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A Copula-Based Algorithm for Discovering Patterns of Dependent Observations

JOURNAL OF CLASSIFICATION 2012年第1期29卷 50-75页

作者： Di Lascio, F. Marta L. Giannerini, Simone Univ Bologna Dipartimento Sci Stat I-40126 Bologna Italy

The main aim of this work is the study of clustering dependent data by means of copula functions. Copulas are popular multivariate tools whose importance within clustering methods has not been investigated yet in detail. We propose a new algorithm (CoClust in brief) that allows to cluster dependent data according to the multivariate structure of the generating process without any assumption on the margins. Moreover, the approach does not require either to choose a starting classification or to set a priori the number of clusters;in fact, the CoClust selects them by using a criterion based on the log-likelihood of a copula fit. We test our proposal on simulated data for different dependence scenarios and compare it with a model-based clustering technique. Finally, we show applications of the CoClust to real microarray data of breast-cancer patients.

关键词： Clustering methods CoClust algorithm Copula functions Model-based clustering microarray data

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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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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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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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Merging two gene-expression studies via cross-platform normalization

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BIOINFORMATICS 2008年第9期24卷 1154-1160页

作者： Shabalin, Andrey A. Tjelmeland, Hakon Fan, Cheng Perou, Charles M. Nobel, Andrew B. Univ N Carolina Dept Stat & Operat Res Chapel Hill NC 27599 USA Norwegian Univ Sci & Technol Dept Math Sci N-7034 Trondheim Norway Univ N Carolina Lineberger Comprehens Canc Ctr Chapel Hill NC 27599 USA Univ N Carolina Dept Pathol & Lab Med Chapel Hill NC 27599 USA Univ N Carolina Dept Genet Chapel Hill NC 27599 USA

Motivation: Gene-expression microarrays are currently being applied in a variety of biomedical applications. This article considers the problem of how to merge datasets arising from different gene-expression studies of a common organism and phenotype. Of particular interest is how to merge data from different technological platforms. Results: The article makes two contributions to the problem. The first is a simple cross-study normalization method, which is based on linked gene/sample clustering of the given datasets. The second is the introduction and description of several general validation measures that can be used to assess and compare cross-study normalization methods. The proposed normalization method is applied to three existing breast cancer datasets, and is compared to several competing normalization methods using the proposed validation measures. Availability: The supplementary materials and XPN Matlab code are publicly available at website: https://***/xpn Contact: shabalin@*** Supplementary information: Supplementary data are available at Bioinformatics online.

关键词： PROBE LEVEL data ART. NO. E15 microarray data BREAST-CANCER MOLECULAR CLASSIFICATION METAANALYSIS PORTRAITS SUMMARIES PROFILES TUMORS

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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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Semiparametric estimation of a two-component mixture model where one component is known

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SCANDINAVIAN JOURNAL OF STATISTICS 2006年第4期33卷 733-752页

作者： Bordes, Laurent Delmas, Celine Vandekerkhove, Pierre Univ Marne Vallee LAMA F-77454 Champs Sur Marne 2 Marne La Vallee France Univ Technol Compiegne LMAC F-60206 Compiegne France

We consider a two-component mixture model where one component distribution is known while the mixing proportion and the other component distribution are unknown. These kinds of models were first introduced in biology to study the differences in expression between genes. The various estimation methods proposed till now have all assumed that the unknown distribution belongs to a parametric family. In this paper, we show how this assumption can be relaxed. First, we note that generally the above model is not identifiable, but we show that under moment and symmetry conditions some 'almost everywhere' identifiability results can be obtained. Where such identifiability conditions are fulfilled we propose an estimation method for the unknown parameters which is shown to be strongly consistent under mild conditions. We discuss applications of our method to microarray data analysis and to the training data problem. We compare our method to the parametric approach using simulated data and, finally, we apply our method to real data from microarray experiments.

关键词： identifiability microarray data mixture multiple test hypothesis sentiparametric training data

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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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Gene expression analysis supports tumor threshold over 2.0 cm for T-category breast cancer

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EURASIP JOURNAL ON BIOINFORMATICS AND SYSTEMS BIOLOGY 2016年第1期2016卷 6页

作者： Solvang, Hiroko K. Frigessi, Arnoldo Kaveh, Fateme Riis, Margit L. H. Luders, Torben Bukholm, Ida R. K. Kristensen, Vessela N. Andreassen, Bettina K. Inst Marine Res Dept Marine Mammals C Sundts Gate 64 N-5004 Bergen Norway Univ Oslo Inst Basic Med Sci Dept Biostat Oslo Norway Stat Innovat Sfi 2 Oslo Norway Oslo Univ Hosp Ulleval Med Genet Dept Oslo Norway Akershus Univ Hosp Dept Surg Lorenskog Norway Akershus Univ Hosp Inst Clin Med Dept Mol Biol & Lab Sci EpiGen Lorenskog Norway Univ Oslo Norwegian Ctr HPH Network Inst Clin Med Oslo Norway Oslo Univ Hosp Radiumhosp Inst Canc Res Dept Genet Oslo Norway Canc Registry Norway Inst Populat Based Res Oslo Norway

Tumor size, as indicated by the T-category, is known as a strong prognostic indicator for breast cancer. It is common practice to distinguish the T1 and T2 groups at a tumor size of 2.0 cm. We investigated the 2.0-cm rule from a new point of view. Here, we try to find the optimal threshold based on the differences between the gene expression profiles of the T1 and T2 groups (as defined by the threshold). We developed a numerical algorithm to measure the overall differential gene expression between patients with smaller tumors and those with larger tumors among multiple expression datasets from different studies. We confirmed the performance of the proposed algorithm by a simulation study and then applied it to three different studies conducted at two Norwegian hospitals. We found that the maximum difference in gene expression is obtained at a threshold of 2.2-2.4 cm, and we confirmed that the optimum threshold was over 2.0 cm, as indicated by a validation study using five publicly available expression datasets. Furthermore, we observed a significant differentiation between the two threshold groups in terms of time to local recurrence for the Norwegian datasets. In addition, we performed an associated network and canonical pathway analyses for the genes differentially expressed between tumors below and above the given thresholds, 2.0 and 2.4 cm, using the Norwegian datasets. The associated network function illustrated a cellular assembly of the genes for the 2.0-cm threshold: an energy production for the 2.4-cm threshold and an enrichment in lipid metabolism based on the genes in the intersection for the 2.0- and 2.4-cm thresholds.

关键词： Breast cancer T-category Differentially expressed microarray data Two-group comparison statistical test Optimization algorithm

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