manner. Next using C-V model, inside curve area of trained spot is compared with test spot finally curve fitting is done.The presented model can handle spots with variations in terms of shape and quality of the spots and meanwhile it is robust to the noise. From the review of experimental work, presented approach is accurate over the approaches like C-means by fuzzy, Morphology sectionalization

Standard and specific compression techniques for DNA microarray images

We review the state of the art in DNA microarray image compression and provide original comparisons between standard and microarray-specific compression techniques that validate and expand previous work. First, we describe the most relevant approaches published in the literature and classify them according to the stage of the typical image compression process where each approach makes its contribution, and then we summarize the compression results reported for these microarray-specific image compression schemes. In a set of experiments conducted for this paper, we obtain new results for several popular image coding techniques that include the most recent coding standards. Prediction-based schemes CALIC and JPEG-LS are the best-performing standard compressors, but are improved upon by the best microarray-specific technique, Battiato's CNN-based scheme

Copasetic analysis: Automated analysis of biological gene expression images

Copyright [2004] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In the past decade computational biology has come to the forefront of the public's perception with advancements in domain knowledge and a variety of analysis techniques. With the recent completion of projects like the human genome sequence, and the development of microarray chips it has become possible to simultaneously analyse expression levels for thousands of genes. Typically, a slide surface of less than 24 cm2, receptors for 30,000 genes can be printed, but currently the analysis process is a time consuming semi-autonomous step requiring human guidance. The paper proposes a framework, which facilitates automated processing of these images. This is supported by real world examples, which demonstrate the technique's capabilities along with results, which show a marked improvement over existing implementations

An Overview of DNA Microarray Grid Alignment and Foreground Separation Approaches

This paper overviews DNA microarray grid alignment and foreground separation approaches. Microarray grid alignment and foreground separation are the basic processing steps of DNA microarray images that affect the quality of gene expression information, and hence impact our confidence in any data-derived biological conclusions. Thus, understanding microarray data processing steps becomes critical for performing optimal microarray data analysis. In the past, the grid alignment and foreground separation steps have not been covered extensively in the survey literature. We present several classifications of existing algorithms, and describe the fundamental principles of these algorithms. Challenges related to automation and reliability of processed image data are outlined at the end of this overview paper.</p

Semantic distillation: a method for clustering objects by their contextual specificity

Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism. The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed \textit{semantic distillation} -- strongly inspired from the theory of quantum measurement --, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence, Springer-Verla

Utilizing microarray spot characteristics to improve cross-species hybridization results

AbstractCross-species hybridization (CSH), i.e., the hybridization of a (target) species RNA to a DNA microarray that represents another (reference) species, is often used to study species diversity. However, filtration of CSH data has to be applied to extract valid information. We present a novel approach to filtering the CSH data, which utilizes spot characteristics (SCs) of image-quantification data from scanned spotted cDNA microarrays. Five SCs that were affected by sequence similarity between probe and target sequences were identified (designated as BS-SCs). Filtration by all five BS-SC thresholds demonstrated improved clustering for two of the three examined experiments, suggesting that BS-SCs may serve for filtration of data obtained by CSH, to improve the validity of the results. This CSH data-filtration approach could become a promising tool for studying a variety of species, especially when no genomic information is available for the target species