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Gene expression network analysis and applications to immunology

BIOINFORMATICS 2007年第7期23卷 850-858页

作者： Nacu, Serban Critchley-Thorne, Rebecca Lee, Peter Holmes, Susan Stanford Univ Dept Stat Stanford CA 94305 USA Stanford Univ Sch Med Stanford CA 94305 USA Ecole Normale Super F-75231 Paris France

We address the problem of using expression data and prior biological knowledge to identify differentially expressed pathways or groups of genes. Following an idea of Ideker et al. (2002), we construct a gene interaction network and search for high-scoring subnetworks. We make several improvements in terms of scoring functions and algorithms, resulting in higher speed and accuracy and easier biological interpretation. We also assign significance levels to our results, adjusted for multiple testing. Our methods are succesfully applied to three human microarray data sets, related to cancer and the immune system, retrieving several known and potential pathways. The method, denoted by the acronym GXNA (Gene eXpression Network Analysis) is implemented in software that is publicly available and can be used on virtually any microarray data set.

关键词： microarray data PATHWAYS PATTERNS

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Hierarchical tree snipping: clustering guided by prior knowledge

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BIOINFORMATICS 2007年第24期23卷 3335-3342页

作者： Dotan-Cohen, Dikla Melkman, Avraham A. Kasif, Simon Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel Childrens Hosp Dept Biomed Engn Boston MA 02115 USA Childrens Hosp Ctr Adv Genom Technol Boston MA 02115 USA Childrens Hosp Bioinformat Program Boston MA 02115 USA Childrens Hosp Harvard MIT Program Hlth Sci & Technol Boston MA 02115 USA

Motivation: Hierarchical clustering is widely used to cluster genes into groups based on their expression similarity. This method first constructs a tree. Next this tree is partitioned into subtrees by cutting all edges at some level, thereby inducing a clustering. Unfortunately, the resulting clusters often do not exhibit significant functional coherence. Results: To improve the biological significance of the clustering, we develop a new framework of partitioning by snipping-cutting selected edges at variable levels. The snipped edges are selected to induce clusters that are maximally consistent with partially available background knowledge such as functional classifications. Algorithms for two key applications are presented: functional prediction of genes, and discovery of functionally enriched clusters of co-expressed genes. Simulation results and cross-validation tests indicate that the algorithms perform well even when the actual number of clusters differs considerably from the requested number. Performance is improved compared with a previously proposed algorithm.

关键词： GENE-EXPRESSION data microarray data SACCHAROMYCES-CEREVISIAE DROSOPHILA-MELANOGASTER ANNOTATION NUMBER CYCLE

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A review of feature selection techniques in bioinformatics

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BIOINFORMATICS 2007年第19期23卷 2507-2517页

作者： Saeys, Yvan Inza, Inaki Larranaga, Pedro VIB Dept Plant Syst Biol B-9052 Ghent Belgium Univ Ghent Dept Mol Genet Bioinformat & Evolutionary Genom Grp B-9052 Ghent Belgium Univ Basque Country Fac Comp Sci Dept Comp Sci & Artificial Intelligence San Sebastian Spain

Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.

关键词： GENE-EXPRESSION data MASS-SPECTROMETRY data FALSE DISCOVERY RATE OVARIAN-CANCER IDENTIFICATION BAYESIAN VARIABLE SELECTION microarray data CLASS PREDICTION SAMPLE CLASSIFICATION SNP SELECTION MULTICLASS CLASSIFICATION

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Analysis of the real EADGENE data set:: Comparison of methods and guidelines for data normalisation and selection of diffrentially expressed genes (Open Access publication)

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GENETICS SELECTION EVOLUTION 2007年第6期39卷 633-650页

作者： Jaffrezic, Florence De Koning, Dirk-Jan Boettcher, Paul J. Bonnet, Agnes Buitenhuis, Bart Closset, Rodrigue Dejean, Sebastien Delmas, Celine Detilleux, Johanne C. Dovc, Peter Duval, Mylene Foulley, Jean-Louis Hedegaard, Jakob Hornshoj, Henrik Hulsegge, Ina Janss, Luc Jensen, Kirsty Jiang, Li Lavric, Miha Le Cao, Kim-Anh Lund, Mogens Sando Malinverni, Roberto Marot, Guillemette Nie, Haisheng Petzl, Wolfram Pool, Marco H. Granie, Christele Robert Cristobal, Magali San Van Schothorst, Evert M. Schuberth, Hans-Joachim Sorensen, Peter Stella, Alessandra Tosser-Klopp, Gwenola Waddington, David Watson, Michael Yang, Wei Zerbe, Holm Seyfert, Hans-Martin INRA UR337 Jouy En Josas France Roslin Inst Roslin Midlothian Scotland Parco Tecnol Padano Lodi Italy INRA UMR444 Castanet Tolosan France Univ Aarhus Tjele Denmark Univ Liege Liege Belgium Univ Toulouse 3 Toulouse France INRA UR631 Castanet Tolosan France Univ Liege Fac Vet Med Liege Belgium Univ Ljubljana Ljubljana Slovenia Anim Sci Grp Wageningen Lelystad Netherlands Univ Wageningen & Res Ctr Wageningen Netherlands Univ Munich Munich Germany RIKILT Inst Food Safety Wageningen Netherlands Univ Vet Med Hannover Germany Inst Anim Hlth Compton England Res Inst Biol Farm Anim Dummerstorf Germany

A large variety of methods has been proposed in the literature for microarray data analysis. The aim of this paper was to present techniques used by the EADGENE ( European Animal Disease Genomics Network of Excellence) WP1.4 participants for data quality control, normalisation and statistical methods for the detection of differentially expressed genes in order to provide some more general data analysis guidelines. All the workshop participants were given a real data set obtained in an EADGENE funded microarray study looking at the gene expression changes following artificial infection with two different mastitis causing bacteria: Escherichia coli and Staphylococcus aureus. It was reassuring to see that most of the teams found the same main biological results. In fact, most of the differentially expressed genes were found for infection by E. coli between uninfected and 24 h challenged udder quarters. Very little transcriptional variation was observed for the bacteria S. aureus. Lists of differentially expressed genes found by the different research teams were, however, quite dependent on the method used, especially concerning the data quality control step. These analyses also emphasised a biological problem of cross-talk between infected and uninfected quarters which will have to be dealt with for further microarray studies.

关键词： quality control differentially expressed genes mastitis resistance microarray data normalisation

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Transcriptional cross talk between the forkhead transcription factor forkhead box O1A and the progesterone receptor coordinates cell cycle regulation and differentiation in human endometrial stromal cells

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MOLECULAR ENDOCRINOLOGY 2007年第10期21卷 2334-2349页

作者： Takano, Masashi Lu, Zhenxiao Goto, Tomoko Fusi, Luca Higham, Jenny Francis, Julia Withey, Anna Hardt, Jennifer Cloke, Brianna Stavropoulou, Alexandra V. Ishihara, Osamu Lam, Eric W. -F. Unterman, Terry G. Brosens, Jan J. Kim, J. Julie Northwestern Univ Dept Obstet & Gynecol Div Reprod Biol Res Chicago IL 60611 USA Univ Illinois Coll Med Dept Physiol & Biophys Chicago IL 60612 USA Univ Illinois Coll Med Dept Med Chicago IL 60612 USA Vet Affairs Chicago Healthcare Syst Chicago IL 60612 USA Hammersmith Hosp Imperial Coll London Inst Reprod & Dev Biol London W12 0HS England Hammersmith Hosp Imperial Coll London Canc Res UK Labs London W12 0HS England Hammersmith Hosp Imperial Coll London Sect Canc Cell Biol London W12 0HS England Hammersmith Hosp Imperial Coll London Dept Oncol London W12 0HS England St Marys Hosp Univ London Imperial Coll Sci Technol & Med Dept Obstet & Gynaecol London W2 1PG England Saitama Med Sch Dept Obstet & Gynecol Moroyama Saitama 3500495 Japan

Differentiation of human endometrial stromal cells ( HESCs) into decidual cells is associated with induction of the forkhead transcription factor forkhead box O1A ( FOXO1). We performed a genomic screen to identify decidua- specific genes under FOXO1 control. Primary HESCs were transfected with small interfering RNA targeting FOXO1 or with nontargeting control small interfering RNA before treatment with a cAMP analogue and the progestin, medroxyprogesterone acetate for 72 h. Total RNA was processed for whole genome analysis using high- density oligonucleotide arrays. We identified 3405 significantly regulated genes upon decidualization of HESCs, 507 ( 15.3%) of which were aberrantly expressed upon FOXO1 knockdown. Among the most up- regulated FOXO1- dependent transcriptional targets were WNT signaling- related genes ( WNT4, WNT16), the insulin receptor ( INSR), differentiation markers ( PRL, IGFBP1, and LEFTY2), and the cyclin- dependent kinase inhibitor p57(Kip2) ( CDKN1C). Analysis of FOXO1- dependent down- regulated genes uncovered several factors involved in cell cycle regulation, including CCNB1, CCNB2, MCM5, CDC2 and NEK2. Cell viability assay and cell cycle analysis demonstrated that FOXO1 silencing promotes proliferation of differentiating HESCs. Using a glutathione- S- transferase pull- down assay, we confirmed that FOXO1 interacts with progesterone receptor, irrespectively of the presence of ligand. In agreement, knockdown of PR disrupted the regulation of FOXO1 target genes involved in differentiation ( IGFBP1, PRL, and WNT4) and cell cycle regulation ( CDKN1, CCNB2 and CDC2) in HESCs treated with either cAMP plus medroxyprogesterone acetate or with cAMP alone. Together, the data demonstrate that FOXO1 engages in transcriptional cross talk with progesterone receptor to coordinate cell cycle regulation and differentiation of HESCs.

关键词： FACTOR-BINDING PROTEIN-1 IN-VITRO OXIDATIVE STRESS GLUTATHIONE-PEROXIDASE PSEUDORECEPTOR BAMBI NUCLEAR EXCLUSION PROSTATE-CANCER MENSTRUAL-CYCLE microarray data EARLY-PREGNANCY

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To how many simultaneous hypothesis tests can normal, student's t or bootstrap calibration be applied?

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2007年第480期102卷 1282-1288页

作者： Fan, Jianqing Hall, Peter Yao, Qiwei Princeton Univ Dept Operat Res & Financial Engn Princeton NJ 08544 USA Acad Math & Syst Sci Ctr Stat Res Beijing Peoples R China Univ Melbourne Dept Math Stat Melbourne Vic 3010 Australia

In the analysis of microarray data, and in some other contemporary statistical problems, it is not uncommon to apply hypothesis tests in a highly simultaneous way. The number, N say, of tests used can be much larger than the sample sizes, n, to which the tests are applied, yet we wish to calibrate the tests so that the overall level of the simultaneous test is accurate. Often the sampling distribution is quite different for each test, so there may not be an opportunity to combine data across samples. In this setting, how large can N be, as a function of n, before level accuracy becomes poor? Here we answer this question in cases where the statistic under test is of Student's t type. We show that if either the normal or Student t distribution is used for calibration, then the level of the simultaneous test is accurate provided that log N increases at a strictly slower rate than n(1/3) as n diverges. On the other hand, if bootstrap methods are used for calibration, then we may choose log N almost as large as n(1/2) and still achieve asymptotic-level accuracy. The implications of these results are explored both theoretically and numerically.

关键词： Bonferroni's inequality edgeworth expansion genetic data large-deviation expansion level accuracy microarray data quantile estimation skewness student's t statistic

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Electronically subtracting expression patterns from a mixed cell population

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BIOINFORMATICS 2007年第24期23卷 3328-3334页

作者： Gosink, Mark M. Petrie, Howard T. Tsinoremas, Nicholas F. Scripps Florida Jupiter FL 33458 USA Univ Miami Miller Sch Med Ctr Computat Sci Miami FL 33136 USA

Motivation: Biological samples frequently contain multiple cell-types that each can play a crucial role in the development and/or regulation of adjacent cells or tissues. The search for biomarkers, or expression patterns of, one cell-type in those samples can be a complex and time-consuming process. Ordinarily, extensive laboratory bench work must be performed to separate the mixed cell population into its subcomponents, such that each can be accurately characterized. Results: We have developed a methodology to electronically subtract gene expression in one or more components of a mixed cell population from a mixture, to reveal the expression patterns of other minor or difficult to isolate components. Examination of simulated data indicates that this procedure can reliably determine the expression patterns in cell-types that contribute as little as 5 of the total expression in a mixed cell population. We re-analyzed microarray expression data from the viral infection of macrophages and from the T-cells of wild type and Foxp3 deletion mice. Using our subtraction methodology, we were able to substantially improve the identification of genes involved in processes of subcomponent portions of these samples.

关键词： REGULATORY T-CELLS TRANSCRIPTION FACTOR FOXP3 microarray data DECONVOLUTION INFECTION TISSUE

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Mining co-regulated gene profiles for the detection of functional associations in gene expression data

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BIOINFORMATICS 2007年第15期23卷 1927-1935页

作者： Gyenesei, Attila Wagner, Ulrich Barkow-Oesterreicher, Simon Stolte, Etzard Schlapbach, Ralph Univ ETH Zurich Funct Genom Ctr Zurich CH-8057 Zurich Switzerland Unilever Res Labs Vlaardingen Knowledge & Data Anal NL-3130 AC Vlaardingen Netherlands GenoSyst Ltd Bioinformat Unit FIN-20520 Turku Finland

Motivation: Association pattern discovery (APD) methods have been successfully applied to gene expression data. They find groups of co-regulated genes in which the genes are either up- or down-regulated throughout the identified conditions. These methods, however, fail to identify similarly expressed genes whose expressions change between up- and down-regulation from one condition to another. In order to discover these hidden patterns, we propose the concept of mining co-regulated gene profiles. Co-regulated gene profiles contain two gene sets such that genes within the same set behave identically (up or down) while genes from different sets display contrary behavior. To reduce and group the large number of similar resulting patterns, we propose a new similarity measure that can be applied together with hierarchical clustering methods. Results: We tested our proposed method on two well-known yeast microarray data sets. Our implementation mined the data effectively and discovered patterns of co-regulated genes that are hidden to traditional APD methods. The high content of biologically relevant information in these patterns is demonstrated by the significant enrichment of co-regulated genes with similar functions. Our experimental results show that the Mining Attribute Profile (MAP) method is an efficient tool for the analysis of gene expression data and competitive with bi-clustering techniques.

关键词： microarray data RULES NETWORK DISCOVERY PATTERNS TOOL

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Cancer onset and progression: A genome-wide, nonlinear dynamical systems perspective on onconetworks

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JOURNAL OF THEORETICAL BIOLOGY 2007年第2期246卷 234-244页

作者： Qu, K. Haidar, A. Abi Fan, J. Ensman, L. Tuncay, K. Jolly, M. Ortoleva, P. Indiana Univ Ctr Cell & Virus Theory Dept Chem Bloomington IN 47405 USA

It is hypothesized that the many human cell types corresponding to multiple states is supported by an underlying nonlinear dynamical system (NDS) of transcriptional regulatory network (TRN) processes. This hypothesis is validated for epithelial cells whose TRN is found to support an extremely complex array of states that we term a "bifurcation nexus", for which we introduce a quantitative measure of complexity. The TRN used is constructed and analyzed by integrating a database of TRN information, cDNA microarray data analyzers, bioinformatics modules, a transcription/transiation/post-translation kinetic model, and NDS analysis software. Results of this genome-wide approach suggest that a cell can be induced to persist in one state or to transition between distinct states;apparently irreversible transitions can be reversed when the high dimensional space of extracellular and intracellular parameters is understood. As conditions change, certain cellular states (cell lines) are no longer supported, new ones emerge, and transitions (cell differentiation or death) occur. The accumulation of simulated point mutations (minor changes which individually are insignificant) lead to occasional dramatic transitions. The genome-wide scope of many of these transitions is shown to arise from the cross-linked TRN structure. These notions imply that studying individual oncogenes may not be sufficient to understand cancer;rather, "onconetworks" (subsets of strongly coupled genes supporting multiple cell states) should be considered. Our approach reveals several epithelial onconetworks, each involving oncogenes and anti-tumor and supporting genes. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： bifurcation nexus cell differentiation onconetwork microarray data gene ontology nonlinear dynamical system regulatory network

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Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

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BIOINFORMATICS 2007年第16期23卷 2096-2103页

作者： Elo, Laura L. Jaervenpaeae, Henna Oresic, Matej Lahesmaa, Riitta Aittokallio, Tero Univ Turku Dept Math FI-20014 Turku Finland Turku Ctr Biotechnol FI-20521 Turku Finland VTT Biotechnol FI-02044 Espoo Finland Inst Pasteur Syst Biol Unit F-75724 Paris France

Motivation: Coexpression networks have recently emerged as a novel holistic approach to microarray data analysis and interpretation. Choosing an appropriate cutoff threshold, above which a gene-gene interaction is considered as relevant, is a critical task in most network-centric applications, especially when two or more networks are being compared. Results: We demonstrate that the performance of traditional approaches, which are based on a pre-defined cutoff or significance level, can vary drastically depending on the type of data and application. Therefore, we introduce a systematic procedure for estimating a cutoff threshold of coexpression networks directly from their topological properties. Both synthetic and real datasets show clear benefits of our data-driven approach under various practical circumstances. In particular, the procedure provides a robust estimate of individual degree distributions, even from multiple microarray studies performed with different array platforms or experimental designs, which can be used to discriminate the corresponding phenotypes. Application to human T helper cell differentiation process provides useful insights into the components and interactions controlling this process, many of which would have remained unidentified on the basis of expression change alone. Moreover, several human-mouse orthologs showed conserved topological changes in both systems, suggesting their potential importance in the differentiation process. Contact: laliel@*** Supplementary information: Supplementary data are available at Bioinformatics online.

关键词： microarray data REGULATORY NETWORKS RELEVANCE NETWORKS EXPRESSION IDENTIFICATION CANCER TRANSCRIPTION LYMPHOCYTES VALIDATION AFFYMETRIX

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