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Computing the maximum similarity bi-clusters of gene expression data

BIOINFORMATICS 2007年第1期23卷 50-56页

作者： Liu, Xiaowen Wang, Lusheng City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

Motivations: Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions. Results: We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters.

关键词： microarray data

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VISDA:: an open-source caBIG™ analytical tool for data clustering and beyond

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BIOINFORMATICS 2007年第15期23卷 2024-2027页

作者： Wang, Jiajing Li, Huai Zhu, Yitan Yousef, Malik Nebozhyn, Michael Showe, Michael Showe, Louise Xuan, Jianhua Clarke, Robert Wang, Yue Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA 22203 USA NIA NIH RRB Bioinformat Unit Baltimore MD 21224 USA Wistar Inst Anat & Biol Philadelphia PA 19104 USA Georgetown Univ Lombardi Comprehens Camc Ctr Washington DC 20057 USA Georgetown Univ Dept Oncol Washington DC 20057 USA

VISDA (Visual Statistical data Analyzer) is a caBIG (TM) analytical tool for cluster modeling, visualization and discovery that has met silver-level compatibility under the caBIG initiative. Being statistically principled and visually interfaced, VISDA exploits both hierarchical statistics modeling and human gift for pattern recognition to allow a progressive yet interactive discovery of hidden clusters within high dimensional and complex biomedical datasets. The distinctive features of VISDA are particularly useful for users across the cancer research and broader research communities to analyze complex biological data.

关键词： GENE-EXPRESSION microarray data SOFTWARE PATTERNS

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Software development vis a vis collaboration in interdisciplinary research

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NEUROINFORMATICS 2007年第3期5卷 176-177页

作者： Herskovits, Edward H. Univ Penn Dept Radiol Philadelphia PA 19104 USA

The ability to reanalyze microarray data would otentiate research explaining the molecular bases of embryological development, aging, cancer, and a plethora of other diseases. This potential results from the rapidly growing volume of microarray data, and from the wide range of applications of this technology. It is, therefore, not surprising that one of the primary goals of bioinformatics researchers is the development of software that would allow biologists to share, combine, and reanalyze microarray data. Wan and Pavlidis present an excellent overview of efforts to make the reanalysis of microarray data a reality.

关键词： microarray data microarray Platform Laser Capture Microdissection Molecular Biologist microarray Gene Expression data

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Inferring gene regulatory networks using differential evolution with local search heuristics

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2007年第4期4卷 634-647页

作者： Noman, Nasimul Iba, Hitoshi Univ Tokyo Grad Sch Frontier Sci Iba Lab Tokyo Japan Univ Dhaka Dept Comp Sci & Engn Dhaka 1000 Bangladesh

We present a memetic algorithm for evolving the structure of biomolecular interactions and inferring the effective kinetic parameters from the time-series data of gene expression using the decoupled S-system formalism. We propose an Information Criteria-based fitness evaluation for gene network model selection instead of the conventional Mean Squared Error (MSE)-based fitness evaluation. A hill-climbing local-search method has been incorporated in our evolutionary algorithm for efficiently attaining the skeletal architecture that is most frequently observed in biological networks. The suitability of the method is tested in gene circuit reconstruction experiments, varying the network dimension and/or characteristics, the amount of gene expression data used for inference, and the noise level present in expression profiles. The reconstruction method inferred the network topology and the regulatory parameters with high accuracy. Nevertheless, the performance is limited to the amount of expression data used and the noise level present in the data. The proposed fitness function has been found to be more suitable for identifying the correct network topology and for estimating the accurate parameter values compared to the existing ones. Finally, we applied the methodology for analyzing the cell-cycle gene expression data of budding yeast and reconstructed the network of some key regulators.

关键词： biology and genetics inverse problems global optimization medicine and science microarray data gene regulatory system transcriptional regulation memetic algorithm

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CONTROL OF THE MEAN NUMBER OF FALSE DISCOVERIES, BONFERRONI AND STABILITY OF MULTIPLE TESTING

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ANNALS OF APPLIED STATISTICS 2007年第1期1卷 179-190页

作者： Gordon, Alexander Glazko, Galina Qiu, Xing Yakovlev, Andrei Univ Rochester Dept Biostat & Computat Biol Rochester NY 14642 USA Univ N Carolina Dept Math & Stat Charlotte NC 28223 USA

The Bonferroni multiple testing procedure is commonly perceived as being overly conservative in large-scale Simultaneous testing situations such as those that arise in microarray data analysis. The objective of the present study is to show that this Popular belief is due to overly stringent requirements that are typically imposed oil the procedure rather than to its conservative nature. To get over its notorious conservatism, we advocate using the Bonferroni selection rule as a procedure that controls the per family error rate (PFER). The present paper reports the first Study of stability properties of the Bonferroni and Benjamini-Hochberg procedures. The Bonferroni procedure shows I superior stability in terms of the variance of both the number of true discoveries and the total number of discoveries. a property that is especially important in the presence of correlations between individual p-values. Its stability and the ability to provide strong control of the PFER make the Bonferroni procedure an attractive choice in microarray studies.

关键词： Multiple testing stability Bonferroni method microarray data

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Inferring gene regulatory networks from multiple data sources via a dynamic Bayesian network with structural EM

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4th International Workshop on data Integration in the Life Sciences

作者： Zhang, Yu Deng, Zhidong Jiang, Hongshan Jia, Peifa Tsinghua Univ Comp Sci & Technol Dept State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci Beijing 100084 Peoples R China

ISBN: (纸本)9783540732549

Using our dynamic Bayesian network with structural Expectation Maximization (SEM-DBN), we develop a new framework to model gene regulatory network from both gene expression data and transcriptional factor binding site data. Only based on mRNA expression data, it is not enough to accurately estimate a gene network. It is difficult for us to estimate a gene network accurately only with the mRNA expression data. In this paper, we use the transcription factor binding location data in order to introduce the prior knowledge to SEM-DBN model. Gene expression data are also exploited specifically for likelihood. Meanwhile, we incorporate the prior knowledge into every learning step by SEM rather than only learning from the very beginning, which can compensate the attenuation of the effect with location data. The effectiveness of our proposed method is demonstrated through the analysis of Saccharomyces cerevisiae cell cycle data. The combination of heterogeneous data from multiple sources ensures that our results are more accurate than those recovered from only gene expression data alone.

关键词： gene regulatory networks dynamic Bayesian network structural expectation maximization microarray data transcription factor binding location data

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A multivariate extension of the gene set enrichment analysis

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Journal of Bioinformatics and Computational Biology 2007年第5期5卷 1139-1153页

作者： Klebanov, Lev Glazko, Galina Salzman, Peter Yakovlev, Andrei Xiao, Yuanhui Department of Probability and Statistics Charles University Praha-8 CZ-18675 Sokolovska 83 Czech Republic Department of Biostatistics and Computational Biology University of Rochester Rochester NY 14642 601 Elmwood Avenue United States Department of Mathematics and Statistics Georgia State University Atlanta GA 30303 United States

A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-statistic, is obtained by permuting group labels. Our simulation studies and analysis of biological data confirm the conjecture that the N-statistic is a much better choice for multivariate significance testing within the framework of the GSEA. We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research. © 2007 Imperial College Press.

关键词： Gene sets microarray data Multivariate testing Probability distance

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State space modeling of yeast gene expression dynamics

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Journal of Bioinformatics and Computational Biology 2007年第1期5卷 31-46页

作者： Haavisto, Olli Hyötyniemi, Heikki Roos, Christophe Control Engineering Laboratory Helsinki University of Technology FI-02015 TKK Helsinki PO Box 5500 Finland Medicel Ltd. FI-00350 Helsinki Huopalahdentie 24 Finland

Combined interaction of all the genes forms a central part of the functional system of a cell. Thus, especially the data-based modeling of the gene expression network is currently one of the main challenges in the field of systems biology. However, the problem is an extremely high-dimensional and complex one, so that normal identification methods are usually not applicable specially if aiming at dynamic models. We propose in this paper a subspace identification approach, which is well suited for high-dimensional system modeling and the presented modified version can also handle the underdetermined case with less data samples than variables (genes). The algorithm is applied to two public stress-response data sets collected from yeast Saccharomyces cerevisiae. The obtained dynamic state space model is tested by comparing the simulation results with the measured data. It is shown that the identified model can relatively well describe the dynamics of the general stress-related changes in the expression of the complete yeast genome. However, it seems inevitable that more precise modeling of the dynamics of the whole genome would require experiments especially designed for systemic modeling. © 2007 Imperial College Press.

关键词： Dynamic modeling Gene expression microarray data State space model Subspace identification

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Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment

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BMC BIOINFORMATICS 2004年第1期5卷 5-5页

作者： Konishi, T Akita Prefectural Univ Fac Bioresource Sci Akita 0100195 Japan

Background: To cancel experimental variations, microarray data must be normalized prior to analysis. Where an appropriate model for statistical data distribution is available, a parametric method can normalize a group of data sets that have common distributions. Although such models have been proposed for microarray data, they have not always fit the distribution of real data and thus have been inappropriate for normalization. Consequently, microarray data in most cases have been normalized with non-parametric methods that adjust data in a pair-wise manner. However, data analysis and the integration of resultant knowledge among experiments have been difficult, since such normalization concepts lack a universal standard. Results: A three-parameter lognormal distribution model was tested on over 300 sets of microarray data. The model treats the hybridization background, which is difficult to identify from images of hybridization, as one of the parameters. A rigorous coincidence of the model to data sets was found, proving the model's appropriateness for microarray data. In fact, a closer fitting to Northern analysis was obtained. The model showed inconsistency only at very strong or weak data intensities. Measurement of z-scores as well as calculated ratios was reproducible only among data in the model-consistent intensity range;also, the ratios were independent of signal intensity at the corresponding range. Conclusion: The model could provide a universal standard for data, simplifying data analysis and knowledge integration. It was deduced that the ranges of inconsistency were caused by experimental errors or additive noise in the data;therefore, excluding the data corresponding to those marginal ranges will prevent misleading analytical conclusions.

关键词： microarray data Additive Noise Northern Analysis Calculated Ratio Technical Reproducibility

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GSMA: Gene Set Matrix Analysis, An Automated Method for Rapid Hypothesis Testing of Gene Expression data

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BIOINFORMATICS AND BIOLOGY INSIGHTS 2007年第unknown期1卷 49-62页

作者： Cheadle, Chris Watkins, Tonya Fan, Jinshui Williams, Marc A. Georas, Steven Hall, John Rosen, Antony Barnes, Kathleen C. Johns Hopkins Univ Sch Med Div Allergy & Clin Immunol Genom Core Baltimore MD 21224 USA Univ Rochester Sch Med & Dent Div Pulm & Crit Care Med Rochester NY USA Johns Hopkins Univ Sch Med Div Rheumatol Baltimore MD 21224 USA

Background: microarray technology has become highly valuable for identifying complex global changes in gene expression patterns. The assignment of functional information to these complex patterns remains a challenging task in effectively interpreting data and correlating results from across experiments, projects and laboratories. Methods which allow the rapid and robust evaluation of multiple functional hypotheses increase the power of individual researchers to data mine gene expression data more efficiently. Results: We have developed (gene set matrix analysis) GSMA as a useful method for the rapid testing of group-wise up-or downregulation of gene expression simultaneously for multiple lists of genes (gene sets) against entire distributions of gene expression changes (datasets) for single or multiple experiments. The utility of GSMA lies in its flexibility to rapidly poll gene sets related by known biological function or as designated solely by the end-user against large numbers of datasets simultaneously. Conclusions: GSMA provides a simple and straightforward method for hypothesis testing in which genes are tested by groups across multiple datasets for patterns of expression enrichment.

关键词： .Enrichment statistically gene sets microarray data changes in gene Lists hypothesis testing matrix analysis GSMA Gene

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