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Error tolerant model for incorporating biological knowledge with expression data in estimating gene networks

Statistical Methodology 2006年第1期3卷 1-16页

作者： Imoto, Seiya Higuchi, Tomoyuki Goto, Takao Miyano, Satoru Human Genome Center Institute of Medical Science University of Tokyo Minato-ku Tokyo 108-8639 4-6-1 Shirokanedai Japan Institute of Statistical Mathematics Minato-ku Tokyo 106-8569 4-6-7 Minami-Azabu Japan

We propose a novel statistical method for estimating gene networks based on microarray gene expression data together with information from biological knowledge databases. Although a large amount of gene regulation information has already been stored in some biological databases, there are still errors and missing facts due to experimental problems and human errors. Therefore, we cannot blindly use them for understanding gene regulation and a robust procedure with a statistical model for using such database information is required. By using gene expression data, we provide a probabilistic framework of a joint learning model for repairing database information and for estimating a gene network based on dynamic Bayesian networks, simultaneously. To show the effectiveness of the proposed method, we analyze Saccharomyces cerevisiae cell-cycle gene expression data together with KEGG information. © 2005 Elsevier B.V. All rights reserved.

关键词： Bayesian network Biological knowledge Error tolerant model Gene network microarray data

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A supervised data-driven approach for microarray spot quality classification

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PATTERN ANALYSIS AND APPLICATIONS 2005年第1-2期8卷 181-187页

作者： Bicego, M Martinez, MD Murino, V Univ Sassari DEIR I-07100 Sassari Italy Univ Verona DI I-37135 Verona Italy

In this paper, the problem of classifying the quality of microarray data spots is addressed, using concepts derived from the supervised learning theory. The proposed method, after extracting spots from the microarray image, computes several features, which take into account shape, color and variability. The features are classified using support vector machines, a recent statistical classification technique that is being employed widely. The proposed method does not make any assumptions on the problem and does not require any a priori information. The proposed system has been tested in a real case, for several different parameters' configurations. Experimental results show the effectiveness of the proposed approach, also in comparison with state-of-the-art methods.

关键词： microarray data quality classification support vector machines pattern recognition

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GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data

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NUCLEIC ACIDS RESEARCH 2005年第Web Server issue期33卷 W616-W620页

作者： Vaquerizas, JM Conde, L Yankilevich, P Cabezón, A Minguez, P Díaz-Uriarte, R Al-Shahrour, F Herrero, J Dopazo, J CNIO Bioinformat Unit Madrid 28029 Spain EMBL EBI Ensembl Team Cambridge England Ctr Invest Principe Felipe INB Valencia 46013 Spain

The Gene Expression Profile Analysis Suite, GEPAS, has been running for more than three years. With > 76000 experiments analysed during the last year and a daily average of almost 300 analyses, GEPAS can be considered a well-established and widely used platform for gene expression microarray data analysis. GEPAS is oriented to the analysis of whole series of experiments. Its design and development have been driven by the demands of the biomedical community, probably the most active collective in the field of microarray users. Although clustering methods have obviously been implemented in GEPAS, our interest has focused more on methods for finding genes differentially expressed among distinct classes of experiments or correlated to diverse clinical outcomes, as well as on building predictors. There is also a great interest in CGH-arrays which fostered the development of the corresponding tool in GEPAS: InSilicoCGH. Much effort has been invested in GEPAS for developing and implementing efficient methods for functional annotation of experiments in the proper statistical framework. Thus, the popular FatiGO has expanded to a suite of programs for functional annotation of experiments, including information on transcription factor binding sites, chromosomal location and tissues. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://***.

关键词： .functional genomics microarray data last year daily average Predictors GEPAS microarray gene expression az-Uriarte Al-Shahrour CGH-arrays

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Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect

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BMC BIOINFORMATICS 2003年第1期4卷 13-13页

作者： O'Neill, MC Song, L Univ Maryland Dept Sci Biol Baltimore MD 21250 USA

Background: microarray chips are being rapidly deployed as a major tool in genomic research. To date most of the analysis of the enormous amount of information provided on these chips has relied on clustering techniques and other standard statistical procedures. These methods, particularly with regard to cancer patient prognosis, have generally been inadequate in providing the reduced gene subsets required for perfect classification. Results: Networks trained on microarray data from DLBCL lymphoma patients have, for the first time, been able to predict the long-term survival of individual patients with 100% accuracy. Other networks were able to distinguish DLBCL lymphoma donors from other donors, including donors with other lymphomas, with 99% accuracy. Differentiating the trained network can narrow the gene profile to less than three dozen genes for each classification. Conclusions: Here we show that artificial neural networks are a superior tool for digesting microarray data both with regard to making distinctions based on the data and with regard to providing very specific reference as to which genes were most important in making the correct distinction in each case.

关键词： diffuse large B-cell lymphoma microarray data neural networks prognosis diagnosis

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A biological question and a balanced (orthogonal) design: the ingredients to efficiently analyze two-color microarrays with Confirmatory Factor Analysis

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BMC GENOMICS 2006年第1期7卷 232-232页

作者： Crijns, Anne P. G. Gerbens, Frans Plantinga, A. Edo D. Meersma, Gert Jan de Jong, Steven Hofstra, Robert M. W. de Vries, Elisabeth G. E. van der Zee, Ate G. J. de Bock, Geertruida H. Meerman, Gerard J. te Univ Groningen Med Ctr Dept Med Genet NL-9700 RB Groningen Netherlands Univ Groningen Med Ctr Dept Gynecol Oncol NL-9700 RB Groningen Netherlands Univ Groningen Med Ctr Dept Med Oncol NL-9700 RB Groningen Netherlands Univ Groningen Med Ctr Dept Epidemiol & Stat NL-9700 RB Groningen Netherlands

Background: Factor analysis ( FA) has been widely applied in microarray studies as a data-reduction-tool without any a-priori assumption regarding associations between observed data and latent structure ( Exploratory Factor Analysis). A disadvantage is that the representation of data in a reduced set of dimensions can be difficult to interpret, as biological contrasts do not necessarily coincide with single dimensions. However, FA can also be applied as an instrument to confirm what is expected on the basis of pre-established hypotheses ( Confirmatory Factor Analysis, CFA). We show that with a hypothesis incorporated in a balanced (orthogonal) design, including 'SelfSelf' hybridizations, dye swaps and independent replications, FA can be used to identify the latent factors underlying the correlation structure among the observed two-color microarray data. An orthogonal design will reflect the principal components associated with each experimental factor. We applied CFA to a microarray study performed to investigate cisplatin resistance in four ovarian cancer cell lines, which only differ in their degree of cisplatin resistance. Results: Two latent factors, coinciding with principal components, representing the differences in cisplatin resistance between the four ovarian cancer cell lines were easily identified. From these two factors 315 genes associated with cisplatin resistance were selected, 199 genes from the first factor ( False Discovery Rate (FDR): 19%) and 152 ( FDR: 24%) from the second factor, while both gene sets shared 36. The differential expression of 16 genes was validated with reverse transcription-polymerase chain reaction. Conclusion: Our results show that FA is an efficient method to analyze two-color microarray data provided that there is a pre-defined hypothesis reflected in an orthogonal design.

关键词： Gene Ontology microarray data Singular Value Decomposition Latent Factor Ovarian Cancer Cell Line

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Storing, linking, and mining microarray databases using SRS

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BMC BIOINFORMATICS 2005年第1期6卷 192-192页

作者： Veldhoven, A de Lange, D Smid, M de Jager, V Kors, JA Jenster, G Erasmus MC Josephine Nefkens Inst Dept Urol NL-3000 DR Rotterdam Netherlands

Background: SRS (Sequence Retrieval System) has proven to be a valuable platform for storing, linking, and querying biological databases. Due to the availability of a broad range of different scientific databases in SRS, it has become a useful platform to incorporate and mine microarray data to facilitate the analyses of biological questions and non-hypothesis driven quests. Here we report various solutions and tools for integrating and mining annotated expression data in SRS. Results: We devised an Auto-Upload Tool by which microarray data can be automatically imported into SRS. The dataset can be linked to other databases and user access can be set. The linkage comprehensiveness of microarray platforms to other platforms and biological databases was examined in a network of scientific databases. The stored microarray data can also be made accessible to external programs for further processing. For example, we built an interface to a program called Venn Mapper, which collects its microarray data from SRS, processes the data by creating Venn diagrams, and saves the data for interpretation. Conclusion: SRS is a useful database system to store, link and query various scientific datasets, including microarray data. The user-friendly Auto-Upload Tool makes SRS accessible to biologists for linking and mining user-owned databases.

关键词： microarray data microarray dataset Scientific database Biological database microarray database

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Kernel hierarchical gene clustering from microarray expression data

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BIOINFORMATICS 2003年第16期19卷 2097-2104页

作者： Qin, J Lewis, DP Noble, WS Columbia Univ Columbia Genome Ctr New York NY 10032 USA Columbia Univ Dept Comp Sci New York NY 10027 USA Univ Washington Dept Genome Sci Seattle WA 98195 USA

Motivation: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchical clustering can be applied to expression profiles of genes across multiple experiments, identifying groups of genes that share similiar expression profiles. Previous work using the support vector machine supervised learning algorithm with microarray data suggests that higher-order features, such as pairwise and tertiary correlations across multiple experiments, may provide significant benefit in learning to recognize classes of co-expressed genes. Results: We describe a generalization of the hierarchical clustering algorithm that efficiently incorporates these higher-order features by using a kernel function to map the data into a high-dimensional feature space. We then evaluate the utility of the kernel hierarchical clustering algorithm using both internal and external validation. The experiments demonstrate that the kernel representation itself is insufficient to provide improved clustering performance. We conclude that mapping gene expression data into a high-dimensional feature space is only a good idea when combined with a learning algorithm, such as the support vector machine that does not suffer from the curse of dimensionality.

关键词： .pairwise microarray data feature space microarray expression kernel function hierarchical clustering algorithm

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Genome-wide analysis of gene expression profiles associated with cell cycle transitions in growing organs of Arabidopsis

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PLANT PHYSIOLOGY 2005年第2期138卷 734-743页

作者： Beemster, GTS De Veylder, L Vercruysse, S West, G Rombaut, D Van Hummelen, P Galichet, A Gruissem, W Inzé, D Vuylsteke, M State Univ Ghent VIB Dept Plant Syst Biol B-9052 Ghent Belgium Flanders Interuniv Inst Biotechnol B-3000 Louvain Belgium Swiss Fed Inst Technol Swiss Fed Inst Plant Sci Inst Plant Sci CH-8092 Zurich Switzerland

Organ growth results from the progression of component cells through subsequent phases of proliferation and expansion before reaching maturity. We combined kinematic analysis, flowcytometry, and microarray analysis to characterize cell cycle regulation during the growth process of leaves 1 and 2 of Arabidopsis ( Arabidopsis thaliana). Kinematic analysis showed that the epidermis proliferates until day 12;thereafter, cells expand until day 19 when leaves reach maturity. Flowcytometry revealed that endoreduplication occurs from the time cell division rates decline until the end of cell expansion. Analysis of 10 time points with a 6k- cDNA microarray showed that transitions between the growth stages were closely reflected in the mRNA expression data. Subsequent genome- wide microarray analysis on the three main stages allowed us to categorize known cell cycle genes into three major classes: constitutively expressed, proliferative, and inhibitory. Comparison with published expression data obtained from root zones corresponding to similar developmental stages and from synchronized cell cultures supported this categorization and enabled us to identify a high confidence set of 131 proliferation genes. Most of those had an M phase- dependent expression pattern and, in addition to many known cell cycle- related genes, there were at least 90 that were unknown or previously not associated with proliferation.

关键词： DEPENDENT KINASE microarray data ZEA-MAYS GROWTH DIVISION ROOT ENDOREDUPLICATION THALIANA ENDOPOLYPLOIDY ENLARGEMENT

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RNA interference identifies a calcium-dependent protein kinase involved in Medicago truncatula root development

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PLANT CELL 2005年第11期17卷 2911-2921页

作者： Ivashuta, S Liu, JY Liu, JQ Lohar, DP Haridas, S Bucciarelli, B VandenBosch, KA Vance, CP Harrison, MJ Gantt, JS Univ Minnesota Dept Plant Biol St Paul MN 55108 USA Cornell Univ Boyce Thompson Inst Plant Res Ithaca NY 14853 USA Univ Minnesota Dept Agron & Plant Genet St Paul MN 55108 USA ARS Plant Sci Res Unit USDA St Paul MN 55108 USA

Changes in cellular or subcellular Ca2+ concentrations play essential roles in plant development and in the responses of plants to their environment. However, the mechanisms through which Ca2+ acts, the downstream signaling components, as well as the relationships among the various Ca2+-dependent processes remain largely unknown. Using an RNA interference-based screen for gene function in Medicago truncatula, we identified a gene that is involved in root development. Silencing Ca2+-dependent protein kinase1 (CDPK1), which is predicted to encode a Ca2+-dependent protein kinase, resulted in significantly reduced root hair and root cell lengths. Inactivation of CDPK1 is also associated with significant diminution of both rhizobial and mycorrhizal symbiotic colonization. Additionally, microarray analysis revealed that silencing CDPK1 alters cell wall and defense-related gene expression. We propose that M. truncatula CDPK1 is a key component of one or more signaling pathways that directly or indirectly modulates cell expansion or cell wall synthesis, possibly altering defense gene expression and symbiotic interactions.

关键词： ARBUSCULAR MYCORRHIZAL SYMBIOSIS RHIZOGENES-TRANSFORMED ROOTS ACTIN-DEPOLYMERIZING FACTOR PLANT DEFENSE RESPONSE ARABIDOPSIS-THALIANA CELL-WALL OXIDATIVEBURST microarray data GROWTH TRANSDUCTION

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Phytochrome control of the Arabidopsis transcriptome anticipates seedling exposure to light

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PLANT CELL 2005年第9期17卷 2507-2516页

作者： Mazzella, MA Arana, MV Staneloni, RJ Perelman, S Batiller, MJR Muschietti, J Cerdán, PD Chen, KH Sánchez, RA Zhu, T Chory, J Casal, JJ Univ Buenos Aires Fac Agron IFEVA Inst Invest Fisiol & Ecol Vinculadas Agr RA-1417 Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn RA-1417 Buenos Aires DF Argentina Inst Invest Ingn Genet & Biol Mol RA-1428 Buenos Aires DF Argentina Inst Invest Bioquim Fdn Leloir RA-1405 Buenos Aires DF Argentina Salk Inst Biol Studies Howard Hughes Med Inst La Jolla CA 92037 USA Salk Inst Biol Studies Plant Biol Lab La Jolla CA 92037 USA Syngenta Biotechnol Res Triangle Pk NC 27709 USA

Phytochromes mediate a profound developmental shift when dark-grown seedlings are exposed to light. Here, we show that a subset of genes is upregulated in phytochrome B ( phyB) mutants even before dark-grown Arabidopsis thaliana seedlings are exposed to light. Most of these genes bear the RY cis motif, which is a binding site of the transcription factor ABSCISIC ACID INSENSITIVE3 (ABI3), and the phyB mutation also enhances ABI3 expression. These changes in transcriptome have physiological consequences, because seedlings of the abi3 mutant showed enhanced responses to pulses of far-red light, whereas ABI3 overexpressers exhibited the opposite pattern. Seedlings of the wild type derived from seeds germinated in full darkness showed enhanced expression of genes bearing the RY cis motif and reduced responses to far-red light. We propose that, via changes in ABI3 expression, light, perceived mainly by phyB in the seed, generates a downstream transdevelopmental phase signal that preconditions the seedling to its most likely environment.

关键词： PHASEOLIN GENE ACTIVATION FALSE DISCOVERY RATE SIGNAL-TRANSDUCTION ABSCISIC-ACID microarray data BLUE-LIGHT EXPRESSION THALIANA PATTERNS MUTANTS

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