bluuebunny/crossref_metadata_2025 huggingface dataset:
Incentives to Invest in Transport Cost Reduction - Conceptual Issues and an Application to Electronic Commerce
Authors: Bandulet Martin , Morasch Karl
Published: December 2005

Do firms have proper incentives to invest in transport cost reduction? We discuss this question in a duopoly with a local firm and a distant competitor that may invest in a reduction of marginal transportation costs. In a two-stage game with investment in the first and duopoly competition in the second stage, we compare profit-maximizing investment with (constrained) welfare maximization by a social planer. Intuitively, a firm will overinvest if the negative impact on its competitor exceeds the gain in consumer surplus. We analyze how the relative strength of these two effects depends on market demand, firm conduct and investment costs. Applying our results to electronic commerce, we argue that for physical goods either overinvestment or the efficient decision not to invest is the most likely outcome while the specific characteristics of digital products yield either underinvestment or an efficient investment level that reduces transportation costs to zero.
The Geography of Intra-Industry Trade: empirics
Authors: Venables Anthony J. , Rice Patricia G. , Stewart Martin
Published: December 2005

This paper uses bilateral trade data for OECD countries at the 3-digit industry level to investigate the geography of intra-industry trade (IIT). IIT diminishes with distance and much of the existing empirical literature suggests that this is an inherent characteristic of such trade, arguing that trade in sectors intensive in IIT is choked off rapidly by distance. We show that the dependence of IIT on geography arises not because of any inherent feature of the effects of distance on such trade, but because of the spatial structure of countries’ supply and demand characteristics; close countries do a lot of IIT because they have similar economic structures.
Legal Hold-up in Cotenancy
Authors: Baucells Manel , Lippman Steven A.
Published: December 2005

Our analysis (Baucells and Lippman [2001]) of the problem of legal hold-up in co-ownership, in which legal partition is the only remedy to force a sale, proceeded as if a sale of the asset could be effected at any time at a fixed price if the cotenants agree. Here we utilize the more realistic assumption that potential buyers appear intermittently (in accord with a Poisson process and that the price offered is drawn from a specified distribution).

In applying the Nash bargaining solution, we select the disagreement point in accord either with Nash's methodology of rational threats or with reservation values. While neither methodology for selecting the disagreement point produces a credible threat when the agents incur legal costs, we argue that the rational threats approach produces more reasonable answers. Our main analysis considers the impact of a Poisson arrival of offers and an exponential time to court upon the optimal bargaining strategies of the cotenants.
Measuring the Impact of Welfare Benefits on Welfare Durations: State Stratified Partial Likelihood and Fixed Effect Approaches
Authors: Fitzgerald John M
Published: December 2005

Previous studies of welfare durations in the U.S. have analyzed the role of welfare benefits on the length of time recipients remain on welfare. Typically these studies use individual data and estimate the benefit effects based on both cross-sectional variation across locations and time variation within locations. Cross sectional results on benefit effects may be biased due to unobserved state specific differences in welfare programs or attitudes. This paper compares two strategies for dealing with the problem when estimating welfare exit hazard models: stratification of baseline hazards by state and the inclusion of state fixed effects in the hazards. Although the results show that high benefit states appear to have unobserved characteristics that lead to longer spells, the results also show that increases in welfare benefits do not directly increase welfare durations once one controls for state specific effects.
What Determines Environmental Performance at Paper Mills? The Roles of Abatement Spending, Regulation, and Efficiency
Authors: Shadbegian Ronald J , Gray Wayne B
Published: December 2005

This paper examines the determinants of environmental performance at paper mills, measured by air pollution emissions per unit of output. We consider differences across plants in air pollution abatement expenditures, local regulatory stringency, and productive efficiency. Emissions are significantly lower in plants with a larger air pollution abatement capital stock: a 10 percent increase in abatement capital stock appears to reduce emissions by 6.9 percent. This translates into a sizable social return: one dollar of abatement capital stock is estimated to provide an annual social return of about 75 cents in pollution reduction benefits. Local regulatory stringency and productive efficiency also matter: plants in non-attainment counties have 43 percent lower emissions and plants with 10 percent higher productivity have 2.5 percent lower emissions. For pollution abatement operating costs we find (puzzlingly) positive, but always insignificant, coefficients.
An Economic Assessment of Parents' Self-Composure: The Case of Physical Child Abuse
Authors: Agee Mark D , Crocker Thomas , Shogren Jason F
Published: December 2005

This paper uses a maximum likelihood procedure that accounts for unobserved heterogeneity in the sample to implement a preference-based model to assess factors that influence parents' likelihood of losing their composure and physically abusing their children. A basic supposition of the model is that parents prefer to deal with parent-child conflict by choosing tactics and behaviors that do not exceed a specified level of violence; however, endogenous parent and child behaviors and exogenous circumstances may arouse parents' emotions that cause this level to be exceeded. Our results suggest policy interventions that influence such circumstances and associated behaviors may strongly influence the incidence of physical child abuse. We estimate the ex ante annual value parents attach to risk reductions of self-composure losses associated with excessive parent-child violence. This value is shown to be greater than currently estimated annual savings in ex post costs associated with a comparable decrease in U.S. physical child abuse incidence.
Tax Competition and Tax Harmonization With Evasion
Authors: Gandelman Néstor , Hernández-Murillo Rubén
Published: December 2005

We examine a two-jurisdiction tax competition environment where local governments can only imperfectly monitor where agents pay taxes and risk-averse individuals may choose to cross borders to pay lower taxes in a neighboring location.

In a game between local authorities, we find that, when communities differ in size, in equilibrium the smaller community sets lower taxes and attracts agents from the larger jurisdiction. With identical communities, tax rates must be equal.

Finally, we examine the incentives of jurisdictions to harmonize tax rates and find that, whenever the smaller community benefits from tax harmonization, the larger jurisdiction will benefit also.
Spatial Competition and Merger
Authors: Higgins Richard S , Johnson Paul A , Sullivan John T
Published: December 2005

We consider a computational equilibrium model of spatially differentiated Bertrand competition and apply it to merger analysis. Two pricing paradigms are studied: one where firms cannot price discriminate among customers and one where firms can. The model encompasses many details that make it highly realistic. A detailed example illustrates several insights into merger analysis that are not readily apparent through traditional means. The most important of these is that merger of substitute products under Bertrand price competition need not result in a price increase.
Has Curriculum Closed the Test Score Gap in Math?
Authors: Rose Heather
Published: December 2005

This study examines the extent to which convergence in mathematics course-taking behavior is responsible for narrowing the Hispanic-white and the black-white test score gaps during the 1980s. Mathematics curriculum is measured in detail using high school transcript data from both High School and Beyond and the National Education Longitudinal Study of 1988. After controlling for demographic, family, and school characteristics, changes in curriculum account for about 60 percent of the narrowing Hispanic-white test score gap between 1982 and 1992. However, the black-white test score gap did not drop significantly.
Producers and Predators in a Multiple Community Setting
Authors: Han Shinkyoo , Ochs Jack
Published: December 2005

This paper analyzes an extension of a model of production and predation due to Grossman (1998) to a multiple community setting. In a multiple community setting, defense expenditures in any one community have the property of a local public good. Such expenditures produce effects on other communities. These effects include changes in the distribution of population among communities, the redistribution of predatory effort over communities, and an induced change in the predator/producer ratio in the economy as a whole. The question we address is whether the level of defense chosen by local governments so as to maximize the per capita consumption of their own producers, given defense levels elsewhere, always produces a second-best outcome. Our analysis shows that if the number of communities is fixed, fully rational local government decision-making leads to the same level of defense activity and equilibrium per capita consumption as would be chosen by a central planner. However, if individual local governments are boundedly rational, in the sense that they do not anticipate the effects of their own defense activity on the equilibrium predator/producer ratio and distribution of producer activity, then competition among local governments never achieves a first-best outcome. Furthermore, the equilibrium associated with competition among boundedly rational local governments can sometimes yield a lower consumption per capita in equilibrium than would be achieved if there were no local governments and each agent who chose to be a producer also chose his/her own level of defense.
Race, Ethnicity, and Gender Differences in the Relationship Between Substance Use and Adolescent Sexual Behavior
Authors: Averett Susan L , Rees Daniel I , Duncan Brian , Argys Laura
Published: December 2005

Previous researchers have noted that the positive correlation between substance use and sexual behavior is stronger for white adolescents than for their black and Hispanic counterparts. Using an instrumental variables approach to control for the possible endogeneity of substance use, and data from The National Longitudinal Study of Adolescent Health, we estimate the effects of alcohol and marijuana use on the probability of being sexually active by race, ethnicity, and gender. Our results suggest that there exist potentially important racial, ethnic and gender differences in the relationship between substance use and sexual behavior. This information may be valuable for policymakers interested in reducing sexual activity among teens.
Time In Purgatory: Examining the Grant Lag for U.S. Patent Applications
Authors: Popp David , Juhl Ted , Johnson Daniel K.N.
Published: December 2005

As patent applications increase, and the range of patentable technologies increases, the length of time it takes for an invention to go through the examination process at the U.S. Patent Office has increased. Concerns over the distributional effects of these changes have been expressed during policy debates. We use data on U.S. patent applications and grants to ask who is affected by longer grant lags. We augment this analysis with interviews of patent examiners, leading to a better understanding of the examination process. Our analysis finds that differences across technology are most important. These differences do not erode over time, suggesting that learning effects alone will not reduce grant lags. Inventor characteristics have statistically significant effects, but the magnitudes are small.
Interest Groups and Get Out the Vote Drives
Authors: Cadigan John
Published: December 2005

This article analyzes voter mobilization and interest group activity within a citizen-candidate model. Interest groups influence the decisions of 'high cost’ voters by running a 'get out the vote’ campaign. Membership fees paid by citizens joining an interest group finance the vote drives. Because citizens choose whether to join an interest group, size is an endogenous feature of equilibrium outcomes. In contrast to the existing literature, it is shown that smaller groups may have greater influence. Importantly, this result does not depend on the ability of smaller groups to overcome the free rider problem. Intuitively, because smaller groups may have a less diverse membership, they can advocate more extreme policy outcomes. An interest group’s influence is shown to depend on voting costs and the separation between candidate positions. The presence of multiple interest groups mitigates an interest’s influence, resulting in inefficiency.
Mono Lake and the Evolving Public Trust in Western Water
Authors: Blumm Michael , Schwartz Thea
Published: December 2005

The Mono Lake case is a lodestar in public trust jurisprudence. This article discusses that case and explains how it revolutionized California water law. The article identifies six principles established by the decision that place it in the vanguard of public trust case law. It also examines some of the progeny of the Mono Lake decision, both in California and in other western states. The article claims that in the wake of Mono Lake, a half dozen western states have recognized the application of the public trust doctrine to water rights, although no other state has embraced all six of the tenets of the Mono Lake doctrine. The article concludes that the the public trust doctrine's deep historical roots and conceptual coherence make it a promising vehicle by which to moderate the excesses of the prior appropriation doctrine of western water law and replace that doctrine's "all or nothing" approach with what the article refers to as the "accommodation principle," under which both diversionary and instream uses of water will be accommodated wherever feasible.
Modelling the Hydrodynamics of Gas-Liquid Packed Beds via Slit Models: A Review
Authors: Iliuta Ion , Larachi Faical
Published: December 2005

Packed bed columns are intensively used as multiphase contactors and reactors in chemical, biochemical, and petrochemical industries. Due to two-phase through the packed beds, the performance of multiphase contactors depends on the complex interaction of the interphase mass transfer, thermodynamics and hydrodynamics. Under reaction conditions, the intraparticle mass transfer and reaction kinetics have additional considerable impact on the reactor performance. The inherent complexity of multiphase systems leaves many issues unresolved and leads to many inconsistencies and questionable approximations, especially in the modelling of multiphase flow. Multiphase flow modelling in packed beds is a complicated task because of the difficulty incorporating the complex geometry into the flow equations, and the difficulty in accounting for the gas-liquid interactions in the presence of complex fluid-particle (e.g., partial wetting) contacting. This review focuses on recent advances made in the modelling of the hydrodynamics of two-phase downflow and counter-current flow in packed beds via two-fluid models coupled with slit and double slit phenomenological approximations relating the mutual three phase hydrodynamic interactions.
Hydrodynamics of High-Density Downer Reactors Using a Novel Solids Feeder
Authors: Song Xuqi , Bi Xiaotao T , Bolkan Yasemin
Published: December 2005

Downer reactors have many advantages over risers for reactions requiring very short residence time. However, its application for reactions where a high solid/gas ratio is required has been restricted by the low solids holdup in the fully developed region (typically less than one percent). In this paper, we present the performance of a 0.078 m diameter and 3.2 m long high-density downer reactor equipped with a recently developed novel solids feeding system.Experiments were carried out using fluid coke particles of particle density 1600 kg/m3 and mean diameter 133 mm, with solids fluxes up to 1400 kg/m2s and superficial gas velocities ranging from 0 to 6 m/s. The downer was operated under batch mode, with solids fed from a solids feeder installed inside a fluidized bed at the top of the downer and the bottom end of the downer connected to a receiving hopper. The solids flux was controlled by a butterfly valve installed 0.4 m below the downer entrance. Air was injected into the downer through four 45-degree-angled nozzles located right below the butterfly valve. Solids were lifted up from the bottom receiving-hopper to the upper fluidized bed through an external riser after each run.The cross-sectional average solids hold-up calculated from the integration of local measurements obtained from a capacitance probe was found to be a function of both the solids flux and superficial gas velocity. The solids hold-up increased with increasing solids flux at a given superficial gas velocity, but decreased with increasing gas velocity at a fixed solids flux. A cross-sectional average solids holdup of 16.5% was achieved at an axial position of 3.0 m below the air injection point under the operating condition of Gs=1400 kg/m2s and Ug=2.0 m/s. The shape of radial solids distribution varied not only with the superficial gas velocity but also with the solids flux in high-density downers. The radial solids distribution became more uniform under higher solids fluxes and lower gas velocities. The dense ring at the near wall region disappeared under the high-flux operating condition (Gs=1400 kg/m2s, Ug=2.0 m/s). The radial solids holdup profiles became less uniform, denser near the wall and more dilute and non-uniformly distributed in the central region, as the gas velocity increased from 3.0 to 6.0 m/s at a fixed solids flux of about 400 kg/m2s, which is significantly different from flow patterns reported in the literature under lower solids flux conditions.
Innovative Reactors for Determining Kinetics of Highly Exothermic Heterogeneous Catalytic Reactions
Authors: Dropka Natasha , Jaehnisch Klaus , Kondratenko E. V. , Kondratenko Vita , Kraehnert Ralph , Steinfeldt Norbert , Wolf Dorit , Baerns Manfred
Published: December 2005

For highly exothermic catalytic reactions particular measures have to be taken for obtaining reliable kinetic and mechanistic information, which has been preferably determined under the prevalence of isothermicity, and which is not disguised by transport processes. If this is true such kinetics can be reliably extrapolated to conditions outside the frame of experimental conditions. To this end various reactor set-ups are presented and their operation is illustrated by some case studies. The temperature-scanning reactor, which is not inherently isothermal, is included since non-isothermicity is not an obstacle to easy kinetic data assessment. Its performance is clarified by estimating kinetic parameters for the ammonia synthesis at elevated pressure using a commercial catalyst. Two types of novel micro-structured reactors are presented which have been applied for two highly exothermic catalytic reactions, i.e., the oxidative dehydrogenation of propane (ODP) and the ammonia oxidation to N2, N2O and NO. These studies served the purpose of getting mechanistic insights into the reaction mechanisms of both reactions, to validate a kinetic scheme for the ODP reaction and to derive the estimates of the respective kinetic parameters. Finally, a reactor for transient experiments in the peak pressure range of 1 to 10 Pa has been used for studying reaction steps related to the catalytic ammonia oxidation under isothermal conditions. Isothermal studies are possible due to the low concentrations of reactants by which any undesired hot spots on the catalyst surface were suppressed. The instrumental arsenal described is an asset for both fundamental as well as applied work in studying exothermic catalytic reactions.
Relating HIV-1 Sequence Variation to Replication Capacity via Trees and Forests
Authors: Segal Mark R , Barbour Jason D , Grant Robert M
Published: December 2005

The problem of relating genotype (as represented by amino acid sequence) to phenotypes is distinguished from standard regression problems by the nature of sequence data. Here we investigate an instance of such a problem where the phenotype of interest is HIV-1 replication capacity and contiguous segments of protease and reverse transcriptase sequence constitutes genotype. A variety of data analytic methods have been proposed in this context. Shortcomings of select techniques are contrasted with the advantages afforded by tree-structured methods. However, tree-structured methods, in turn, have been criticized on grounds of only enjoying modest predictive performance. A number of ensemble approaches (bagging, boosting, random forests) have recently emerged, devised to overcome this deficiency. We evaluate random forests as applied in this setting, and detail why prediction gains obtained in other situations are not realized. Other approaches including logic regression, support vector machines and neural networks are also applied. We interpret results in terms of HIV-1 reverse transcriptase structure and function.
Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns
Authors: Liang Yulan , Kelemen Arpad G
Published: December 2005

There are several important issues to be addressed for gene expression temporal patterns' analysis: first, the correlation structure of multidimensional temporal data; second, the numerous sources of variations with existing high level noise; and last, gene expression mostly involves heterogeneous multiple dynamic patterns. We propose a Hierarchical Bayesian Neural Network model to account for the input correlations of time course gene array data. The variations in absolute gene expression levels and the noise can be estimated with the hierarchical Bayesian setting. The network parameters and the hyperparameters were simultaneously optimized with Monte Carlo Markov Chain simulation. Results show that the proposed model and algorithm can well capture the dynamic feature of gene expression temporal patterns despite the high noise levels, the highly correlated inputs, the overwhelming interactions, and other complex features typically present in microarray data. We test and demonstrate the proposed models with yeast cell cycle temporal data sets. The model performance of Hierarchical Bayesian Neural Network was compared to other popular machine learning methods such as Nearest Neighbor, Support Vector Machine, and Self Organized Map.
Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data
Authors: Rahnenführer Jörg , Domingues Francisco S , Maydt Jochen , Lengauer Thomas
Published: December 2005

We present a statistical approach to scoring changes in activity of metabolic pathways from gene expression data. The method identifies the biologically relevant pathways with corresponding statistical significance. Based on gene expression data alone, only local structures of genetic networks can be recovered. Instead of inferring such a network, we propose a hypothesis-based approach. We use given knowledge about biological networks to improve sensitivity and interpretability of findings from microarray experiments.

Recently introduced methods test if members of predefined gene sets are enriched in a list of top-ranked genes in a microarray study. We improve this approach by defining scores that depend on all members of the gene set and that also take pairwise co-regulation of these genes into account. We calculate the significance of co-regulation of gene sets with a nonparametric permutation test. On two data sets the method is validated and its biological relevance is discussed. It turns out that useful measures for co-regulation of genes in a pathway can be identified adaptively.

We refine our method in two aspects specific to pathways. First, to overcome the ambiguity of enzyme-to-gene mappings for a fixed pathway, we introduce algorithms for selecting the best fitting gene for a specific enzyme in a specific condition. In selected cases, functional assignment of genes to pathways is feasible. Second, the sensitivity of detecting relevant pathways is improved by integrating information about pathway topology. The distance of two enzymes is measured by the number of reactions needed to connect them, and enzyme pairs with a smaller distance receive a higher weight in the score calculation.
A Family-Based Association Test for Repeatedly Measured Quantitative Traits Adjusting for Unknown Environmental and/or Polygenic Effects
Authors: Lange Christoph , van Steen Kristel , Andrew Toby , Lyon Helen , DeMeo Dawn L , Raby Benjamin , Murphy Amy , Silverman Edwin K , MacGregor Alex , Weiss Scott T , Laird Nan M
Published: December 2005

We propose a family-based association test, FBAT-PC, for studies with quantitative traits that are measured repeatedly. The traits may be influenced by partially or completely unknown factors that may vary for each measurement. Using generalized principal component analysis, FBAT-PC amplifies the genetic effects of each measurement by constructing an overall phenotype with maximal heritability. Analytically, and in the simulation studies, we compare FBAT-PC with standard methodology and assess both the heritability of the overall phenotype and the power of FBAT-PC. Compared to univariate analysis, FBAT-PC achieves power gains of up to 200%. Applications of FBAT-PC to an osteoporosis study and to an asthma study show the practical relevance of FBAT-PC. FBAT-PC has been implemented in the software package PBAT and is freely available at http://www.biostat.harvard.edu/~clange/default.htm.
Deletion/Substitution/Addition Algorithm in Learning with Applications in Genomics
Authors: Sinisi Sandra E , van der Laan Mark J.
Published: December 2005

van der Laan and Dudoit (2003) provide a road map for estimation and performance assessment where a parameter of interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using a loss function. After briefly reviewing this approach, this article proposes a general deletion/substitution/addition algorithm for minimizing, over subsets of variables (e.g., basis functions), the empirical risk of subset-specific estimators of the parameter of interest. This algorithm provides us with a new class of loss-based cross-validated algorithms in prediction of univariate outcomes, which can be extended to handle multivariate outcomes, conditional density and hazard estimation, and censored outcomes such as survival. In the context of regression, using polynomial basis functions, we study the properties of the deletion/substitution/addition algorithm in simulations and apply the method to detect transcription factor binding sites in yeast gene expression experiments.
PLS Dimension Reduction for Classification with Microarray Data
Authors: Boulesteix Anne-Laure
Published: December 2005

Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new components is compared with some of the best state-of-the-art classification methods. Moreover, a boosting algorithm is applied to this classification method. In addition, a simple procedure to choose the number of PLS components is suggested. The connection between PLS dimension reduction and gene selection is examined and a property of the first PLS component for binary classification is proved. In addition, we show how PLS can be used for data visualization using real data. The whole study is based on 9 real microarray cancer data sets.
A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks
Authors: Ruschhaupt Markus , Huber Wolfgang , Poustka Annemarie , Mansmann Ulrich
Published: December 2005

We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).
Concerns About Unreliable Data from Spotted cDNA Microarrays Due to Cross-Hybridization and Sequence Errors
Authors: Handley Daniel , Serban Nicoleta , Peters David G , Glymour Clark
Published: December 2005

We discuss our concerns regarding the reliability of data generated by spotted cDNA microarrays. Two types of error we highlight are cross-hybridization artifact due to sequence homologies and sequence errors in the cDNA used for spotting on microarrays. We feel that statisticians who analyze microarray data should be aware of these sources of unreliability intrinsic to cDNA microarray design and use.
Generalized Rank Tests for Replicated Microarray Data
Authors: Lee Mei-Ling Ting , Gray Robert J , Björkbacka Harry , Freeman Mason W
Published: December 2005

Gene expression data from microarray experiments have been studied using several statistical models. Significance Analysis of Microarrays (SAM), for example, has proved to be useful in analyzing microarray data. In the spirit of the SAM procedures, we develop permutation based rank-tests for generalized Wilcoxon ranksum test for two-group comparisons of replicated microarray data. Also, for microarray experiments with randomized block design, we consider generalized signed rank test. The statistical analysis software package is written in R and is freely available in a package.
Statistical Analysis of Genomic Tag Data
Authors: LaFramboise Thomas L , Hayes D. Neil , Tengs Torstein
Published: December 2005

We present a series of statistical solutions to challenges that commonly arise in the production and analysis of genomic tag libraries. Tag libraries are collections of fragments of DNA or RNA, with each unique fragment often present in millions or billions of copies. Inferences can be made from data obtained by sequencing a subset of the library. The statistical approaches outlined in this paper are divided into three parts. First, we demonstrate the application of classical capture-recapture theory to the question of library complexity, i.e. the number of unique fragments in the library. Simulation studies verify the accuracy, for sample sizes of magnitudes typical in genomic studies, of the formulas we use to make our estimates. Second, we present a straightforward statistical cost analysis of tag experiments designed to uncover either disease-causing pathogens or new genes. Third, we develop a hidden Markov model approach to karyotyping a sample using a tag library derived from the sample's genomic DNA. While the resolution of the approach depends upon the number of tags sequenced from the library, we show via simulation that copy number alterations can be reliably detected for lengths as small as 1 Mb, even when a moderate number of tags are sequenced. Simulations predict very good specificity as well. Finally, all three of our approaches are applied to data from real tag library experiments. The hidden Markov model results are in line with what was expected from simulation, and genomic alterations found by applying the method to a cancer cell line library are confirmed using PCR.The methods and data described in this paper are contained in an R package, tagAnalysis, freely available at http://meyerson.dfci.harvard.edu/~tl974/tagAnalysis.
Estimating Motifs Under Order Restrictions
Authors: van Zwet Erik W , Kechris Katherina J , Bickel Peter J , Eisen Michael B.
Published: December 2005

Transcription factors and many other DNA-binding proteins recognize more than one specific sequence. Among sequences recognized by a given DNA-binding protein, different positions exhibit varying degrees of conservation. The reason is that base pairs that are more extensively contacted by the protein tend to be more conserved. This observation can be used in the discovery of transcription factor binding sites. Here we present a rigorous means to accomplish this. In particular, we constrain the order of the information (entropy) in the columns of the position specific weight matrix (PWM) which characterizes the motif being sought. We then show how to compute the maximum likelihood estimate of a PWM under such order restrictions. This computation is easily integrated with the EM algorithm or the Gibbs sampler to enhance performance in the search for motifs in unaligned sequences. We demonstrate our method on a well-known data set of binding sites of the transcription factor Crp in E. coli.
Making Sense of High-Throughput Protein-Protein Interaction Data
Authors: Scholtens Denise , Gentleman Robert
Published: December 2005

Accurate systems biology modeling requires a complete catalog of protein complexes and their constituent proteins. We discuss a graph-theoretic/statistical algorithm for local dynamic modeling of protein complexes using data from affinity purification-mass spectrometry experiments. The algorithm readily accommodates multicomplex membership by individual proteins and dynamic complex composition, two biological realities not accounted for in existing topological descriptions of the overall protein network. A likelihood-based objective function guides the protein complex modeling algorithm. With an accurate complex membership catalog in place, systems biology can proceed with greater precision.
Early Diagnostic Marker Panel Determination for Microarray Based Clinical Studies
Authors: Jaeger Jochen , Weichenhan Dieter , Ivandic Boris , Spang Rainer
Published: December 2005

We present a novel, cost efficient two-phase design for predictive clinical gene expression studies: early marker panel determination (EMPD). In Phase-1, genome-wide microarrays are used only for a small number of individual patient samples. From this Phase-1 data a panel of marker genes is derived. In Phase-2, the expression values of these marker panel genes are measured for a large group of patients and a predictive classification model is learned from this data. Phase-2 does not require the use of expensive whole genome microarrays, thus making EMPD a cost efficient alternative for current trials. The expected performance loss of EMPD is compared to designs which use genome-wide microarrays for all patients. We also examine the trade-off between the number of patients included in Phase-1 and the number of marker genes required in Phase-2. By analysis of five published datasets we find that in Phase-1 already 16 patients per group are sufficient to determine a suitable marker panel of 10 genes, and that this early decision compromises the final performance only marginally.
A Simple Loglinear Model for Haplotype Effects in a Case-Control Study Involving Two Unphased Genotypes
Authors: Baker Stuart G.
Published: December 2005

Because haplotypes may parsimoniously summarize the effect of genes on disease, there is great interest in using haplotypes in case-control studies of unphased genotype data. Previous methods for investigating haplotypes effects in case-control studies have not allowed for both of the following two scenarios that could have a large impact on results (i) departures from Hardy-Weinberg equilibrium in controls as well as cases, and (ii) an interactive effect of haplotypes and environmental covariates on the probability of disease. A new method is proposed that generalizes the model of Epstein and Satten to incorporate both (i) and (ii). Computations are relatively simple involving a single loglinear design matrix for parameters modeling the distribution of haplotype frequencies in controls, parameters modeling the effect of haplotypes and covariate-haplotype interactions on disease, and nuisance parameters required for correct inference. Based on simulations with realistic sample sizes, the method is recommended with data from two genotypes, a recessive or dominant model linking haplotypes to disease, and estimates of haplotype effects among haplotypes with a frequency greater than 10%. The methodology is most useful with candidate genotype pairs or for searching through pairs of genotypes when scenarios (i) and (ii) are likely. An example without a covariate illustrates the importance of modeling a departure from Hardy-Weinberg equilibrium in controls.
Incorporating Biological Information as a Prior in an Empirical Bayes Approach to Analyzing Microarray Data
Authors: Pan Wei
Published: December 2005

Currently the practice of using existing biological knowledge in analyzing high throughput genomic and proteomic data is mainly for the purpose of validations. Here we take a different approach of incorporating biological knowledge into statistical analysis to improve statistical power and efficiency. Specifically, we consider how to fuse biological information into a mixture model to analyze microarray data. In contrast to a standard mixture model where it is assumed that all the genes come from the same (marginal) distribution, including an equal prior probability of having an event, such as having differential expression or being bound by a transcription factor (TF), our proposed mixture model allows the genes in different groups to have different distributions while the grouping of the genes reflects biological information. Using a list of about 800 putative cell cycle-regulated genes as prior biological knowledge, we analyze a genome-wide location data to detect binding sites of TF Fkh1. We find that our proposal improves over the standard approach, resulting in reduced false discovery rates (FDR), and hence it is a useful alternative to the current practice.
A General Framework for Weighted Gene Co-Expression Network Analysis
Authors: Zhang Bin , Horvath Steve
Published: December 2005

Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for `soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion).We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the `weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its `unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding.We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.
Empirical Bayes Estimation of a Sparse Vector of Gene Expression Changes
Authors: Erickson Stephen , Sabatti Chiara
Published: December 2005

Gene microarray technology is often used to compare the expression of thousand of genes in two different cell lines. Typically, one does not expect measurable changes in transcription amounts for a large number of genes; furthermore, the noise level of array experiments is rather high in relation to the available number of replicates. For the purpose of statistical analysis, inference on the ``population'' difference in expression for genes across the two cell lines is often cast in the framework of hypothesis testing, with the null hypothesis being no change in expression. Given that thousands of genes are investigated at the same time, this requires some multiple comparison correction procedure to be in place. We argue that hypothesis testing, with its emphasis on type I error and family analogues, may not address the exploratory nature of most microarray experiments. We instead propose viewing the problem as one of estimation of a vector known to have a large number of zero components. In a Bayesian framework, we describe the prior knowledge on expression changes using mixture priors that incorporate a mass at zero, and we choose a loss function that favors the selection of sparse solutions. We consider two different models applicable to the microarray problem, depending on the nature of replicates available, and show how to explore the posterior distributions of the parameters using MCMC. Simulations show an interesting connection between this Bayesian estimation framework and false discovery rate (FDR) control. Finally, two empirical examples illustrate the practical advantages of this Bayesian estimation paradigm.
Hierarchical Inverse Gaussian Models and Multiple Testing: Application to Gene Expression Data
Authors: Labbe Aurelie , Thompson Mary
Published: December 2005

Detecting differentially expressed genes in microarray experiments is a topic that has been well studied in the literature. Many hypothesis testing methods have been proposed that rely on strong distributional assumptions for the gene intensities. However, the shape of microarray data may vary substantially from one experiment to another, and model assumptions may be seriously violated in many cases. The literature on microarray data is mainly based on two distributions: the log-normal and the gamma distributions, that often appear to be effective when used in a Bayesian hierarchical framework. However, if a model that fits the data well in a global manner seems attractive, two points should be regarded with attention: the ability of the model to fit the tail of the observed distribution, and its robustness to a wrong specification of the model, in terms of error rates for the hypothesis tests.In order to focus on these aspects, we propose to use Bayesian models involving the inverse Gaussian distribution to describe gene expression data. We show that these models can be good competitors to the traditional Bayesian or random effect gamma or log-normal models in some situations. A multiple testing procedure is then proposed, based on an asymptotic property of the posterior probability of the one-sided alternative hypothesis. We show that the asymptotic property is well approximated for inverse Gaussian models, even when the number of observations available for each test is very small.
Correlation Between Gene Expression Levels and Limitations of the Empirical Bayes Methodology for Finding Differentially Expressed Genes
Authors: Qiu Xing , Klebanov Lev , Yakovlev Andrei
Published: December 2005

Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test statistics across genes. The empirical Bayes methodology in the nonparametric and parametric formulations, as well as closely related methods employing a two-component mixture model, represent typical examples. It is frequently assumed that dependence between gene expressions (or associated test statistics) is sufficiently weak to justify the application of such methods for selecting differentially expressed genes. By applying resampling techniques to simulated and real biological data sets, we have studied a potential impact of the correlation between gene expression levels on the statistical inference based on the empirical Bayes methodology. We report evidence from these analyses that this impact may be quite strong, leading to a high variance of the number of differentially expressed genes. This study also pinpoints specific components of the empirical Bayes method where the reported effect manifests itself.
Comparing Bacterial DNA Microarray Fingerprints
Authors: Willse Alan , Chandler Darrell P , White Amanda , Protic Miroslava , Daly Don S , Wunschel Sharon
Published: December 2005

Epidemiologic and forensic investigations often require assays to detect subtle genetic differences between closely related microorganisms. Typically, gel electrophoresis is used to compare randomly amplified DNA fragments between microbial samples, where the patterns of DNA fragment sizes are viewed as genotype fingerprints. The limited genomic sample captured on a gel, however, is not always sufficient to discriminate closely related strains. This paper examines the application of microarray technology to DNA fingerprinting as a high-resolution alternative to gel-based methods. The so-called universal microarray, which uses short oligonucleotide probes that do not target specific genes or species, is intended to be applicable to all microorganisms because it does not require prior knowledge of genomic sequence. In principle, closely related strains can be distinguished if enough independent oligonucleotide probes are used on the microarray, i.e., if the genome is sufficiently sampled. In practice, we confront noisy data, imperfectly matched hybridizations, and a high-dimensional inference problem. We describe the statistical problems of microarray fingerprinting, outline similarities with and differences from more conventional microarray applications, and illustrate a statistical measurement error model to fingerprint 10 closely related strains from three Bacillus species, and 3 strains from non-Bacillus species.
A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics
Authors: Schäfer Juliane , Strimmer Korbinian
Published: December 2005

Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity.Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.