2,525 research outputs found
Microarray Data Preprocessing: From Experimental Design to Differential Analysis
DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.Peer reviewe
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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
Whole-transciptome analysis of [psi+] budding yeast via cDNA microarrays
Introduction: Prions of yeast present a novel analytical challenge in terms of both initial characterization and in vitro manipulation as models for human disease research. Presently, few robust analysis strategies have been successfully implemented which enable the efficient study of prion behavior in vivo. This study sought to evaluate the utilization of conventional dual-channel cDNA microarrays for the surveillance of transcriptomic regulation patterns by the [PSI+] yeast prion relative to an identical prion deficient yeast variant, [psi-]. Methods: A data analysis and normalization workflow strategy was developed and applied to cDNA array images, yielded quality-regulated expression ratios for a subset of genes exhibiting statistical congruence across multiple experimental repetitions and nested hybridization events. The significant gene list was analyzed using classical analytical approaches including several clustering-based methods and singular value decomposition. To add biological meaning to the differential expression data in hand, functional annotation using the Gene Ontology as well as several pathway-mapping approaches was conducted. Finally, the expression patterns observed were queried against all publicly curated microarray data performed using S. cerevisiae in order to discover similar expression behavior across a vast array of experimental conditions. Results: These data collectively implicate a low-level of overall genomic regulation as a result of the [PSI+] state, where the maximum statistically significant degree of differential expression was less than ±1 Log2(FC) in all cases. Notwithstanding, the [PSI+] differential expression was localized to several specific classes of structural elements and cellular functions, implying under homeostatic conditions significant up or down regulation is likely unnecessary but possible in those specific systems if environmental conditions warranted. As a result of these findings additional work pertaining to this system should include controlled insult to both yeast variants of differing environmental properties to promote a potential [PSI+] regulatory response coupled with co-surveillance of these conditions using transcriptomic and proteomic analysis methodologies
Nonuniform Power Changes and Spatial, Temporal and Spectral Diversity in High Gamma Band (\u3e60 Hz) Signals in Human Electrocorticography
High-gamma band: \u3e60Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. In spite of discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60Hz. Based on non-uniformities in time-frequency analyses of electrocorticographic: ECoG) signals, we hypothesized that induced high-gamma band: 60-500Hz) power changes are more heterogeneous than currently understood. We quantified this spectral non-uniformity with two different approaches using single-word repetition tasks in human subjects. First, we showed that the functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive tasks: e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions: sensorimotor, Broca\u27s area, and superior temporal gyrus), these behavior- and location- dependent power changes evidenced nonuniform trends across the population of subjects. Second, we studied the dynamics of multiple frequency bands in order to quantify the diversity present in the ECoG signals. Using a matched filter construct and receiver operating characteristic: ROC) analysis we show that power modulations correlated with phonemic content in spoken and heard words are represented diffusely in space, time and frequency. Correlating power modulation in multiple frequency bands above 60 Hz over broad cortical areas, with time varying envelopes significantly improved performed area under the ROC curve scores in phoneme prediction experiments. Finally we show preliminary evidence supporting our hypothesis in microarray ECoG data. Taken together, the nonuniformity of high frequency power changes and the information content captured in the spatio-temporal dynamics of those frequencies suggests that a new approach to evaluating high-gamma band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics
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Automating the processing of cDNA microarray images
This work is concerned with the development of an automatic image processing tool for DNA microarray images. This paper proposes, implements and tests a new tool for cDNA image analysis. The DNAs are imaged as thousands of circularly shaped objects (spots) on the microarray image and the purpose of this tool is to correctly address their location, segment the pixels belonging to spots and extract the quality features of each spot. Techniques used for addressing, segmentation and feature extraction of spots are described in detail. The results obtained with the proposed tool are systematically compared with conventional cDNA microarray analysis software tools
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