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Graph-based iterative Group Analysis enhances microarray interpretation

BMC BIOINFORMATICS 2004年第1期5卷 1-10页

作者： Breitling, R Amtmann, A Herzyk, P Univ Glasgow Inst Biomed & Life Sci Plant Sci Grp Glasgow G12 8QQ Lanark Scotland Univ Glasgow Dept Comp Sci Bioinformat Res Ctr Glasgow G12 8QQ Lanark Scotland Univ Glasgow Inst Biomed & Life Sci Sir Henry Wellcome Funct Genom Facil Glasgow G12 8QQ Lanark Scotland

Background: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e. g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression. We applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph. Results: We validated the Graph-based iterative Group Analysis (GiGA) by applying it to the classic yeast diauxic shift experiment of DeRisi et al., using GeneOntology and metabolic network information. GiGA reliably identified and summarized all the biological processes discussed in the original publication. Visualization of the detected subgraphs allowed the convenient exploration of the results. The method also identified several processes that were not presented in the original paper but are of obvious relevance to the yeast starvation response. Conclusions: GiGA provides a fast and flexible delimitation of the most interesting areas in a microarray experiment, and leads to a considerable speed-up and improvement of the interpretation process.

关键词： microarray experiment Glyoxylate Cycle Gene Expression experiment Evidence Graph Significant Expression Change

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Quality determination and the repair of poor quality spots in array experiments

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BMC BIOINFORMATICS 2005年第1期6卷 1-12页

作者： Tom, BDM Gilks, WR Brooke-Powell, ET Ajioka, JW Inst Publ Hlth MRC Biostat Unit Cambridge CB2 2SR England Univ Cambridge Dept Pathol Cambridge CB2 1QP England Washington Univ Sch Med Dept Mol Microbiol St Louis MO 63110 USA

Background: A common feature of microarray experiments is the occurence of missing gene expression data. These missing values occur for a variety of reasons, in particular, because of the filtering of poor quality spots and the removal of undefined values when a logarithmic transformation is applied to negative background-corrected intensities. The efficiency and power of an analysis performed can be substantially reduced by having an incomplete matrix of gene intensities. Additionally, most statistical methods require a complete intensity matrix. Furthermore, biases may be introduced into analyses through missing information on some genes. Thus methods for appropriately replacing ( imputing) missing data and/or weighting poor quality spots are required. Results: We present a likelihood-based method for imputing missing data or weighting poor quality spots that requires a number of biological or technical replicates. This likelihood-based approach assumes that the data for a given spot arising from each channel of a two-dye ( two-channel) cDNA microarray comparison experiment independently come from a three-component mixture distribution - the parameters of which are estimated through use of a constrained E-M algorithm. Posterior probabilities of belonging to each component of the mixture distributions are calculated and used to decide whether imputation is required. These posterior probabilities may also be used to construct quality weights that can down-weight poor quality spots in any analysis performed afterwards. The approach is illustrated using data obtained from an experiment to observe gene expression changes with 24 hr paclitaxel (Taxol(R)) treatment on a human cervical cancer derived cell line ( HeLa). Conclusion: As the quality of microarray experiments affect downstream processes, it is important to have a reliable and automatic method of identifying poor quality spots and arrays. We propose a method of identifying poor quality spots, and suggest a meth

关键词： Root Mean Square Error Singular Value Decomposition microarray experiment Spot Intensity Poor Quality Data

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Identification of gene expression patterns using planned linear contrasts

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BMC BIOINFORMATICS 2006年第1期7卷 1-11页

作者： Li, Hao Wood, Constance L. Liu, Yushu Getchell, Thomas V. Getchell, Marilyn L. Stromberg, Arnold J. Univ Kentucky Dept Stat Lexington KY 40536 USA Coll Med Dept Physiol Lexington KY 40536 USA Coll Med Dept Anat & Neurobiol Lexington KY 40536 USA Univ Kentucky Med Ctr Sanders Brown Ctr Aging 309 Lexington KY 40536 USA

Background: In gene networks, the timing of significant changes in the expression level of each gene may be the most critical information in time course expression profiles. With the same timing of the initial change, genes which share similar patterns of expression for any number of sampling intervals from the beginning should be considered co-expressed at certain level(s) in the gene networks. In addition, multiple testing problems are complicated in experiments with multi-level treatments when thousands of genes are involved. Results: To address these issues, we first performed an ANOVA F test to identify significantly regulated genes. The Benjamini and Hochberg (BH) procedure of controlling false discovery rate (FDR) at 5% was applied to the P values of the F test. We then categorized the genes with a significant F test into 4 classes based on the timing of their initial responses by sequentially testing a complete set of orthogonal contrasts, the reverse Helmert series. For genes within each class, specific sequences of contrasts were performed to characterize their general 'fluctuation' shapes of expression along the subsequent sampling time points. To be consistent with the BH procedure, each contrast was examined using a stepwise Studentized Maximum Modulus test to control the gene based maximum family-wise error rate (MFWER) at the level of a new determined by the BH procedure. We demonstrated our method on the analysis of microarray data from murine olfactory sensory epithelia at five different time points after target ablation. Conclusion: In this manuscript, we used planned linear contrasts to analyze time-course microarray experiments. This analysis allowed us to characterize gene expression patterns based on the temporal order in the data, the timing of a gene's initial response, and the general shapes of gene expression patterns along the subsequent sampling time points. Our method is particularly suitable for analysis of microarray experiments in whi

关键词： False Discovery Rate microarray experiment Olfactory Epithelium Olfactory Sensory Neuron Fluctuation Pattern

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SYMBIOmatics: Synergies in Medical Informatics and Bioinformatics - exploring current scientific literature for emerging topics

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BMC BIOINFORMATICS 2007年第1期8卷 1-9页

作者： Rebholz-Schuhman, Dietrich Cameron, Graham Clark, Dominic van Mulligen, Erik Coatrieux, Jean-Louis Del Hoyo Barbolla, Eva Martin-Sanchez, Fernando Milanesi, Luciano Porro, Ivan Beltrame, Francesco Tollis, Ioannis Van der Lei, Johan EMBL European Bioinformat Inst Cambridge CB10 1SD England Erasmus Univ Med Ctr NL-3000 DR Rotterdam Netherlands INSERM Rennes France Minist Educ & Sci Madrid Spain Inst Hlth Carlos III Majadahonda Madrid Spain CNR Inst Biomed Technol ITB I-20090 Segrate MI Italy CNR I-00185 Rome Italy Fdn Res & Technol Hellas FORTH Inst Comp Sci GR-71110 Iraklion Greece

Background: The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to http://***). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. Results: This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000-2005("recent") and 1990-1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. Conclusion: We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science.

关键词： Support Vector Machine Gene Ontology microarray experiment Noun Phrase Text Mining

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CATMA, a comprehensive genome-scale resource for silencing and transcript profiling of Arabidopsis genes

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BMC BIOINFORMATICS 2007年第1期8卷 1-13页

作者： Sclep, Gert Allemeersch, Joke Liechti, Robin De Meyer, Bjoern Beynon, Jim Bhalerao, Rishikesh Moreau, Yves Nietfeld, Wilfried Renou, Jean-Pierre Reymond, Philippe Kuiper, Martin T. R. Hilson, Pierre Dept Plant Syst Biol B-9052 Ghent Belgium Univ Ghent Dept Mol Genet B-9052 Ghent Belgium Katholieke Univ Leuven ESAT SISTA B-3001 Heverlee Belgium Swiss Inst Bioinformat CH-1015 Lausanne Switzerland Hort Res Int Dept Plant Genet & Biol Warwick CV35 9EF England SLU Dept Forest Genet & Plant Physiol SE-90183 Umea Sweden Max Planck Inst Mol Genet Dept Vertebrate Genom D-14195 Berlin Germany INRA Unit Rech Genom Vegetale F-91057 Evry France Univ Lausanne Dept Plant Mol Biol CH-1015 Lausanne Switzerland Parco Technol Padano I-26900 Lodi Italy Gasthuisberg Onderwijs Navorsing 1 B-3000 Louvain Belgium

Background: The Complete Arabidopsis Transcript microarray (CATMA) initiative combines the efforts of laboratories in eight European countries [ 1] to deliver gene-specific sequence tags (GSTs) for the Arabidopsis research community. The CATMA initiative offers the power and flexibility to regularly update the GST collection according to evolving knowledge about the gene repertoire. These GST amplicons can easily be reamplified and shared, subsets can be picked at will to print dedicated arrays, and the GSTs can be cloned and used for other functional studies. This ongoing initiative has already produced approximately 24,000 GSTs that have been made publicly available for spotted microarray printing and RNA interference. Results: GSTs from the CATMA version 2 repertoire (CATMAv2, created in 2002) were mapped onto the gene models from two independent Arabidopsis nuclear genome annotation efforts, TIGR5 and PSB-EuGene, to consolidate a list of genes that were targeted by previously designed CATMA tags. A total of 9,027 gene models were not tagged by any amplified CATMAv2 GST, and 2,533 amplified GSTs were no longer predicted to tag an updated gene model. To validate the efficacy of GST mapping criteria and design rules, the predicted and experimentally observed hybridization characteristics associated to GST features were correlated in transcript profiling datasets obtained with the CATMAv2 microarray, confirming the reliability of this platform. To complete the CATMA repertoire, all 9,027 gene models for which no GST had yet been designed were processed with an adjusted version of the Specific Primer and Amplicon Design Software (SPADS). A total of 5,756 novel GSTs were designed and amplified by PCR from genomic DNA. Together with the pre-existing GST collection, this new addition constitutes the CATMAv3 repertoire. It comprises 30,343 unique amplified sequences that tag 24,202 and 23,009 protein-encoding nuclear gene models in the TAIR6 and EuGene genome annotations

关键词： microarray experiment Percentage Sequence Identity Arabidopsis Genome Sequence Spotted microarrays Design Round

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MIMAS: an innovative tool for network-based high density oligonucleotide microarray data management and annotation

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BMC BIOINFORMATICS 2006年第1期7卷 1-10页

作者： Hermida, L Schaad, O Demougin, P Descombes, P Primig, M Univ Basel Biozentrum CH-4056 Basel Switzerland Swiss Inst Bioinformat CH-4056 Basel Switzerland Univ Geneva Med Ctr NCCR Frontier Genet CH-1211 Geneva Switzerland

Background: The high-density oligonucleotide microarray (GeneChip) is an important tool for molecular biological research aiming at large-scale detection of small nucleotide polymorphisms in DNA and genome-wide analysis of mRNA concentrations. Local array data management solutions are instrumental for efficient processing of the results and for subsequent uploading of data and annotations to a global certified data repository at the EBI (ArrayExpress) or the NCBI (GeneOmnibus). Description: To facilitate and accelerate annotation of high-throughput expression profiling experiments, the microarray Information Management and Annotation System (MIMAS) was developed. The system is fully compliant with the Minimal Information About a microarray experiment (MIAME) convention. MIMAS provides life scientists with a highly flexible and focused GeneChip data storage and annotation platform essential for subsequent analysis and interpretation of experimental results with clustering and mining tools. The system software can be downloaded for academic use upon request. Conclusion: MIMAS implements a novel concept for nation-wide GeneChip data management whereby a network of facilities is centered on one data node directly connected to the European certified public microarray data repository located at the EBI. The solution proposed may serve as a prototype approach to array data management between research institutes organized in a consortium.

关键词： microarray experiment Application Programmer Interface Control Vocabulary Free Text Field GeneChip Data

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Two-part permutation tests for DNA methylation and microarray data -: art. no. 35

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BMC BIOINFORMATICS 2005年第1期6卷 1-7页

作者： Neuhäuser, M Boes, T Jöckel, KH Univ Duisburg Essen Inst Med Informat Biometry & Epidemiol D-45122 Essen Germany

Background: One important application of microarray experiments is to identify differentially expressed genes. Often, small and negative expression levels were clipped-off to be equal to an arbitrarily chosen cutoff value before a statistical test is carried out. Then, there are two types of data: truncated values and original observations. The truncated values are not just another point on the continuum of possible values and, therefore, it is appropriate to combine two statistical tests in a two-part model rather than using standard statistical methods. A similar situation occurs when DNA methylation data are investigated. In that case, there are null values (undetectable methylation) and observed positive values. For these data, we propose a two-part permutation test. Results: The proposed permutation test leads to smaller p-values in comparison to the original two-part test. We found this for both DNA methylation data and microarray data. With a simulation study we confirmed this result and could show that the two-part permutation test is, on average, more powerful. The new test also reduces, without any loss of power, to a standard test when there are no null or truncated values. Conclusion: The two-part permutation test can be used in routine analyses since it reduces to a standard test when there are positive values only. Further advantages of the new test are that it opens the possibility to use other test statistics to construct the two-part test and that it avoids the use of any asymptotic distribution. The latter advantage is particularly important for the analysis of microarrays since sample sizes are usually small.

关键词： Permutation Test microarray experiment Asymptotic Distribution Uveal Melanoma Methylation Data

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A weighted sample size for microarray datasets that considers the variability of variance and multiplicity

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JOURNAL OF BIOSCIENCE AND BIOENGINEERING 2009年第3期108卷 252-258页

作者： Kim, Ki-Yeol Chung, Hyun Cheol Rha, Sun Young Yonsei Univ Coll Med Dept Internal Med Seoul 120752 South Korea Yonsei Univ Coll Dent Oral Canc Res Inst Seoul 120752 South Korea Yonsei Univ Coll Med Brain Korea Project Med Sci 21 Seoul 120752 South Korea Yonsei Univ Coll Med Canc Metastasis Res Ctr Seoul 120752 South Korea

microarray experiments are often performed to detect differently expressed genes among different clinical phenotypes. The method used to calculate the appropriate sample size for this purpose differs from the sample size calculation used for general clinical experiments, because microarrays include tens of thousands of genes. We proposed a sample size calculation method that considers variance among an entire gene set and used the Bonferroni correction to address the multiplicity problem. Specifically, by adjusting for the multiplicity problem, the existing equation for sample size calculation was modified based on the Bonferroni correction. By k-means cluster analysis, the variances across all genes can be divided into several groups with similar values, and the sample sizes for each group were subsequently calculated and weight-averaged. The results of this study show that the sample size was related to the number of genes on a chip. The weighted sample size, calculated by the proposed method, preserved the Type I error for selection of significant genes within a microarray data set. (C) 2009, The Society for Biotechnology, Japan. All rights reserved.

关键词： microarray experiment Weighted sample size Variability of variance Multiplicity problem Bonferroni correction

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PageMan:: An interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments

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BMC BIOINFORMATICS 2006年第1期7卷 1-8页

作者： Usadel, Bjoern Nagel, Axel Steinhauser, Dirk Gibon, Yves Blaesing, Oliver E. Redestig, Henning Sreenivasulu, Nese Krall, Leonard Hannah, Matthew A. Poree, Fabien Fernie, Alisdair R. Stitt, Mark Max Planck Inst Mol Physiol D-14476 Potsdam Germany Leibniz Inst Plant Genet & Crop Plant Res IPK D-06466 Gatersleben Germany

Background: microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. Results: Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs. PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis. PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is

关键词： microarray experiment False Discovery Rate Level Functional Ontology Ontological Group Graphical User Interface Tool

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Optimized LOWESS normalization parameter selection for DNA microarray data -: art. no. 194

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BMC BIOINFORMATICS 2004年第1期5卷 1-13页

作者： Berger, JA Hautaniemi, S Järvinen, AK Edgren, H Mitra, SK Astola, J Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Tampere Univ Technol Inst Signal Proc FIN-33101 Tampere Finland Univ Helsinki Biomedicum Biochip Ctr FIN-00014 Helsinki Finland VTT Tech Res Ctr Finland Med Biotechnol Grp Turku 20521 Finland Univ Turku Turku 20521 Finland

Background: microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail. Results and discussion: In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that represents the mean-squared difference between the LOWESS estimates and the normalization reference level. We demonstrate the utility of the systematic parameter selection using two publicly available data sets. The first data set consists of three self versus self hybridizations, which allow for a quantitative study of the optimization method. The second data set contains a collection of DNA microarray data from a breast cancer study utilizing four breast cancer cell lines. Our results show that different parameter choices for the bandwidth window yield dramatically different calibration results in both studies. Conclusions: Results derived from the self versus self experiment indicate that the proposed optimization approach is a plausible solution for estimating the LOWESS parameters, while results from the breast cancer experiment show that the optimization procedure is readily applicable to real-life microarray data normalization. In summary, the systematic approach to obtain critica

关键词： microarray experiment Bandwidth Parameter Arbitrary Selection LOWESS Normalization Comparative Genomic Hybridization microarray

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