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An evolutionary rough partitive clustering

PATTERN RECOGNITION LETTERS 2004年第12期25卷 1439-1449页

作者： Mitra, S Indian Stat Inst Machine Intelligence Unit Kolkata 700108 W Bengal India

An evolutionary rough c-means clustering algorithm is proposed. Genetic algorithms are employed to tune the threshold, and relative importance of upper and lower approximations of the rough sets modeling the clusters. The Davies-Bouldin clustering validity index is used as the fitness function, that is minimized while arriving at an optimal partitioning. A comparative study of its performance is made with related partitive algorithms. The effectiveness of the algorithm is demonstrated on real and synthetic datasets, including microarray gene expression data from Bioinformatics. (C) 2004 Elsevier B.V. All rights reserved.

关键词： partitive clustering genetic algorithms rough sets clustering validity index gene expression microarray data

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Characterization of Antirrhinum petal development and identification of target genes of the class B MADS box gene DEFICIENS

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PLANT CELL 2004年第12期16卷 3197-3215页

作者： Bey, M Stüber, K Fellenberg, K Schwarz-Sommera, Z Sommer, H Saedler, H Zachgo, S Max Planck Inst Plant Breeding Res Dept Mol Plant Genet D-50829 Cologne Germany German Canc Res Ctr Dept Funct Genome Anal D-69009 Heidelberg Germany

The class B MADS box transcription factors DEFICIENS (DEF) and GLOBOSA (GLO) of Antirrhinum majus together control the organogenesis of petals and stamens. Toward an understanding of how the downstream molecular mechanisms controlled by DEF contribute to petal organogenesis, we conducted expression profiling experiments using macroarrays comprising >11,600 annotated Antirrhinum unigenes. First, four late petal developmental stages were compared with sepals. More than 500 ESTs were identified that comprise a large number of stage-specifically regulated genes and reveal a highly dynamic transcriptional regulation. For identification of DEF target genes that might be directly controlled by DEF, we took advantage of the temperature-sensitive def-101 mutant. To enhance the sensitivity of the profiling experiments, one petal developmental stage was selected, characterized by increased transcriptome changes that reflect the onset of cell elongation processes replacing cell division processes. Upon reduction of the DEF function, 49 upregulated and 52 downregulated petal target genes were recovered. Eight target genes were further characterized in detail by RT-PCR and in situ studies. Expression of genes responding rapidly toward an altered DEF activity is confined to different petal tissues, demonstrating the complexity of the DEF function regulating diverse basic processes throughout petal morphogenesis.

关键词： LIPID-TRANSFER PROTEIN FLORAL ORGAN IDENTITY GENOME-WIDE ANALYSIS CELL-WALL PROTEIN ARABIDOPSIS-THALIANA HOMEOTIC GENE BETA-GLUCOSIDASE MOLECULARCHARACTERIZATION GERBERA-HYBRIDA microarray data

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Transcriptional programs of early reproductive stages in Arabidopsis

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PLANT PHYSIOLOGY 2004年第3期135卷 1765-1775页

作者： Hennig, L Gruissem, W Grossniklaus, U Köhler, C ETH Ctr Swiss Fed Inst Technol CH-8092 Zurich Switzerland ETH Ctr Inst Plant Sci CH-8092 Zurich Switzerland ETH Ctr Zurich Basel Plant Sci Ctr CH-8092 Zurich Switzerland ETH Ctr Funct Genom Ctr Zurich CH-8092 Zurich Switzerland Univ Zurich Inst Plant Biol CH-8008 Zurich Switzerland Univ Zurich Zurich Basel Plant Sci Ctr CH-8008 Zurich Switzerland

The life cycle of flowering plants alternates between a diploid sporophytic and a haploid gametophytic generation. After fertilization of each the egg and central cells by one male gamete, the development of both fertilization products occurs coordinated with the maternally derived seed coat and carpel tissues forming the fruit. The reproduction program is likely to involve the concerted activity of many genes. To identify genes with specific functions during reproduction, we have analyzed the expression profile of more than 22,000 genes present on the Arabidopsis ATH1 microarray during three stages of flower and fruit development. We found 1,886 genes regulated during reproductive development and 1,043 genes that were specifically expressed during reproduction. When compared to cells from an Arabidopsis suspension culture, S-phase genes were underrepresented and G2 and M-phase genes were strongly enriched in the set of specific genes, indicating that important functions during reproduction are exerted in the G2 and M phases of the cell cycle. Many potential signaling components, such as receptor-like protein kinases, phosphatases, and transcription factors, were present in both groups of genes. Members of the YABBY, MADS box, and Myb transcription factor families were significantly overrepresented in the group of specific genes, revealing an important role of these families during reproduction. Furthermore, we found a significant enrichment of predicted secreted proteins smaller than 15 kD that could function directly as signaling molecules or as precursors for peptide hormones. Our study provides a basis for targeted reverse-genetic approaches aimed to identify key genes of reproductive development in plants.

关键词： Mads-box genes Shoot apical meristem Genome-wide analysis Female gametophyte Saccharomyces-cerevisiae Flavonoid biosynthesis Pollen transcriptome Seeddevelopment microarray data Energy status

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A semiparametric density estimation approach to pattern classification

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PATTERN RECOGNITION 2004年第3期37卷 409-419页

作者： Hoti, F Holmström, L Univ Helsinki Rolf Nevanlinna Inst FIN-00014 Helsinki Finland

A new multivariate density estimator suitable for pattern classifier design is proposed. The data are first transformed so that the pattern vector components with the most non-Gaussian structure are separated from the Gaussian components. Nonparametric density estimation is then used to capture the non-Gaussian structure of the data while parametric Gaussian conditional density estimation is applied to the rest of the components. Both simulated and real data sets are used to demonstrate the potential usefulness of the proposed approach. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： semiparametric density estimation kernel estimation classification handwritten digit data satellite data microarray data

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Faster cyclic loess: normalizing RNA arrays via linear models

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BIOINFORMATICS 2004年第16期20卷 2778-2786页

作者： Ballman, KV Grill, DE Oberg, AL Therneau, TM Mayo Clin & Mayo Fdn Div Biostat Coll Med Rochester MN 55905 USA

Motivation: Our goal was to develop a normalization technique that yields results similar to cyclic loess normalization and with speed comparable to quantile normalization. Results: Fastlo yields normalized values similar to cyclic loess and quantile normalization and is fast;it is at least an order of magnitude faster than cyclic loess and approaches the speed of quantile normalization. Furthermore, fastlo is more versatile than both cyclic loess and quantile normalization because it is model-based.

关键词： microarray data

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Interactive and dynamic graphics in statistical consulting

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JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2004年第4期9卷 402-431页

作者： Henderson, HV AgResearch Ruakura Res Ctr Hamilton New Zealand

Twelve years ago, Unwin wrote an article titled "How Interactive Graphics Will Revolutionize Statistical Practice." Well, has it? I think it has. At that time data Desk was the prime commercially available package for interactive statistical graphics. Since then interactive and dynamic graphics have become increasingly available in many statistical and visualization packages, and some are even available, or easy to set up, in general spread-sheets like Excel. Examples of using interactive and dynamic graphics in ways that are useful in statistical consulting will be given. We'll gain insight into some old data and into some new data, including microarray data.

关键词： brushing data analysis data visualization exploratory data analysis interactive graphics linked scatterplots microarray data slicin statistical graphics

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A module map showing conditional activity of expression modules in cancer

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NATURE GENETICS 2004年第10期36卷 1090-1098页

作者： Segal, E Friedman, N Koller, D Regev, A Stanford Univ Dept Comp Sci Stanford CA 94305 USA Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-91904 Jerusalem Israel Harvard Univ Bauer Ctr Genom Res Cambridge MA 02138 USA

DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor;for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.

关键词： GENE-EXPRESSION HEPATOCELLULAR-CARCINOMA DOWN-REGULATION microarray data METASTASIS KINASE TUMOR BONE ORGANIZATION PROGRESSION

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GO-Mapper: functional analysis of gene expression data using the expression level as a score to evaluate Gene Ontology terms

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BIOINFORMATICS 2004年第16期20卷 2618-2625页

作者： Smid, M Dorssers, LCJ Erasmus MC Josephine Nefkens Inst Dept Pathol NL-3000 DR Rotterdam Netherlands

Motivation: Retrieval of information on biological processes from large-scale expression data is still a time-consuming task. An automated analysis utilizing all expression information would greatly increase our understanding of the samples under study. Results: We describe here a novel method to obtain a functional analysis of complex gene expression data. Instead of applying a predefined expression threshold, Gene Ontology (GO) terms are weighted using the actual measured levels of expression of all associated genes. Based on this concept, the application GO-Mapper was developed to quantitatively link gene expression levels to GO-terms for multiple experiments in an automated way. The applicability of GO-Mapper was developed and validated on in house and public human microarray data and mouse SAGE data. We demonstrate that the GO-Mapper allows for interrelating relevant biological functions with the experiments under study.

关键词： SERIAL ANALYSIS microarray data RESOURCE GENOME TOOL

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Sequence-matched probes produce increased cross-platform consistency and more reproducible biological results in microarray-based gene expression measurements

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NUCLEIC ACIDS RESEARCH 2004年第9期32卷 e74-e74页

作者： Mecham, BH Klus, GT Strovel, J Augustus, M Byrne, D Bozso, P Wetmore, DZ Mariani, TJ Kohane, IS Szallasi, Z Harvard Univ Sch Med Childrens Hosp Informat Program Boston MA 02215 USA Brigham & Womens Hosp Div Pulm & Crit Care Med Dept Med & Pulm Bioinformat Lung Biol Ctr Boston MA 02115 USA Uniformed Serv Univ Hlth Sci Dept Pharmacol Bethesda MD 20814 USA Avalon Pharmaceut Germantown MD 20876 USA

Cancer derived microarray data sets are routinely produced by various platforms that are either commercially available or manufactured by academic groups. The fundamental difference in their probe selection strategies holds the promise that identical observations produced by more than one platform prove to be more robust when validated by biology. However, cross-platform comparison requires matching corresponding probe sets. We are introducing here sequence-based matching of probes instead of gene identifier-based matching. We analyzed breast cancer cell line derived RNA aliquots using Agilent cDNA and Affymetrix oligonucleotide microarray platforms to assess the advantage of this method. We show, that at different levels of the analysis, including gene expression ratios and difference calls, cross-platform consistency is significantly improved by sequence- based matching. We also present evidence that sequence-based probe matching produces more consistent results when comparing similar biological data sets obtained by different microarray platforms. This strategy allowed a more efficient transfer of classification of breast cancer samples between data sets produced by cDNA microarray and Affymetrix gene-chip platforms.

关键词： .difference probe sets platforms microarray data matching cross-platform

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Gene expression profiling for prognosis using Cox regression

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STATISTICS IN MEDICINE 2004年第11期23卷 1767-1780页

作者： Pawitan, Y Bjöhle, J Wedren, S Humphreys, K Skoog, L Huang, F Amler, L Shaw, P Hall, P Bergh, J Karolinska Inst Dept Med Epidemiol & Biostat S-10401 Stockholm Sweden Karolinska Hosp S-10401 Stockholm Sweden Genentech Inc San Francisco CA 94080 USA

Given the promise of rich biological information in microarray data we will expect an increasing demand for a robust, practical and well-tested methodology to provide patient prognosis based on gene expression data. In standard settings, with few clinical predictors, such a methodology has been provided by the Cox proportional hazard model, but no corresponding methodology is available to deal with the full set of genes in microarray data. Furthermore, we want the procedure to be able to deal with the general survival data that include censored information. Conceptually such a procedure can be constructed quite easily, but its implementation will never be straightforward due to computational problems. We have developed an approach that relies on an extension of the Cox proportional likelihood that allows random effects parameters. In this approach, we use the full set of genes in the analysis and deal with survival data in the most general way. We describe the development of the model and the steps in the implementation, including a fast computational formula based on a subsampling of the risk set and the singular value decomposition. Finally, we illustrate the methodology using a data set obtained from a cohort of breast cancer patients. Copyright (C) 2004 John Wiley Sons, Ltd.

关键词： AIC GLMM hazard likelihood microarray data mixed models Poisson regression random effects survival analysis

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