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Integrating Gene Expression and Phenotypic Information to Analyze Alzheimer's Disease

JOURNAL OF ALZHEIMERS DISEASE 2009年第1期16卷 73-84页

作者： Ray, Monika Zhang, Weixiong Washington Univ Dept Comp Sci & Engn St Louis MO USA Washington Univ Sch Med Dept Genet St Louis MO 63110 USA

The assessment of the relationship between gene expression profiling, clinical and histopathological phenotypes would be better suited to understanding Alzheimer's disease (AD) pathogenesis. We developed a multiple linear regression (MLR) method to simultaneously model three variables - Mini-Mental Status Examination (MMSE) score, neurofibrillary tangles (NFT) score and gene expression profile - to identify significant genes. These genes were also used to distinguish subjects with incipient AD from healthy controls. Finally we investigated the behavior of the significant genes across the entorhinal cortex and hippocampus of AD subjects in two different Braak stages. Results indicate that integrating multiple phenotypic and gene expression information of samples increases the power of methods while analyzing small datasets. The MLR method could identify significant genes at reasonable false discovery rates (FDRs), thereby providing a choice of reasonable FDRs. The accuracy in discriminating between subjects affected and unaffected by AD using MLR identified genes was high. We found that transcription and tumor suppressor responses do begin quite early in AD and therefore should be the target of drugs. Several genes were consistently up/down-regulated across the two brain regions and Braak stages and, therefore, can be used as predictive markers to detect AD at an earlier stage.

关键词： Alzheimer's disease classification gene selection microarray data Mini-Mental Status Examination neurofibrillary tangles score

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Hierarchical Bayesian meta-analysis models for cross-platform microarray studies

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JOURNAL OF APPLIED STATISTICS 2009年第10期36卷 1067-1085页

作者： Conlon, E. M. Postier, B. L. Methe, B. A. Nevin, K. P. Lovley, D. R. Univ Massachusetts Dept Math & Stat Amherst MA 01003 USA Univ Massachusetts Dept Microbiol Amherst MA 01003 USA

The development of new technologies to measure gene expression has been calling for statistical methods to integrate findings across multiple-platform studies. A common goal of microarray analysis is to identify genes with differential expression between two conditions, such as treatment versus control. Here, we introduce a hierarchical Bayesian meta-analysis model to pool gene expression studies from different microarray platforms: spotted DNA arrays and short oligonucleotide arrays. The studies have different array design layouts, each with multiple sources of data replication, including repeated experiments, slides and probes. Our model produces the gene-specific posterior probability of differential expression, which is the basis for inference. In simulations combining two and five independent studies, our meta-analysis model outperformed separate analyses for three commonly used comparison measures;it also showed improved receiver operating characteristic curves. When combining spotted DNA and CombiMatrix short oligonucleotide array studies of Geobacter sulfurreducens, our meta-analysis model discovered more genes for fixed thresholds of posterior probability of differential expression and Bayesian false discovery than individual study analyses. We also examine an alternative model and compare models using the deviance information criterion.

关键词： Bayesian statistics meta-analysis microarray data multiple platform Markov chain Monte Carlo deviance information criterion

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Gene subset selection using an iterative approach based on genetic algorithms

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ARTIFICIAL LIFE AND ROBOTICS 2009年第1期14卷 12-15页

作者： Mohamad, Mohd Omatu, Sigeru Deris, Safaai Yoshioka, Michifumi Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Univ Teknol Malaysia Fac Comp Sci & Informat Syst Dept Software Engn Skudai 81310 Johore Malaysia

microarray data are expected to be useful for cancer classification. However, the process of gene selection for the classification contains a major problem due to properties of the data such as the small number of samples compared with the huge number of genes (higher-dimensional data), irrelevant genes, and noisy data. Hence, this article aims to select a near-optimal (small) subset of informative genes that is most relevant for the cancer classification. To achieve this aim, an iterative approach based on genetic algorithms has been proposed. Experimental results show that the performance of the proposed approach is superior to other previous related work, as well as to four methods tried in this work. In addition, a list of informative genes in the best gene subsets is also presented for biological usage.

关键词： Gene selection Genetic algorithm Iterative approach microarray data

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Model-based redesign of global transcription regulation

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NUCLEIC ACIDS RESEARCH 2009年第5期37卷 e38-e38页

作者： Carrera, Javier Rodrigo, Guillermo Jaramillo, Alfonso Ecole Polytech CNRS Biochim Lab F-91128 Palaiseau France Univ Politecn Valencia CSIC Inst Biol Mol & Celular Plantas Valencia 46022 Spain Univ Politecn Valencia Inst Aplicac Tecnol Informat & Comunicac Avanzada Valencia 46022 Spain Univ Evry Val Essonne Genopole CNRS Epigenom Project F-91034 Evry France

Synthetic biology aims to the design or redesign of biological systems. In particular, one possible goal could be the rewiring of the transcription regulation network by exchanging the endogenous promoters. To achieve this objective, we have adapted current methods to the inference of a model based on ordinary differential equations that is able to predict the network response after a major change in its topology. Our procedure utilizes microarray data for training. We have experimentally validated our inferred global regulatory model in Escherichia coli by predicting transcriptomic profiles under new perturbations. We have also tested our methodology in silico by providing accurate predictions of the underlying networks from expression data generated with artificial genomes. In addition, we have shown the predictive power of our methodology by obtaining the gene profile in experimental redesigns of the E. coli genome, where rewiring the transcriptional network by means of knockouts of master regulators or by upregulating transcription factors controlled by different promoters. Our approach is compatible with most network inference methods, allowing to explore computationally future genome-wide redesign experiments in synthetic biology.

关键词： .providing profiles microarray data expression data obtaining predictive power knockouts perturbations global transcription

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Testing the prediction error difference between 2 predictors

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BIOSTATISTICS 2009年第3期10卷 550-560页

作者： van de Wiel, Mark A. Berkhof, Johannes van Wieringen, Wessel N. Vrije Univ Amsterdam Med Ctr Dept Epidemiol & Biostat NL-1007 MB Amsterdam Netherlands Vrije Univ Amsterdam Dept Math NL-1081 HV Amsterdam Netherlands

We develop an inference framework for the difference in errors between 2 prediction procedures. The 2 procedures may differ in any aspect and possibly utilize different sets of covariates. We apply training and testing on the same data set, which is accommodated by sample splitting. For each split, both procedures predict the response of the same samples, which results in paired residuals to which a signed-rank test is applied. Multiple splits result in multiple p-values. The median p-value and the mean inverse normal transformed p-value are proposed as summary (test) statistics, for which bounds on the overall type I error rate under a variety of assumptions are proven. A simulation study is performed to check type I error control of the least conservative bound. Moreover, it confirms superior power of our method with respect to a one-split approach. Our inference framework is applied to genomic survival data sets to study 2 issues: compare lasso and ridge regression and decide upon use of both methylation and gene expression markers or the latter only. The framework easily accommodates any prediction paradigm and allows comparing any 2, possibly nonmodel-based, prediction procedures.

关键词： Cross-validation microarray data Model comparison Nonparametric testing

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The Shivplot: a graphical display for trend elucidation and exploratory analysis of microarray data

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SOURCE CODE FOR BIOLOGY AND MEDICINE 2006年第1期1卷 6-6页

作者： Woody, Owen Z. Nadon, Robert McGill Univ 740 Ave Docteur Penfield Montreal PQ H3A 1A4 Canada Genome Quebec Innovat Ctr Montreal PQ H3A 1A4 Canada Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON N2L 3G1 Canada McGill Univ Dept Human Genet Montreal PQ Canada

Background: High-throughput systems are powerful tools for the life science research community. The complexity and volume of data from these systems, however, demand special treatment. Graphical tools are needed to evaluate many aspects of the data throughout the analysis process because plots can provide quality assessments for thousands of values simultaneously. The utility of a plot, in turn, is contingent on both its interpretability and its efficiency. Results: The shivplot, a graphical technique motivated by microarrays but applicable to any replicated high-throughput data set, is described. The plot capitalizes on the strengths of three wellestablished plotting graphics - a boxplot, a distribution density plot, and a variability vs intensity plot - by effectively combining them into a single representation. Conclusion: The utility of the new display is illustrated with microarray data sets. The proposed graph, retaining all the information of its precursors, conserves space and minimizes redundancy, but also highlights features of the data that would be difficult to appreciate from the individual display components. We recommend the use of the shivplot both for exploratory data analysis and for the communication of experimental data in publications.

关键词： microarray data Density Plot Simultaneous Interpretation Variability Plot Golden Spike data

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HYBRIDISING GENETIC ALGORITHM-NEURAL NETWORK (GANN) IN MARKER GENES DETECTION

HYBRIDISING GENETIC ALGORITHM-NEURAL NETWORK (GANN) IN MARKE...

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International Conference on Machine Learning and Cybernetics

作者： Tong, Dong-Ling Bournemouth Univ Sch Design Engn & Comp Poole BH12 5BB Dorset England

ISBN: (纸本)9781424447053

The identification of marker genes trigger the growth of mutated cells has received a significant attention from both medical and computing communities. Through the identified genes, the pathology of mutated cells can be revealed and precautions can be taken to prevent further proliferation of abnormal cells. In this paper, we propose an innovative gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia cancer. Our approach able to provide a sharper focus on a group of highly expressed genes in leukaemia dataset and the identified genes have been proven significant to the study of leukaemia cancer development.

关键词： Genetic algorithms neural networks microarray data gene selection fitness optimisation

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A note on oligonucleotide expression values not being normally distributed

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BIOSTATISTICS 2009年第3期10卷 446-450页

作者： Hardin, Johanna Wilson, Jason Pomona Coll Dept Math Claremont CA 91711 USA Biola Univ Dept Math La Mirada CA 90639 USA

Novel techniques for analyzing microarray data are constantly being developed. Though many of the methods contribute to biological discoveries, inability to properly evaluate the novel techniques limits their ability to advance science. Because the underlying distribution of microarray data is unknown, novel methods are typically tested against the assumed normal distribution. However, microarray data are not, in fact, normally distributed, and assuming so can have misleading consequences. Using an Affymetrix technical replicate spike-in data set, we show that oligonucleotide expression values are not normally distributed for any of the standard methods for calculating expression values. The resulting data tend to have a large proportion of skew and heavy tailed genes. Additionally, we show that standard methods can give unexpected and misleading results when the data are not well approximated by the normal distribution. Robust methods are therefore recommended when analyzing microarray data. Additionally, new techniques should be evaluated with skewed and/or heavy-tailed data distributions.

关键词： Affymetrix Distributions microarray data Nonnormality

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Particle swarm optimization for gene selection in classifying cancer classes

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ARTIFICIAL LIFE AND ROBOTICS 2009年第1期14卷 16-19页

The application of microarray data for cancer classification has recently gained in popularity. The main problem that needs to be addressed is the selection of a small subset of genes from the thousands of genes in the data that contribute to a disease. This selection process is difficult due to the availability of a small number of samples compared with the huge number of genes, many irrelevant genes, and noisy genes. Therefore, this article proposes an improved binary particle swarm optimization to select a near-optimal (small) subset of informative genes that is relevant for the cancer classification. Experimental results show that the performance of the proposed method is superior to the standard version of particle swarm optimization (PSO) and other previous related work in terms of classification accuracy and the number of selected genes.

关键词： Gene selection Hybrid approach microarray data Particle swarm optimization

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An Association Analysis Approach to Biclustering 09

An Association Analysis Approach to Biclustering

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15th ACM SIGKDD International Conference on Knowledge Discovery and data Mining

作者： Pandey, Gaurav Atluri, Gowtham Steinbach, Michael Myers, Chad L. Kumar, Vipin Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781605584959

The discovery of biclusters, which denote groups of items that show coherent values across a subset of all the transactions in a data set, is an important type of analysis performed on real-valued data sets in various domains, such as biology. Several algorithms have been proposed to find different types of biclusters in such data sets. However, these algorithms are unable to search the space of all possible biclusters exhaustively. Pattern mining algorithms in association analysis also essentially produce biclusters as their result, since the patterns consist of items that are supported by a subset of all the transactions. However, a major limitation of the numerous techniques developed in association analysis is that they are only able to analyze data sets with binary and/or categorical variables, and their application to real-valued data sets often involves some lossy transformation such as discretization or binarization of the attributes. In this paper, we propose a novel association analysis framework for exhaustively and efficiently mining "range support" patterns from such a data set. On one hand, this framework reduces the loss of information incurred by the binarization- and discretization-based approaches, and on the other, it enables the exhaustive discovery of coherent biclusters. We compared the performance of our framework with two standard biclustering algorithms through the evaluation of the similarity of the cellular functions of the genes constituting the patterns/biclusters derived by these algorithms from microarray data. These experiments show that the real-valued patterns discovered by our framework are better enriched by small biologically interesting functional classes. Also, through specific examples, we demonstrate the ability of the RAP framework to discover functionally enriched patterns that are not found by the commonly used biclustering algorithm ISA. The source code and data sets used in this paper, as well as the supplementary materi

关键词： Real-valued data biclustering association analysis range support microarray data functional modules

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