Microarray Gridding Research Articles

Multilevel Segmentation Optimized by Physical Information for Gridding of Microarray Images

As one of the great advances in modern technology, the microarray is widely used in many fields, including biomedical research, clinical diagnosis, and so on. Evidently, in order to extract the intensity of fluorescence bio-probes accurately, we need to pay special attention to the gridding of microarray at first. To solve the poor effect of the traditional Otsu method for microarray gridding, an innovative algorithm of Otsu optimized by multilevel thresholds is proposed to improve the accuracy and effectiveness of the microarray image gridding and segmentation. The experimental results indicate that considering the physical information carried by microarrays, the improved algorithm of Otsu optimized by multilevel thresholds achieves high-quality gridding and establishes the bio-spot coordinates more precisely. Compared with the traditional Otsu method, its gridding error is reduced to zero, and the integrated relative error of bio-spot coordinates is decreased from 2.89% to 1.05%. This optimization of Otsu combined with physical information of spot-matrix will greatly improve the performance of segmentation so as to make the contribution to extracting the fluorescence intensity of microarray accurately.

A shape-independent algorithm for fully-automated gridding of cDNA microarray images

In this paper, a fully-automated microarray gridding algorithm is presented. This algorithm contains the block finding in an image by using variable length Blackman window, image contrast enhancement based on the Otsu thresholding approach, and identification of image objects including spots and artifacts through the 8-connected labeling method. Furthermore, a shape-independent algorithm based on the area of pixels in each object is proposed for noise elimination. The final gridding is performed using a new method based on the constructed spot matrix and a refinement procedure is exploited to minimize all probable grid-line errors. The performance of the proposed algorithm is evaluated by five public datasets including the Swiss Institute of Bioinformatics (SIB), Joe DeRisi's individual tiff files (DeRisi), University of California, San Francisco (UCSF), Gene Expression Omnibus (GEO), and Stanford Microarray Database (SMD). The obtained results reveal that the proposed algorithm reaches a higher level of accuracy and stability against some restrictions in microarray images such as noise, artifacts, and irregularities regarding the shapes of spots in comparison with other state-of-the-art methods.

New Method for Applying Multiple Samples to a TEM Specimen Grid

Transmission electron microscopy (TEM) enables visualization and structural determination of biological macromolecules. Examination of several samples by TEM is greatly limited by the time that it takes to insert each sample on the microscope, and the deteriorating vacuum on the TEM after this process is repeated several times. Nevertheless, when observing a specimen by TEM, in almost all cases the examined area of the grid represents a small fraction of the available area. Thus, in theory, a TEM grid has space for more than one sample. Here we have exploited the microarray technology to apply multiple samples to a single TEM specimen grid. Microarrayers can deposit small quantities of sample using a printer-like machine that transfers the liquid from the reservoir to the assay surface. We demonstrate that microarray technology can be used to accurately position picoliter quantities of sample within micrometric distances in the reduced space of the TEM grid with negligible cross-contamination. This technique permitted the deposition of samples into arrays of 2 to 36 discrete spots on a single TEM grid. The TEM grid containing a sample microarray can be negatively stained. After introducing the microarray grid in the microscope every spot can be inspected and images can be recorded digitally or on film. In conclusion, the microarray method can dramatically decrease the time necessary for TEM grid preparation and examination at least by one order of magnitude, while preserving the vacuum of the electron microscope. This new method is very suitable for screening and data collection in experiments that generate a multiplicity of samples.

FPGA based system for automatic cDNA microarray image processing

Automation is an open subject in DNA microarray image processing, aiming reliable gene expression estimation. The paper presents a novel shock filter based approach for automatic microarray grid alignment. The proposed method brings up significantly reduced computational complexity compared to state of the art approaches, while similar results in terms of accuracy are achieved. Based on this approach, we also propose an FPGA based system for microarray image analysis that eliminates the shortcomings of existing software platforms: user intervention, increased computational time and cost. Our system includes application-specific architectures which involve algorithm parallelization, aiming fast and automated cDNA microarray image processing. The proposed automated image processing chain is implemented both on a general purpose processor and using the developed hardware architectures as co-processors in a FPGA based system. The comparative results included in the last section show that an important gain in terms of computational time is obtained using hardware based implementations.

AUTOMATED MICROARRAY IMAGE GRIDDING USING IMAGE PROJECTION VECTORS COUPLED WITH POWER SPECTRUM MODEL

Microarray technology has been increasingly recognized as a powerful means for monitoring the expression levels of thousands of genes simultaneously. Microarray image processing is an essential aspect of microarray experiment, of which gridding is thought to be the most important step of spot recognition. Many times, microarray image gridding requires assisted intervention to achieve the acceptable accuracy. In this paper, an automatic microarray image gridding algorithm was presented by using image projection vectors together with power spectrum model. For obtaining grid position, the image projection vectors were utilized by adequately considering the grid parameters. On the other hand, as a preprocessing procedure of microarray gridding, detection of the grid rotation was involved in our study by using power spectrum analyses of the image projection vectors. Our approach has been evaluated by three different microarray datasets. Experimental comparisons with up-to-date approaches by using both synthetic and real image data are demonstrated. The gridding result was shown to be very accurate, and able to provide correct gridding dataset for the downstream microarray analyses. In summary, our study demonstrated the combination of image projection vectors with power spectrum model as a powerful strategy for microarray image gridding.

Unsupervised SVM-based gridding for DNA microarray images

This paper presents a novel method for unsupervised DNA microarray gridding based on support vector machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray gridding is the separation of the spots into distinct cells. The positions of the spots on a DNA microarray image are first detected using image analysis operations and then a set of soft-margin linear SVM classifiers is used to estimate the optimal layout of the grid lines in the image. Each grid line is the separating line produced by one of the SVM classifiers, which maximizes the margin between two consecutive rows or columns of spots. The classifiers are trained using the spot locations as training vectors. The proposed method was evaluated on reference microarray images containing more than two million spots in total. The results illustrate its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotation. The comparison to state of the art methods for microarray gridding reveals the superior performance of the proposed method. In 96.4% of the cases, the spots reside completely inside their respective grid cells.

Adaptive techniques for microarray image analysis with related quality assessment

We propose novel techniques for microarray image analysis. In particular, we describe an overall pipeline able to solve the most common problems of microarray image analysis. We propose the microarray image rotation algorithm (MIRA) and the statistical gridding pipeline (SGRIP) as two advanced modules devoted to restoring the original microarray grid orientation and to detecting, the correct geometrical information about each spot of input microarray, respectively. Both solutions work by making use of statistical observations, obtaining adaptive and reliable information about each spot property. They improve the performance of the microarray image segmentation pipeline (MISP) we recently developed. MIRA, MISP, and SGRIP modules have been developed as plug-ins for an advanced framework for microarray image analysis. A new quality measure able to effectively evaluate the adaptive segmentation with respect to the fixed (e.g., nonadaptive) circle segmentation of each spot is proposed. Experiments confirm the effectiveness of the proposed techniques in terms of visual and numerical data.

Improving gene quantification by adjustable spot-image restoration

One of the major factors that complicate the task of microarray image analysis is that microarray images are distorted by various types of noise. In this study a robust framework is proposed, designed to take into account the effect of noise in microarray images in order to assist the demanding task of microarray image analysis. The proposed framework, incorporates in the microarray image processing pipeline a novel combination of spot adjustable image analysis and processing techniques and consists of the following stages: (1) gridding for facilitating spot identification, (2) clustering (unsupervised discrimination between spot and background pixels) applied to spot image for automatic local noise assessment, (3) modeling of local image restoration process for spot image conditioning (adjustable wiener restoration using an empirically determined degradation function), (4) automatic spot segmentation employing seeded-region-growing, (5) intensity extraction and (6) assessment of the reproducibility (real data) and the validity (simulated data) of the extracted gene expression levels. Both simulated and real microarray images were employed in order to assess the performance of the proposed framework against well-established methods implemented in publicly available software packages (Scanalyze and SPOT). Regarding simulated images, the novel combination of techniques, introduced in the proposed framework, rendered the detection of spot areas and the extraction of spot intensities more accurate. Furthermore, on real images the proposed framework proved of better stability across replicates. Results indicate that the proposed framework improves spots' segmentation and, consequently, quantification of gene expression levels. All algorithms were implemented in Matlab (The Mathworks, Inc., Natick, MA, USA) environment. The codes that implement microarray gridding, adaptive spot restoration and segmentation/intensity extraction are available upon request. Supplementary results and the simulated microarray images used in this study are available for download from: ftp://users:bioinformatics@mipa.med.upatras.gr. Supplementary data are available at Bioinformatics online.

A Deformable Grid-Matching Approach for Microarray Images

A fundamental step of microarray image analysis is the detection of the grid structure for the accurate location of each spot, representing the state of a given gene in a particular experimental condition. This step is known as gridding and belongs to the class of deformable grid matching problems which are well known in literature. Most of the available microarray gridding approaches require human intervention; for example, to specify landmarks, some points in the spot grid, or even to precisely locate individual spots. Automating this part of the process can allow high throughput analysis. This paper focuses on the development of a fully automated procedure for the problem of automatic microarray gridding. It is grounded on the Bayesian paradigm and on image analysis techniques. The procedure has two main steps. The first step, based on the Radon transform, is aimed at generating a grid hypothesis; the second step accounts for local grid deformations. The accuracy and properties of the procedure are quantitatively assessed over a set of synthetic and real images; the results are compared with well-known methods available from the literature.

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.

An automated procedure to properly handle digital images in large scale Tissue Microarray experiments

Tissue Microarray (TMA) methodology has been recently developed to enable "genome-scale" molecular pathology studies. To enable high-throughput screening of TMAs automation is mandatory, both to speed up the process and to improve data quality. In particular, in acquiring digital images of single tissues (core sections) a crucial step is the correct recognition of each tissue position in the array. In fact, further reliable data analysis is based on the exact assignment of each tissue to the corresponding tumor. As most of the times tissue alignment in the microarray grid is far from being perfect, simple strategies to perform proper acquisition do not fit well. The present paper describes a new solution to automatically perform grid location assignment. We developed an ad hoc image processing procedure and a robust algorithm for object recognition. Algorithm accuracy tests and assessment of working constraints are discussed. Our approach speeds up TMA data collection and enables large scale investigation.

Novel antibacterials: a genomics approach to drug discovery.

The appearance of antibiotic resistant pathogens, including vancomycin resistant Staphylococcus aureus, in the clinic has necessitated the development of new antibiotics. The golden age of antibiotic discovery, in which potent selective compounds were readily extracted from natural product extracts is over and novel approaches need to be implemented to cover the therapeutic shortfall. The generation of huge quantities of bacterial sequence data has allowed the identification of all the possible targets for therapeutic intervention and allowed the development of screens to identify inhibitors. Here, we described a number of target classes in which genomics has contributed to its identification. As a result of analyzing sequence data, all of the tRNA synthetases and all of the two-component signal transduction systems were readily isolated; which would not have been easily identified if whole genome sequences were not available. Fatty acid biosynthesis is a known antibacterial target, but genomics showed which genes in that pathway had the appropriate spectrum to be considered as therapeutic targets. Genes of unknown function may seem untractable targets, but if those that are broad spectrum and essential are identified, it becomes valuable to invest time and effort to determine their cellular role. In addition, we discuss the role of genomics in developing technologies that assist in the discovery of new antibiotics including microarray gridding technology. Genomics can also increase the chemical diversity against which the novel targets can be screened.