Clustering for DNA Microarray Data Analysis with a Graph Cut Based Algorithm

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.

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.

We investigate discrete structures and combinatoric modeling of weighted prefix trees for managing and analyzing DNA microarray data. We describe the algorithms to construct the weighted trees for these data. Using these weighted trees with our algorithms, we propose methods to compute the appearance probability of a DNA microarray, to compare the informational distances in the expression of genes between the DNA microarrays, to search the characteristic microarrays and the group of candidate genes suggestive of a pathology.

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.

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.

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.

An initiative to increase biopharmaceutical research productivity by capturing, sharing and computationally integrating proprietary scientific discoveries with public knowledge is described. This initiative involves both organisational process change and multiple interoperating software systems. The software components rely on mutually supporting integration techniques. These include a richly structured ontology, statistical analysis of experimental data against stored conclusions, natural language processing of public literature, secure document repositories with lightweight metadata, web services integration, enterprise web portals and relational databases. This approach has already begun to increase scientific productivity in our enterprise by creating an organisational memory (OM) of internal research findings, accessible on the web. Through bringing together these components it has also been possible to construct a very large and expanding repository of biological pathway information linked to this repository of findings which is extremely useful in analysis of DNA microarray data. This repository, in turn, enables our research paradigm to be shifted towards more comprehensive systems-based understandings of drug action.

Statistical inferences based on outliers for gene expression analysis

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.

In this paper, we proposed a new testing statistic for testing the equality of mean vectors from two multivariate normal populations when the covariance matrices are unknown and unequal in high–dimensional data. A new test is proposed based on the idea of keeping more information from the sample covariance matrices as much as possible. A proposed test is invariant under scalar transformations and location shifts. We showed that the asymptotic distribution of proposed statistic is standard normal distribution when number of random variables approach infinity. We also compared the performance of the proposed test with other three existing tests by the simulation study. The simulation results showed that the attained significance level of proposed test close to setting nominal significance level satisfactorily. The attained power of proposed test outperforms as the other comparative tests under form of covariance matrices considered which can be arranged to block diagonal matrix structure. The attained power becomes more powerful when the dimension increases for a given sample size or vice versa, or relationship level between random variables in each sample increases. Finally, the proposed test is also illustrated with an analysis of DNA microarray data.

We used discrete combinatoric methods and non numerical algorithms [9], based on weighted prefix trees, to examine the data mining of DNA microarray data, in order to capture biological or medical informations and extract new knowledge from these data. We describe hierarchical cluster analysis of DNA microarray data using structure of weighted trees in two manners : classifying the degree of overlap between different microarrays and classifying the degree of expression levels between different genes. These are most efficiently done by finding the characteristic genes and microarrays with the maximum degree of overlap and determining the group of candidate genes suggestive of a pathology.Keywordscombinatoric of wordsweighted treesdata miningcluster analysisDNA microarrays

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

6. Analysis of large-scale DNA microarray data to identify auxin-biosynthesis inhibitor