Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering

A Bayesian model-based clustering approach is proposed for identifying differentially expressed genes in meta-analysis. A Bayesian hierarchical model is used as a scientific tool for combining information from different studies, and a mixture prior is used to separate differentially expressed genes from non-differentially expressed genes. Posterior estimation of the parameters and missing observations are done by using a simple Markov chain Monte Carlo method. From the estimated mixture model, useful measure of significance of a test such as the Bayesian false discovery rate (FDR), the local FDR (Efron et al., 2001), and the integration-driven discovery rate (IDR; Choi et al., 2003) can be easily computed. The model-based approach is also compared with commonly used permutation methods, and it is shown that the model-based approach is superior to the permutation methods when there are excessive under-expressed genes compared to over-expressed genes or vice versa. The proposed method is applied to four publicly available prostate cancer gene expression data sets and simulated data sets.     

Direct crystallographic observation of an acyl-enzyme intermediate in the elastase-catalyzed hydrolysis of a peptidyl ester substrate: Exploiting the "glass transition" in protein dynamics

The crystal structure of the acyl complex of porcine pancreatic elastase with its peptidyl ester substrate N-acetyl-ala-ala-ala-methyl ester (Ac(Ala)3OMe) has been determined at 2.5 A resolution. The complex was stabilized by exploiting the "glass transition" in protein dynamics that occurs at around -53 degrees C (220 K). Substrate was flowed into the crystal in a cryoprotective solvent above this temperature, and then the crystal was rapidly cooled to a temperature below the transition to trap the species that formed. The use of a flow cell makes the experiment a kinetic one and means that the species prior to the rate determining transition state has a chance to accumulate. The resulting crystal structure shows an acyl-enzyme intermediate in which the leaving group is absent and the carbonyl carbon of the C-terminal alanine residue is covalently bound to the gamma oxygen of the active site serine. The ester carbonyl shows no significant distortion from planarity, with the carbonyl oxygen forming one hydrogen bond with the oxyanion hole. The tripeptide is bound in an extended antiparallel beta-sheet with main chain residues of the enzyme. The geometry and interactions of this acyl-enzyme suggest that it represents a productive intermediate. To test this hypothesis, the same crystal was then warmed above the glass transition temperature and a second data set was collected. The resulting electron density map shows no sign of the substrate, indicating hydrolysis of the intermediate followed by product release. This experiment provides direct evidence for the importance of dynamic properties in catalysis and also provides a blueprint for the stabilization of other short-lived species for direct crystallographic observation.     

Symmetry breaking and convergent extension in early chordate development

The initiation of axis, polarity, cell differentiation, and gastrulation in the very early chordate development is due to the breaking of radial symmetry. It is believed that this occurs by an external signal. We suggest instead spontaneous symmetry breaking through the agency of the Turing-Child field. Increased size or decreased diffusivity, both brought about by mitotic activity, cause the spontaneous loss of stability of the homogeneous state and the evolution of the metabolic pattern during development. The polar metabolic pattern is the cause of polar gene expression, polar morphogenesis (gastrulation), and polar mitotic activity. The Turing-Child theory explains not only the spontaneous formation of the invagination in gastrulation but also the coherent cell movement observed in convergence and extension during gastrulation and neurulation. The theory is demonstrated with respect to experimental observations on the early development of fish, amphibian, and the chick. The theory can explain a multitude of experimental details. For example, it explains the splayed polar progression of reduction in the fish blastoderm. Reduction starts on that side of the blastoderm margin, which will initiate invagination several hours later. It progresses toward the blastoderm center and somewhat laterally from this future "dorsal lip". This is precisely as predicted by a Turing-Child system in a circle. And for a fish like zebrafish with a blastoderm that is slightly oval, reduction is observed to progress along the long axis of the ellipse, which is what Turing-Child theory predicts. In general the shape and the chemical nature of the experimental patterns are the same as predicted by the Turing couple (cAMP, ATP). Embryological polarity and convergent extension are based on polar eigenfunction and saddle-shaped eigenfunction, respectively.     

Electrolyte effects on the intervalence transition within discrete binuclear cyano-bridged complexes. An estimation of activation free energy from static, optical and electrochemical data

A study of the metal-to-metal charge-transfer (MMCT) transition within the binuclear cyano-bridged complexes cis-[L¹³Co^III(μ-NC)Fe^II(CN)₅]⁻ (L¹³ = 12-methyl-1,4,7,10-tetraazacyclotridecan-12-amine), trans-[L¹⁴Co^III(μ-NC)Fe^II(CN)₅]⁻ (L¹⁴ = 6-methyl-1,4,8,11-tetraazacyclotetradecan-6-amine) and trans-[L¹⁵Co^III(μ-NC)Fe^II(CN)₅]⁻ (L¹⁵ = 10-methyl-1,4,8,12-tetraazacyclopentadecan-10-amine) has been carried out in electrolyte solutions at varying concentrations. Using these data, as well as the reaction free energies obtained from electrochemical measurements, the reorganisation and activation free energies for the forward and reverse thermal electron-transfer processes have been estimated. The changes of these parameters with the electrolyte concentration, as well as those of the energy of the maximum MMCT band and the reaction free energy, are mainly due to ion-pairing effects.     

烧伤病房MRSA医院感染暴发的PFGE分型研究

利用表型分型和基因分型的方法解析医院感染常见致病菌——耐甲氧西林金黄色葡萄球菌(MRSA)造成医院感染暴发的可能传播途径。本实验对某医院烧伤科、ICU、肿瘤科病房内从患者和环境分离的19株MRSA,进行了16种抗生素的耐药性实验和全基因组稀有位点限制性内切酶酶切脉冲凝胶电泳(PFGE)分析,并聚类分析归纳了菌株之间的相关性。结果发现在19株MRSA中有11株属于同一个菌种A型,在这些菌株中,有8株属于相同的克隆亚型A1型,分别来自烧伤科和ICU患者以及烧伤科医生、护士的手。4株属于B型,均分离自同一烧伤病房。这暗示该医院可能存在MRSA(A型)院内感染的暴发,并且存在B型流行的潜在危险。MRSA很有可能通过医护工作人员的手及鼻腔等媒介在患者间传播。因此,加强医护人员的感染控制观念,利用灵敏、可靠且分辨率强的分型技术加强MRSA感染监控至关重要。     

Nonparametric estimation of stage occupation probabilities in a multistage model with current status data

Multistage models are used to describe individuals (or experimental units) moving through a succession of "stages" corresponding to distinct states (e.g., healthy, diseased, diseased with complications, dead). The resulting data can be considered to be a form of multivariate survival data containing information about the transition times and the stages occupied. Traditional survival analysis is the simplest example of a multistage model, where individuals begin in an initial stage (say, alive) and move irreversibly to a second stage (death). In this article, we consider general multistage models with a directed tree structure (progressive models) in which individuals traverse through stages in a possibly non-Markovian manner. We construct nonparametric estimators of stage occupation probabilities and marginal cumulative transition hazards. Empirical calculations of these quantities are not possible due to the lack of complete data. We consider current status information which represents a more severe form of censoring than the commonly used right censoring. Asymptotic validity of our estimators can be justified using consistency results for nonparametric regression estimators. Finite-sample behavior of our estimators is studied by simulation, in which we show that our estimators based on these limited data compare well with those based on complete data. We also apply our method to a real-life data set arising from a cardiovascular diseases study in Taiwan.     

A general approach for two-stage analysis of multilevel clustered non-Gaussian data

In this article, we propose a two-stage approach to modeling multilevel clustered non-Gaussian data with sufficiently large numbers of continuous measures per cluster. Such data are common in biological and medical studies utilizing monitoring or image-processing equipment. We consider a general class of hierarchical models that generalizes the model in the global two-stage (GTS) method for nonlinear mixed effects models by using any square-root-n-consistent and asymptotically normal estimators from stage 1 as pseudodata in the stage 2 model, and by extending the stage 2 model to accommodate random effects from multiple levels of clustering. The second-stage model is a standard linear mixed effects model with normal random effects, but the cluster-specific distributions, conditional on random effects, can be non-Gaussian. This methodology provides a flexible framework for modeling not only a location parameter but also other characteristics of conditional distributions that may be of specific interest. For estimation of the population parameters, we propose a conditional restricted maximum likelihood (CREML) approach and establish the asymptotic properties of the CREML estimators. The proposed general approach is illustrated using quartiles as cluster-specific parameters estimated in the first stage, and applied to the data example from a collagen fibril development study. We demonstrate using simulations that in samples with small numbers of independent clusters, the CREML estimators may perform better than conditional maximum likelihood estimators, which are a direct extension of the estimators from the GTS method.     

Optimization of two-stage genetic designs where data are combined using an accurate and efficient approximation for Pearson's statistic

There is an increasing interest in the use of two-stage case-control studies to reduce genotyping costs in the search for genes underlying common disorders. Instead of analyzing the data from the second stage separately, a more powerful test can be performed by combining the data from both stages. However, standard tests cannot be used because only the markers that are significant in the first stage are selected for the second stage and the test statistics at both stages are dependent because they partly involve the same data. Theoretical approximations are not available for commonly used test statistics and in this specific context simulations can be problematic because of the computational burden. We therefore derived a cost-effective, that is, accurate but fast in terms of central processing unit (CPU) time, approximation for the distribution of Pearson's statistic on 2 xm contingency tables in two-stage design with combined data. We included this approximation in an iterative method for designing optimal two-stage studies. Simulations supported the accuracy of our approximation. Numerical results confirmed that the use of two-stage designs reduces the genotyping burden substantially. Compared to not combining data, combining the data decreases the required sample sizes on average by 15% and the genotyping burden by 5%.     

A simple local sensitivity analysis tool for nonignorable coarsening: application to dependent censoring

Right- and interval-censored data are common special cases of coarsened data (Heitjan and Rubin, 1991, Annals of Statistics19, 2244-2253). As with missing data, standard statistical methods that ignore the random nature of the coarsening mechanism may lead to incorrect inferences. We extend a simple sensitivity analysis tool, the index of local sensitivity to nonignorability (Troxel, Ma, and Heitjan, 2004, Statistica Sinica14, 1221-1237), to the evaluation of nonignorability of the coarsening process in the general coarse-data model. By converting this index into a simple graphical display one can easily assess the sensitivity of key inferences to nonignorable coarsening. We illustrate the validity of the method with a simulated example, and apply it to right-censored data from an observational study of cardiac transplantation and to interval-censored data on time to detectable viral load from a clinical trial in HIV disease.