Management and analysis of DNA microarray data by using weighted trees

DNA Microarray Technique Current Analytical Methods of DNA Microarray Data Advantages and Disadvantages Current Application of DNA Microarray Technique in Medicine A Novel Method for DNA Microarray Data Analysis Future Directions.

SummaryTo review the current state of the art in computational methods for the analysis of DNA microarray data.The review considers methods of microarray data collection, transformation and representation, comparisons and predictions of gene expression from the data, their mechanistic analysis, related systems biology, and the application of clustering techniques.Functional genomics approaches have greatly increased the rate at which data on biological systems is generated, leading to corresponding challenges in analyzing the data through advanced computational techniques . The paper compares and contrasts the application of computational clustering for discovery, comparison, and prediction of gene expression classes, together with their evaluation and relation to mechanistic analyses of biological systems.Methods for assaying gene expression levels by DNA microarray experiments produce considerably more data than other techniques, and require a wide variety of computational techniques for identifying patterns of expression that may be biologically significant. These will have to be verified and validated by comparison to results from other methods, integrated with other systems data, and provide the feedback for further experimentation for testing mechanistic or other biological hypotheses.

Development of public toxicogenomics software for microarray data management and analysis

Clustering is an important approach to the analysis of DNA microarray data. In this paper, we develop a new algorithm that can cluster DNA microarray data with a graph cut based algorithm. The algorithm can generate a list of clustering results with statistically significant likelihood. It can thus resolve the issue where a gene product may participate in different subsets of co-expressed genes. Our testing results on two biological sets showed that this approach can achieve improved clustering accuracy, compared with other clustering methods.

In a number of domains, such as DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of constant rows with a group of columns. This kind of clustering is called biclustering. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed. We introduce a new algorithm called BestBinBicluster for biclustering of binary microarray data. Our biclustering algorithm BestBinBicluster extracts a group of biclusters from binary matrix Mb. This algorithm adopts the strategy of one bicluster at a time. It is a novel alternative to extract biclusters from binary data sets. Our algorithm is based on the use of a polynomial function to search Greatest Common Divisor (GCD) and the use of Galois Lattice. We propose to generate the Enumerative BinBicluster Lattice. The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data; our algorithm outperforms other biclustering algorithms for binary microarray data. We test the biological significance using a gene annotation web tool to show that our proposed method is able to produce biologically relevant biclusters.

physiological genomics ( PG ) publishes, on a quarterly basis, brief editorial perspectives entitled “Physiological genomics in PG and beyond.” These editorial perspectives provide brief, accessible highlights of the studies published in PG in the most recent quarter, and relate those studies to

High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.

We describe the use of a higher-order singular value decomposition (HOSVD) in transforming a data tensor of genes x "x-settings," that is, different settings of the experimental variable x x "y-settings," which tabulates DNA microarray data from different studies, to a "core tensor" of "eigenarrays" x "x-eigengenes" x "y-eigengenes." Reformulating this multilinear HOSVD such that it decomposes the data tensor into a linear superposition of all outer products of an eigenarray, an x- and a y-eigengene, that is, rank-1 "subtensors," we define the significance of each subtensor in terms of the fraction of the overall information in the data tensor that it captures. We illustrate this HOSVD with an integration of genome-scale mRNA expression data from three yeast cell cycle time courses, two of which are under exposure to either hydrogen peroxide or menadione. We find that significant subtensors represent independent biological programs or experimental phenomena. The picture that emerges suggests that the conserved genes YKU70, MRE11, AIF1, and ZWF1, and the processes of retrotransposition, apoptosis, and the oxidative pentose phosphate pathway that these genes are involved in, may play significant, yet previously unrecognized, roles in the differential effects of hydrogen peroxide and menadione on cell cycle progression. A genome-scale correlation between DNA replication initiation and RNA transcription, which is equivalent to a recently discovered correlation and might be due to a previously unknown mechanism of regulation, is independently uncovered.

RSCTC'2010 Discovery Challenge was a special event of Rough Sets and Current Trends in Computing conference. The challenge was organized in the form of an interactive on-line competition, at TUNEDIT.org platform, in days between Dec 1, 2009 and Feb 28, 2010. The task was related to feature selection in analysis of DNA microarray data and classification of samples for the purpose of medical diagnosis or treatment. Prizes were awarded to the best solutions. This paper describes organization of the competition and the winning solutions.

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

DNA microarrays are a well-known and established technology in biological and pharmaceutical research providing a wealth of information essential for understanding biological processes and aiding drug development. Protein microarrays are quickly emerging as a follow-up technology, which will also begin to experience rapid growth as the challenges in protein to spot methodologies are overcome. Like DNA microarrays, their protein counterparts produce large amounts of data that must be suitably analyzed in order to yield meaningful information that should eventually lead to novel drug targets and biomarkers. Although the statistical management of DNA microarray data has been well described, there is no available report that offers a successful consolidated approach to the analysis of high-throughput protein microarray data. We describe the novel application of a statistical methodology to analyze the data from an immune response profiling assay using human protein microarray with over 5000 proteins on each chip.

The tissue of origin of the cell line MDA-MB-435 has been a matter of debate since analysis of DNA microarray data led Ross et al. (2000, Nat Genet 24(3):227-235) to suggest they might be of melanocyte origin due to their similarity to melanoma cell lines. We have previously shown that MDA-MB-435 cells maintained in multiple laboratories are of common origin to those used by Ross et al. and concluded that MDA-MB-435 cells are not a representative model for breast cancer. We could not determine, however, whether the melanoma-like properties of the MDA-MB-435 cell line are the result of misclassification or due to transdifferention to a melanoma-like phenotype. We used karyotype, comparative genomic hybridization (CGH), and microsatalite polymorphism analyses, combined with bioinformatics analysis of gene expression and single nucleotide polymorphism (SNP) data, to test the hypothesis that the MDA-MB-435 cell line is derived from the melanoma cell line M14. We show that the MDA-MB-435 and M14 cell lines are essentially identical with respect to cytogenetic characteristics as well as gene expression patterns and that the minor differences found can be explained by phenotypic and genotypic clonal drift. All currently available stocks of MDA-MB-435 cells are derived from the M14 melanoma cell line and can no longer be considered a model of breast cancer. These cells are still a valuable system for the study of cancer metastasis and the extensive literature using these cells since 1982 represent a valuable new resource for the melanoma research community.

‘‘Genomics in Endocrinology’’ edited by Stuart Handwerger, M.D. and Bruce J. Aronow, Ph.D. presents a series of short articles by hormonal researchers who utilized DNA microarray techniques to analyze the expression of diverse genes in different hormone-producing and hormoneresponsive tissues. In addition, gene expression was monitored in diseased tissues using the DNA microarray approach. This monograph represents a state-of-the-art summary of the present use of DNA microarray analyses in hormonal research for readers interested in this topic. The interesting chapters by 40 some authors represent key DNA microarray findings in the field and provide a useful resource for readers to get an overview of this area of research. In addition, the introductory chapter by the editors provides a useful lead-in to the general approach, including data analyses, statistical consideration, data storage and validation, as well as potential pitfalls. The major advantage of the DNA microarray approach is certainly the high-throughput, genome-wide analyses and the identification of candidate genes important for physiological processes of interest. Although many of the chapters deal with a general survey of the expression of multiple genes, few chapters used DNA microarray data to provide molecular classification of different disease states. Of particular interest, several chapters described more advanced use of DNA microarray data, including analyses of promoter regions of co-expressed genes, identification of candidate genes related to a particular disease, advanced chromatin immuno-precipitation microarray for genes regulated by transcriptional factors, and attempts to reveal gene pathways based on expression data. The overall field is progressing from the straightforward description of gene expression patterns to the formulation of new hypotheses based on gene expression to allow a better understanding of role of genes in physiology and pathophysiology. A more appropriate title for the present monograph is probably ‘‘Transcriptomics of hormone-producing and hormone-responsive tissues’’ due to the emphasis on DNA microarray analyses and a lack of information on genomic topics such as single nucleotide polymorphism, gene copy numbers, genome-wide analyses of the phylogenetic relationship of hormone and receptor genes, identification of new hormone and receptor genes, and other genomic issues.

Statistical inferences based on outliers for gene expression analysis