含不可忽略缺失数据非线性再生散度模型参数的Bayes估计

给出协变量带有不可忽略缺失数据的非线性再生散度模型的Bayes方法,缺失数据机制由Logistic回归模型来确定.Gibbs抽样技术和Metropolis-Hastings算法(简称MH算法)用来得到模型参数、缺失数据机制中回归系数的联合Bayes估计,并用实例加以说明.     

DNA提取过程中混入的实验室空气微生物DNA定量

元基因组测序方法为微生物研究提供了有力的工具。但其中的DNA提取过程,会不可避免地混入实验室中的空气微生物。这些微生物DNA,是否会对一些极微量的元基因组检测 (如皮肤样本等) 结果造成影响,有多大影响,仍没有明确结论。本研究首先收集了实验室空气样品,用16S rRNA引物建立了基于qPCR的标准曲线,并检测了在开放环境下提取DNA过程中可掺杂的环境微生物DNA量。然后在开放环境下提取纯水DNA样品并进行元基因组分析,以确定掺杂环境微生物的种类。最后分别在生物安全柜和实验室开放环境下提取皮肤样本,并用鸟枪测序方法对样本的微生物组成进行分析,以评估掺杂环境微生物对元基因组检测结果的影响。结果显示,在实验室开放环境的DNA提取过程中,环境微生物的DNA残留可达28.9 pg,可达某些极微量样本DNA总量的30%。元基因组分析显示,样品中掺杂的环境微生物主要是痤疮杆菌Cutibacterium acnes、大肠杆菌Escherichia coli等皮肤常见细菌。与洁净皮肤样本的信息相比,开放环境下提取掺杂了数十种环境微生物,并导致主要菌种的丰度大幅降低,从而影响结果的真实性。因此,微量样品的DNA提取应在洁净环境下执行。     

基于三链核酸的DNA计算

一种研究DNA计算的新模型——三链DNA计算模型在本文中提出。此模型是在近年三链核酸的研究成果的基础上建立的。并应用于求解可满足性问题（SAT）,这是一个困难的NP-完全问题。不同于以住的DNA计算方法,基于三链核酸的分子算法通过寡聚脱氧核苷酸（ODN）在RecA蛋白的介导下与同源的双链DNA匹配成三链DNA进行基本的运算,这样可以有效减少计算中的错误。依据分子生物学的实验方法,该算法切实可行并且有效。     

基因组水平的插入和缺失变异研究进展

插入和缺失(insertion and deletion)是DNA和蛋白质在进化过程中发生的序列长度上的改变,由于缺乏祖先序列的信息,不能肯定其到底是插入事件还是缺失事件,故统称之为增减(indel)。indel是分子水平进化变异的主要来源之一,近年来对这种进化事件的研究已经涵盖了其发生频率、大小、分布模式、序列进化模型及应用等各个方面。该文总结了基因组水平上插入和缺失的研究进展和发生机制;介绍了已经提出的插入和缺失进化模型,包括TKF91、TKF92、"Long Indel"模型和序列环境模型;讨论了插入和缺失作为分子标记在分子进化、基因分型和药物设计等方面的应用。     

在枯草杆菌中有启动子功能的噬菌体T5 DNA片段的克隆

用启动子探针质粒pTG 402为载体,用鸟枪射击法克隆了噬菌体T5 DNA的片段。克隆中的2％含有具启动子功能的插入片段,其中pTG 402-20含有启动功能最强的插入片段。DNA-DNA分子杂交实验结果表明,pTG 402-20的插入片段能与T5 DNA Hind Ⅲ酶切的G和B片段杂交。这个插入片段的大小约为0.84kb。     

基因敲入技术的突破

Baylor医学院的KoenVenken博士创新了基因敲入技术，这一突破使生物学家向果蝇体内敲入大量的DNA成为可能。传统的方法只能在果蝇基因组中插入较小的DNA片段，Venken的方法可将20000到133000大小碱基对的DNA敲入果蝇基因组。传统上，生物学家利用果蝇基因组中的P元素能够整合外源性DNA片段的特性，将目标DNA片段插入P元素，再将含有外源性DNA的P元素复合体导入果蝇基因组。这一传统方法能整合的DNA片段长度有限。Venken在研究中需要敲入很长的DNA片段到果蝇体内。于是他将含有DNA片段的P元素转入到质粒里，质粒能够较P元素自身更稳定地携带大片断DNA。     

CART算法在原发性肝癌放疗后HBV再激活的应用

为了建立乙型肝炎病毒(Hepatitis B virus,HBV)再激活的预测模型,提出CART(classification and regression tree)特征选择方法应用在原发性肝癌患者精确放疗后HBV再激活的危险因素分析中,进而建立基于CART和Bayes算法的HBV再激活预测模型。实验结果显示:CART算法划分了多组具有优秀分类能力的特征节点集(危险因素),尤其当特征节点集为HBV DNA水平、外放边界、放疗总剂量、V20和KPS评分时,在CART和Bayes预测模型中的分类正确性分别为88.51%和86.69%,得到HBV再激活正确性贡献度的排序为KPS评分全肝平均剂量V20放疗总剂量V10;当甲胎蛋白AFP出现时,增加了HBV再激活的预测正确性。     

地衣型真菌的I型内含子及其在系统发育中的应用

以蜈蚣衣属、黑蜈蚣衣属地衣样品为材料,结合GenBank中相关数据,对地衣型真菌核糖体小亚基 DNA上的I型内含子分布模式进行归纳,并探讨了其在地衣型真菌系统发育研究中的应用。结果表明在地衣型真菌核糖体小亚基 DNA上存在多个I型内含子插入位点,通过二级结构分析给出了天然状态下I型内含子发生转座的证据。分析显示,I型内含子作为分子标记,只适合用于种下单位的系统发育研究中。     

基于改进投影寻踪的海洋生态环境综合评价

为了克服现有的海洋环境评价中存在的主观性强、不易处理高维数据的缺陷,提出了基于改进投影寻踪模型的海洋环境评价新方法.该方法利用改进蚁群算法实现了投影寻踪技术,将方案的多维评价指标值投影为一维投影数据,并据投影值大小对样本进行综合评价.工程应用实例表明,该模型易于决策,具有很强的客观性、适用性和可操作性,为海洋生态环境评价提供了新的技术工具.     

河北省小麦白粉病发生气象等级动态预警

根据河北省4县2001—2010年小麦白粉病病情和逐日气象资料,采用因子膨化、秩相关分析、通径分析、Bayes准则、模糊数学(Fuzzy)和广义回归神经网络(GRNN)等方法,筛选影响小麦白粉病发生的关键期和关键因子,建立了小麦白粉病发生气象等级指标模型、基于Bayes准则的Fuzzy模型和基于Fuzzy模型的GRNN模型。结果表明:影响河北4县小麦白粉病发生气象等级的关键因子是前三候至当候的平均温度、前三候至当候的降水量、前三候至当候的降雨系数和前一候的小麦白粉病实际发生等级;3种预警模型具有层层递进的关系,预报准确率基于Fuzzy模型的GRNN模型基于Bayes准则的Fuzzy模型指标模型,并均超过了85%,可以用于对候尺度小麦白粉病发生等级进行中短期预报。     

Bayesian hierarchical models combining different study types and adjusting for covariate imbalances: a simulation study to assess model performance

Background

Bayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between study arms may bias the results. The objective of this study was to assess the performance of a proposed Bayesian approach to adjust for imbalances in patient level covariates when combining evidence from both types of study designs.

Methodology/Principal Findings

Simulation techniques, in which the truth is known, were used to generate sets of data for randomised and non-randomised studies. Covariate imbalances between study arms were introduced in the non-randomised studies. The performance of the Bayesian hierarchical model adjusted for imbalances was assessed in terms of bias. The data were also modelled using three other Bayesian approaches for synthesising evidence from randomised and non-randomised studies. The simulations considered six scenarios aimed at assessing the sensitivity of the results to changes in the impact of the imbalances and the relative number and size of studies of each type. For all six scenarios considered, the Bayesian hierarchical model adjusted for differences within studies gave results that were unbiased and closest to the true value compared to the other models.

Conclusions/Significance

Where informed health care decision making requires the synthesis of evidence from randomised and non-randomised study designs, the proposed hierarchical Bayesian method adjusted for differences in patient characteristics between study arms may facilitate the optimal use of all available evidence leading to unbiased results compared to unadjusted analyses.     

Building food networks from molecular data: Bayesian or fixed-number thresholds for including links

DNA metabarcoding of faeces or gut contents has greatly increased our ability to construct networks of predators and prey (food webs) by reducing the need to observe predation events directly. The possibility of both false positives and false negatives in DNA sequences, however, means that constructing food networks using DNA requires researchers to make many choices as to which DNA sequences indicate true prey for a particular predator. To date, DNA-based food networks are usually constructed by including any DNA sequence with more than a threshold number of reads. The logic used to select this threshold is often not explained, leading to somewhat arbitrary-seeming networks. As an alternative strategy, we demonstrate how to construct food networks using a simple Bayesian model to suggest which sequences correspond to true prey. The networks obtained using a well-chosen fixed cutoff and our Bayesian approach are very similar, especially when links are resolved to prey families rather than species. We therefore recommend that researchers reconstruct diet data using a Bayesian approach with well-specified assumptions rather than continuing with arbitrary fixed cutoffs. Explicitly stating assumptions within a Bayesian framework will lead to better-informed comparisons between networks constructed by different groups and facilitate drawing together individual case studies into more coherent ecological theory. Note that our approach can easily be extended to other types of ecological networks constructed by DNA metabarcoding of pollen loads, identification of parasite DNA in faeces, etc.     

A straightforward multiallelic significance test for the Hardy-Weinberg equilibrium law

Much forensic inference based upon DNA evidence is made assuming Hardy-Weinberg Equilibrium (HWE) for the genetic loci being used. Several statistical tests to detect and measure deviation from HWE have been devised, and their limitations become more obvious when testing for deviation within multiallelic DNA loci. The most popular methods-Chi-square and Likelihood-ratio tests-are based on asymptotic results and cannot guarantee a good performance in the presence of low frequency genotypes. Since the parameter space dimension increases at a quadratic rate on the number of alleles, some authors suggest applying sequential methods, where the multiallelic case is reformulated as a sequence of "biallelic" tests. However, in this approach it is not obvious how to assess the general evidence of the original hypothesis; nor is it clear how to establish the significance level for its acceptance/rejection. In this work, we introduce a straightforward method for the multiallelic HWE test, which overcomes the aforementioned issues of sequential methods. The core theory for the proposed method is given by the Full Bayesian Significance Test (FBST), an intuitive Bayesian approach which does not assign positive probabilities to zero measure sets when testing sharp hypotheses. We compare FBST performance to Chi-square, Likelihood-ratio and Markov chain tests, in three numerical experiments. The results suggest that FBST is a robust and high performance method for the HWE test, even in the presence of several alleles and small sample sizes.     

A Decision Rule for Quantitative Trait Locus Detection Under the Extended Bayesian LASSO Model

Bayesian shrinkage analysis is arguably the state-of-the-art technique for large-scale multiple quantitative trait locus (QTL) mapping. However, when the shrinkage model does not involve indicator variables for marker inclusion, QTL detection remains heavily dependent on significance thresholds derived from phenotype permutation under the null hypothesis of no phenotype-to-genotype association. This approach is computationally intensive and more importantly, the hypothetical data generation at the heart of the permutation-based method violates the Bayesian philosophy. Here we propose a fully Bayesian decision rule for QTL detection under the recently introduced extended Bayesian LASSO for QTL mapping. Our new decision rule is free of any hypothetical data generation and relies on the well-established Bayes factors for evaluating the evidence for QTL presence at any locus. Simulation results demonstrate the remarkable performance of our decision rule. An application to real-world data is considered as well.     

A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables

The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering.     

Genome-wide evaluation for quantitative trait loci under the variance component model

The identity-by-descent (IBD) based variance component analysis is an important method for mapping quantitative trait loci (QTL) in outbred populations. The interval-mapping approach and various modified versions of it may have limited use in evaluating the genetic variances of the entire genome because they require evaluation of multiple models and model selection. In this study, we developed a multiple variance component model for genome-wide evaluation using both the maximum likelihood (ML) method and the MCMC implemented Bayesian method. We placed one QTL in every few cM on the entire genome and estimated the QTL variances and positions simultaneously in a single model. Genomic regions that have no QTL usually showed no evidence of QTL while regions with large QTL always showed strong evidence of QTL. While the Bayesian method produced the optimal result, the ML method is computationally more efficient than the Bayesian method. Simulation experiments were conducted to demonstrate the efficacy of the new methods.     

FGX: a frequentist gene expression index for Affymetrix arrays

We consider a new frequentist gene expression index for Affymetrix oligonucleotide DNA arrays, using a similar probe intensity model as suggested by Hein and others (2005), called the Bayesian gene expression index (BGX). According to this model, the perfect match and mismatch values are assumed to be correlated as a result of sharing a common gene expression signal. Rather than a Bayesian approach, we develop a maximum likelihood algorithm for estimating the underlying common signal. In this way, estimation is explicit and much faster than the BGX implementation. The observed Fisher information matrix, rather than a posterior credibility interval, gives an idea of the accuracy of the estimators. We evaluate our method using benchmark spike-in data sets from Affymetrix and GeneLogic by analyzing the relationship between estimated signal and concentration, i.e. true signal, and compare our results with other commonly used methods.     

Comparing Families of Dynamic Causal Models

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.     

On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread

Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.     

The hierarchical metaregression approach and learning from clinical evidence

The hierarchical metaregression (HMR) approach is a multiparameter Bayesian approach for meta‐analysis, which generalizes the standard mixed effects models by explicitly modeling the data collection process in the meta‐analysis. The HMR allows to investigate the potential external validity of experimental results as well as to assess the internal validity of the studies included in a systematic review. The HMR automatically identifies studies presenting conflicting evidence and it downweights their influence in the meta‐analysis. In addition, the HMR allows to perform cross‐evidence synthesis, which combines aggregated results from randomized controlled trials to predict effectiveness in a single‐arm observational study with individual participant data (IPD). In this paper, we evaluate the HMR approach using simulated data examples. We present a new real case study in diabetes research, along with a new R package called jarbes (just a rather Bayesian evidence synthesis), which automatizes the complex computations involved in the HMR.