Analysis Of DNA Microarray Data Research Articles

Recurrent copy number variants associated with bronchopulmonary dysplasia.

Variability in the incidence and severity of bronchopulmonary dysplasia (BPD) among premature infants suggests that genetic susceptibility plays a role in pathogenesis. An assessment of copy number variants (CNV) in BPD subjects may help to identify loci that harbor genetic susceptibility factors. We conducted a retrospective analysis of clinical DNA microarray data from our institution. We identified 19 BPD subjects, and 2 controls groups (full-term and preterm) with no lung-related disease. We reanalyzed raw data from each of these subjects to identify recurrent CNV loci in BPD subjects. We identified three loci (at 11q13.2, 16p13.3, and 22q11.23-q12.1) with recurrent CNV in BPD subjects. The frequency of these CNV was significantly higher in BPD subjects when compared with at least one control group. We interrogated 21 genes residing within the recurrent CNV regions for development-associated changes in expression. Fifteen genes demonstrated significant changes in expression between the pseudoglandular and canalicular stage in human lungs, a time commensurate with birth at highest risk for BPD. We also identified pathways represented by the genes present within the recurrent loci. These data identify novel loci that may harbor genes contributing to the genetic susceptibility of BPD.

Bayesian nonparametric multiple testing

Multiple testing, or multiplicity problems often require testing several means with the assumption of rejecting infrequently, as motivated by the need to analyze DNA microarray data. The goal is to keep the combined rate of false discoveries and non-discoveries as small as possible. A discrete approximation to a Polya tree prior that enjoys fast, conjugate updating, centered at the usual Gaussian distribution is proposed. This new technique and the advantages of this approach are demonstrated using extensive simulation and data analysis accompanied by a Java web application. The numerical studies demonstrate that this new procedure shows promising false discovery rate and estimation of key values in the mixture model with very reasonable computational speed.

Differentially expressed genes in metastatic advanced Egyptian bladder cancer.

Bladder cancer is one of the most common cancers worldwide. Gene expression profiling using microarray technologies improves the understanding of cancer biology. The aim of this study was to determine the gene expression profile in Egyptian bladder cancer patients. Samples from 29 human bladder cancers and adjacent non-neoplastic tissues were analyzed by cDNA microarray, with hierarchical clustering and multidimensional analysis. Five hundred and sixteen genes were differentially expressed of which SOS1, HDAC2, PLXNC1, GTSE1, ULK2, IRS2, ABCA12, TOP3A, HES1, and SRP68 genes were involved in 33 different pathways. The most frequently detected genes were: SOS1 in 20 different pathways; HDAC2 in 5 different pathways; IRS2 in 3 different pathways. There were 388 down-regulated genes. PLCB2 was involved in 11 different pathways, MDM2 in 9 pathways, FZD4 in 5 pathways, p15 and FGF12 in 4 pathways, POLE2 in 3 pathways, and MCM4 and POLR2E in 2 pathways. Thirty genes showed significant differences between transitional cell cancer (TCC) and squamous cell cancer (SCC) samples. Unsupervised cluster analysis of DNA microarray data revealed a clear distinction between low and high grade tumors. In addition 26 genes showed significant differences between low and high tumor stages, including fragile histidine triad, Ras and sialyltransferase 8 (alpha) and 16 showed significant differences between low and high tumor grades, like methionine adenosyl transferase II, beta. The present study identified some genes, that can be used as molecular biomarkers or target genes in Egyptian bladder cancer patients.

A New Approach for Mining Order-Preserving Submatrices Based on All Common Subsequences.

Order-preserving submatrices (OPSMs) have been applied in many fields, such as DNA microarray data analysis, automatic recommendation systems, and target marketing systems, as an important unsupervised learning model. Unfortunately, most existing methods are heuristic algorithms which are unable to reveal OPSMs entirely in NP-complete problem. In particular, deep OPSMs, corresponding to long patterns with few supporting sequences, incur explosive computational costs and are completely pruned by most popular methods. In this paper, we propose an exact method to discover all OPSMs based on frequent sequential pattern mining. First, an existing algorithm was adjusted to disclose all common subsequence (ACS) between every two row sequences, and therefore all deep OPSMs will not be missed. Then, an improved data structure for prefix tree was used to store and traverse ACS, and Apriori principle was employed to efficiently mine the frequent sequential pattern. Finally, experiments were implemented on gene and synthetic datasets. Results demonstrated the effectiveness and efficiency of this method.

Subspace Clustering of DNA Microarray Data

Identification of biological significant subspace clusters (biclusters and triclusters) of genes from microarray experimental data is a very daunting task that emerged, especially with the development of high throughput technologies. Several methods and applications of subspace clustering (biclustering and triclustering) in DNA microarray data analysis have been developed in recent years. Various computational and evaluation methods based on diverse principles were introduced to identify new similarities among genes. This review discusses and compares these methods, highlights their mathematical principles, and provides insight into the applications to solve biological problems.

Meta-Analysis of Pathway Enrichment: Combining Independent and Dependent Omics Data Sets

A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.

Eureka-DMA: an easy-to-operate graphical user interface for fast comprehensive investigation and analysis of DNA microarray data.

BackgroundIn the past decade, the field of molecular biology has become increasingly quantitative; rapid development of new technologies enables researchers to investigate and address fundamental issues quickly and in an efficient manner which were once impossible. Among these technologies, DNA microarray provides methodology for many applications such as gene discovery, diseases diagnosis, drug development and toxicological research and it has been used increasingly since it first emerged. Multiple tools have been developed to interpret the high-throughput data produced by microarrays. However, many times, less consideration has been given to the fact that an extensive and effective interpretation requires close interplay between the bioinformaticians who analyze the data and the biologists who generate it. To bridge this gap and to simplify the usability of such tools we developed Eureka-DMA — an easy-to-operate graphical user interface that allows bioinformaticians and bench-biologists alike to initiate analyses as well as to investigate the data produced by DNA microarrays.ResultsIn this paper, we describe Eureka-DMA, a user-friendly software that comprises a set of methods for the interpretation of gene expression arrays. Eureka-DMA includes methods for the identification of genes with differential expression between conditions; it searches for enriched pathways and gene ontology terms and combines them with other relevant features. It thus enables the full understanding of the data for following testing as well as generating new hypotheses. Here we show two analyses, demonstrating examples of how Eureka-DMA can be used and its capability to produce relevant and reliable results.ConclusionsWe have integrated several elementary expression analysis tools to provide a unified interface for their implementation. Eureka-DMA's simple graphical user interface provides effective and efficient framework in which the investigator has the full set of tools for the visualization and interpretation of the data with the option of exporting the analysis results for later use in other platforms. Eureka-DMA is freely available for academic users and can be downloaded at http://blue-meduza.org/Eureka-DMA.

Screening of key genes of unruptured intracranial aneurysms by using DNA microarray data analysis techniques

This study aimed to identify differentially expressed genes (DEGs) of unruptured intracranial aneurysms (IAs) and provide beneficial information for early diagnosis and treatment of IAs. The gene expression profile GSE26969 from the Gene Expression Omnibus database was downloaded, which included six human IA samples: three intracranial arterial aneurysm samples and three normal superficial temporal artery samples (control). Based on these data, we identified the DEGs between normal and disease samples with packages in the R language. The selected DEGs were further analyzed using bioinformatic methods. First, the STRING software was used to search co-expression relationships among DEGs, and the most important hub gene was found. We then used the plugins of the Cytoscape software, Mcode and Bingo, to conduct a module analysis. Next, pathways of the module genes were annotated using the FuncAssociate program. Compared with the control group, we obtained 169 DEGs in total, and by mining a module with the hub gene MYH11, we retrieved the ACTA2, MYH11, MYLK, and MYL9 genes, which were all in the module and were most significantly related to vascular smooth muscle contraction. We hypothesize that the genes identified here can be beneficial for early diagnosis and treatment of IAs.

Gene Features Selection for Three-Class Disease Classification via Multiple Orthogonal Partial Least Square Discriminant Analysis and S-Plot Using Microarray Data

MotivationDNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes.ResultsHere we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub’s leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

An Evolutionary and Visual Framework for Clustering of DNA Microarray Data

Summary This paper presents a case study to show the competence of our evolutionary and visual framework for cluster analysis of DNA microarray data. The proposed framework joins a genetic algorithm for hierarchical clustering with a set of visual components of cluster tasks given by a tool. The cluster visualization tool allows us to display different views of clustering results as a means of cluster visual validation. The results of the genetic algorithm for clustering have shown that it can find better solutions than the other methods for the selected data set. Thus, this shows the reliability of the proposed framework.

Archetypal analysis of diverse Pseudomonas aeruginosatranscriptomes reveals adaptation in cystic fibrosis airways

BackgroundAnalysis of global gene expression by DNA microarrays is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully understand the underlying biological features present in the data.The aim of this study is to introduce a method for DNA microarray analysis that provides an intuitive interpretation of data through dimension reduction and pattern recognition. We present the first “Archetypal Analysis” of global gene expression. The analysis is based on microarray data from five integrated studies of Pseudomonas aeruginosa isolated from the airways of cystic fibrosis patients.ResultsOur analysis clustered samples into distinct groups with comprehensible characteristics since the archetypes representing the individual groups are closely related to samples present in the data set. Significant changes in gene expression between different groups identified adaptive changes of the bacteria residing in the cystic fibrosis lung. The analysis suggests a similar gene expression pattern between isolates with a high mutation rate (hypermutators) despite accumulation of different mutations for these isolates. This suggests positive selection in the cystic fibrosis lung environment, and changes in gene expression for these isolates are therefore most likely related to adaptation of the bacteria.ConclusionsArchetypal analysis succeeded in identifying adaptive changes of P. aeruginosa. The combination of clustering and matrix factorization made it possible to reveal minor similarities among different groups of data, which other analytical methods failed to identify. We suggest that this analysis could be used to supplement current methods used to analyze DNA microarray data.

Abstract 260: Cardiosphere Derived Cells Generated from the Human Atria Exhibit Superior Potency Compared to the Left Ventricle

Currently, allogeneic cardiosphere-derived cells (CDCs) are being tested in a phase I/II clinical trial. Our manufacturing process utilizes the atria and other regions of the heart to build a master cell bank, but leaves the left ventricle (LV) intact thus eliminating a potential source to build our clinical stocks. In an effort to increase CDC yield per heart, we compared CDCs generated from the atria to the left ventricle (LV). To investigate this hypothesis we characterized CDCs by flow cytometry, growth kinetics and in vivo potency. The surface marker profiles for atrial CDCs and LV CDCs were very similar. Of the 17 markers used to characterize CDCs (c-kit, MDR-1, Sca-1, Abcg2, CD133, CD31, CD34, CD45, CD105, CD29, CD44, CD73, CD166, CD140b, CD90, DDR2 and α-SMA) only one demonstrated substantial differential expression in LV versus atria. PDGFR-β (CD140b) was upregulated in CDCs derived from the LV compared to the atria. Consistent with this finding, GEO2R analysis of DNA microarray data revealed increased PDGFR-β and PDGF-B expression in normal human LV tissue compared to atrial tissue. We have also observed that CDC expression of PDGFR-β negatively correlates with in vivo potency (R=0.899) suggesting that the high expression of this marker in CDCs derived from the LV may limit its regenerative performance. Indeed when calculated growth rates were compared, tissue samples from the atria yielded 4 to 6-fold more CDCs compared to the LV (atria=26±10 versus LV=5±5 Million CDCs/g/day). Also, CDCs derived from the atria and LV led to differential improvements in ejection fraction (EF) over three weeks. CDCs from the atria significantly outperformed the control group (atrial CDCs=Δ+12.6±10.8%, control=Δ-15.3±13.6%), while CDCs from the LV showed minimal treatment effects and failed to meet our minimal potency requirement (LV CDCs=Δ+0.0±6.2%). In conclusion, LV CDCs display limited potential for clinical use. This observation provides a unique opportunity to explore the mechanisms that govern functional potency and assist in understanding the basic processes involved in CDC mediated repair.

Visual Analysis of DNA Microarray Data for Accurate Molecular Identification of Non-albicans Candida Isolates from Patients with Candidemia Episodes

The performance of a visual slide-based DNA microarray for the identification of non-albicans Candida spp. was evaluated. Among 167 isolates that had previously been identified by Vitek 2, the agreement between DNA microarray and sequencing results was 97.6%. This DNA microarray platform showed excellent performance.

Development and application of a DNA microarray-based yeast two-hybrid system

The yeast two-hybrid (Y2H) system is the most widely applied methodology for systematic protein–protein interaction (PPI) screening and the generation of comprehensive interaction networks. We developed a novel Y2H interaction screening procedure using DNA microarrays for high-throughput quantitative PPI detection. Applying a global pooling and selection scheme to a large collection of human open reading frames, proof-of-principle Y2H interaction screens were performed for the human neurodegenerative disease proteins huntingtin and ataxin-1. Using systematic controls for unspecific Y2H results and quantitative benchmarking, we identified and scored a large number of known and novel partner proteins for both huntingtin and ataxin-1. Moreover, we show that this parallelized screening procedure and the global inspection of Y2H interaction data are uniquely suited to define specific PPI patterns and their alteration by disease-causing mutations in huntingtin and ataxin-1. This approach takes advantage of the specificity and flexibility of DNA microarrays and of the existence of solid-related statistical methods for the analysis of DNA microarray data, and allows a quantitative approach toward interaction screens in human and in model organisms.

An evolutionary computational model applied to cluster analysis of DNA microarray data

This paper proposes a new hierarchical clustering method using genetic algorithms for the analysis of gene expression data. This method is based on the mathematical proof of several results, showing its effectiveness with regard to other clustering methods. Genetic algorithms applied to cluster analysis have disclosed good results on biological data and many studies have been carried out in this sense, although most of them are focused on partitional clustering methods. Even though there are few studies that attempt to use genetic algorithms for building hierarchical clustering, they do not include constraints that allow us to reduce the complexity of the problem. Therefore, these studies become intractable problems for large data sets. On the other hand, the deterministic hierarchical clustering methods generally face the problem of convergence towards local optimums due to their greedy strategy. The method introduced here is an alternative to solve some of the problems existing methods face. The results of the experiments have shown that our approach can be very effective in cluster analysis of DNA microarray data.

A visual analytics framework for cluster analysis of DNA microarray data

Cluster analysis of DNA microarray data is an important but difficult task in knowledge discovery processes. Many clustering methods are applied to analysis of data for gene expression, but none of them is able to deal with an absolute way with the challenges that this technology raises. Due to this, many applications have been developed for visually representing clustering algorithm results on DNA microarray data, usually providing dendrogram and heat map visualizations. Most of these applications focus only on the above visualizations, and do not offer further visualization components to the validate the clustering methods or to validate one another. This paper proposes using a visual analytics framework in cluster analysis of gene expression data. Additionally, it presents a new method for finding cluster boundaries based on properties of metric spaces. Our approach presents a set of visualization components able to interact with each other; namely, parallel coordinates, cluster boundary genes, 3D cluster surfaces and DNA microarray visualizations as heat maps. Experimental results have shown that our framework can be very useful in the process of more fully understanding DNA microarray data. The software has been implemented in Java, and the framework is publicly available at http://www.analiticavisual.com/jcastellanos/3DVisualCluster/3D-VisualCluster.

Similarity of Multivariate Methods to Establish Microarray Quality Control Standards

ABSTRACT This article describes a multivariate method based on logistic regression that distinguishes between usable and low-quality digital image spots associated with DNA microarray data analysis. To create a response variable describing spot quality, the authors analyzed the similarity among decisions that were made with control charts applied to quality parameters established in the gene expression measurement literature.

Gene expression profiling of Bangpungtongseong-san (Bofutsushosan) and Bangkihwangki-tang (Boiogito) administered individuals

Bangpungtongseong-san (Bofutsushosan, BTS) and Bangkihwangki-tang (Boiogito, BOT) are widely prescribed Korean, Japanese and Chinese traditional herbal medicines used to clinically prevent or improve obesity. In this study, we investigated genomewide transcriptional response to administration of either BTS or BOT to the Korean obese adults. From the blood samples, peripheral blood mononuclear cells (PBMCs) were isolated and RNA samples extracted from PBMCs were processed to conduct DNA microarray. Analysis of DNA microarray data revealed that administration of the both BTS and BOT evokes gene expression profile changes of PBMCs. In addition, comparative analysis between the drugs and placebo identified sets of specific genes regulated by BTS or BOT. Interestingly, analysis of gene list using functional annotation tool revealed that PMBCs of BOT-prescribed subjects have increased expression level of genes which are localized to mitochondria. The fact that mitochondrion is the main organelle of regulating cellular energy metabolism suggests that induced expressions of mitochondrial genes by BOT administration might be responsible for the anti-obesity effect of the medicine. We expect that further study based on our finding will provide molecular mechanisms of BTS and BOT effects on obesity.

Statistics of mixtures with varying concentrations with application to DNA microarray data analysis

A finite mixture model is considered in which the mixing probabilities vary from observation to observation. Estimation of mixture components distributions, functional moments and densities is discussed. Tests are proposed for testing hypotheses on the moments. An application to the analysis of DNA microarray data is considered.

DNA microarray SNP associations with clinical efficacy and side effects of domperidone treatment for gastroparesis

BackgroundDomperidone treatment for gastroparesis is associated with variable efficacy as well as the potential for side effects. DNA microarray single nucleotide polymorphism (SNP) analysis may help to elucidate the role of genetic variability on the therapeutic effectiveness and toxicity of domperidone. AimThe aim of this study was to identify SNPs that are associated with clinical efficacy and side effects of domperidone treatment for gastroparesis from DNA microarray experiments. This will help develop a strategy for rational selection of patients for domperidone therapy. MethodsDNA samples extracted from the saliva of 46 patients treated with domperidone were analyzed using Affymetrix 6.0 SNP microarrays. Then least angle regression (LARS) was used to select SNPs that are related to domperidone efficacy and side effects. Decision tree based prediction models were constructed with the most correlated features selected by LARS. ResultsUsing the most stable SNP selected by LARS a prediction model for side effects of domperidone achieved (95±0)% true negative rate (TN) and (78±11)% true positive rate (TP) in nested leave-one-out tests. For domperidone efficacy, the prediction based on five most stable SNPs achieved (85±7)% TP and (61±4)% TN. Five identified SNPs are related to ubiquitin mediated proteolysis, epithelial cell signaling, leukocyte, cell adhesion, and tight junction signaling pathways. Genetic polymorphisms in three genes that are related to cancer and hedgehog signaling were found to significantly correlate with efficacy of domperidone. ConclusionLARS was found to be a useful tool for statistical analysis of domperidone-related DNA microarray data generated from a small number of patients.