A nonparametric Bayesian modeling approach for cytogenetic dosimetry

In cytogenetic dosimetry, samples of cell cultures are exposed to a range of doses of a given agent. In each sample at each dose level, some measure of cell disability is recorded. The objective is to develop models that explain cell response to dose. Such models can be used to predict response at unobserved doses. More important, such models can provide inference for unknown exposure doses given the observed responses. Typically, cell disability is viewed as a Poisson count, but in the present work, a more appropriate response is a categorical classification. In the literature, modeling in this case is very limited. What exists is purely parametric. We propose a fully Bayesian nonparametric approach to this problem. We offer comparison with a parametric model through a simulation study and the analysis of a real dataset modeling blood cultures exposed to radiation where classification is with regard to number of micronuclei per cell.     

A Bayesian method for calculating real-time quantitative PCR calibration curves using absolute plasmid DNA standards

Background  

In real-time quantitative PCR studies using absolute plasmid DNA standards, a calibration curve is developed to estimate an unknown DNA concentration. However, potential differences in the amplification performance of plasmid DNA compared to genomic DNA standards are often ignored in calibration calculations and in some cases impossible to characterize. A flexible statistical method that can account for uncertainty between plasmid and genomic DNA targets, replicate testing, and experiment-to-experiment variability is needed to estimate calibration curve parameters such as intercept and slope. Here we report the use of a Bayesian approach to generate calibration curves for the enumeration of target DNA from genomic DNA samples using absolute plasmid DNA standards.     

A coalescence-guided hierarchical Bayesian method for haplotype inference

Haplotype inference from phase-ambiguous multilocus genotype data is an important task for both disease-gene mapping and studies of human evolution. We report a novel haplotype-inference method based on a coalescence-guided hierarchical Bayes model. In this model, a hierarchical structure is imposed on the prior haplotype frequency distributions to capture the similarities among modern-day haplotypes attributable to their common ancestry. As a consequence, the model both allows distinct haplotypes to have different a priori probabilities according to the inferred hierarchical ancestral structure and results in a proper joint posterior distribution for all the parameters of interest. A Markov chain-Monte Carlo scheme is designed to draw from this posterior distribution. By using coalescence-based simulation and empirically generated data sets (Whitehead Institute's inflammatory bowel disease data sets and HapMap data sets), we demonstrate the merits of the new method in comparison with HAPLOTYPER and PHASE, with or without the presence of recombination hotspots and missing genotypes.     

A Bayesian approach to nonlinear random effects models

Nonlinear random effects models are considered from the Bayesian point of view. The method of analysis follows closely that of Lindley and Smith (1972, Journal of the Royal Statistical Society, Series B 34, 1-42). The numerical method is related to the EM algorithm.     

Bayesian calibration method used to elucidate carbon turnover in forest on drained organic soil

Depending on the balance between sink and source processes for C, drained organic forest soil ecosystems can be in balance or act as net sinks or sources of CO₂ to the atmosphere. In order to study the effect of groundwater level and soil temperature on C-flux, the CoupModel was calibrated (climate data, groundwater levels, soil CO₂ flux, net ecosystem fluxes of CO₂-exchange, sensible heat flux and latent heat flux, forest production etc.) for a drained forest in Sweden. Bayesian calibration techniques were used to elucidate how different parameters and variables were interlinked in C-circulation. The calibrated model reproduced abiotic and biotic variables reasonably well except for root respiration, which was largely underestimated. Bayesian calibration reduced the uncertainties in the model and highlighted the fact that calibrations should be performed with a high number of parameters instead of specific parameter values.     

A hierarchical Bayesian model for next-generation population genomics

The demography of populations and natural selection shape genetic variation across the genome and understanding the genomic consequences of these evolutionary processes is a fundamental aim of population genetics. We have developed a hierarchical Bayesian model to quantify genome-wide population structure and identify candidate genetic regions affected by selection. This model improves on existing methods by accounting for stochastic sampling of sequences inherent in next-generation sequencing (with pooled or indexed individual samples) and by incorporating genetic distances among haplotypes in measures of genetic differentiation. Using simulations we demonstrate that this model has a low false-positive rate for classifying neutral genetic regions as selected genes (i.e., Φ(ST) outliers), but can detect recent selective sweeps, particularly when genetic regions in multiple populations are affected by selection. Nonetheless, selection affecting just a single population was difficult to detect and resulted in a high false-negative rate under certain conditions. We applied the Bayesian model to two large sets of human population genetic data. We found evidence of widespread positive and balancing selection among worldwide human populations, including many genetic regions previously thought to be under selection. Additionally, we identified novel candidate genes for selection, several of which have been linked to human diseases. This model will facilitate the population genetic analysis of a wide range of organisms on the basis of next-generation sequence data.     

A multiorganism based method for Bayesian gene network estimation

The primary goal of this article is to infer genetic interactions based on gene expression data. A new method for multiorganism Bayesian gene network estimation is presented based on multitask learning. When the input datasets are sparse, as is the case in microarray gene expression data, it becomes difficult to separate random correlations from true correlations that would lead to actual edges when modeling the gene interactions as a Bayesian network. Multitask learning takes advantage of the similarity between related tasks, in order to construct a more accurate model of the underlying relationships represented by the Bayesian networks. The proposed method is tested on synthetic data to illustrate its validity. Then it is iteratively applied on real gene expression data to learn the genetic regulatory networks of two organisms with homologous genes.     

A hierarchical Bayesian approach for handling missing classification data

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A collaborative exercise on cytogenetic dosimetry for simulated whole and partial body accidental irradiation

An experiment sponsored by the International Atomic Energy Agency was undertaken to compare dose estimation by cytogenetic analysis on aliquots of samples of irradiated blood sent by air to participating laboratories. Accidental acute whole-body irradiations to 0.7 and 2.34 Gy and half-body irradiations to 3.5 Gy were simulated with X- and gamma-rays. For the partial irradiations the size of the irradiated fraction and its dose were estimated by the Qdr and contaminated Poisson techniques. Each laboratory's in vitro dose-response data were fitted to the quadratic model by the iteratively reweighted least squares method. Interlaboratory variations in dose-response curves, and in the aberration yields and dose estimates for the simulated accidents were noted. However, in general, most participants consistently obtained results acceptably close to the true values.     

A Bayesian hierarchical model for identifying epitopes in peptide microarray data

Peptide Microarray Immunoassay (PMI for brevity) is a novel technology that enables researchers to map a large number of proteomic measurements at a peptide level, providing information regarding the relationship between antibody response and clinical sensitivity. PMI studies aim at recognizing antigen-specific antibodies from serum samples and at detecting epitope regions of the protein antigen. PMI data present new challenges for statistical analysis mainly due to the structural dependence among peptides. A PMI is made of a complete library of consecutive peptides. They are synthesized by systematically shifting a window of a fixed number of amino acids through the finite sequence of amino acids of the antigen protein as ordered in the primary structure of the protein. This implies that consecutive peptides have a certain number of amino acids in common and hence are structurally dependent. We propose a new flexible Bayesian hierarchical model framework, which allows one to detect recognized peptides and bound epitope regions in a single framework, taking into account the structural dependence between peptides through a suitable latent Markov structure. The proposed model is illustrated using PMI data from a recent study about egg allergy. A simulation study shows that the proposed model is more powerful and robust in terms of epitope detection than simpler models overlooking some of the dependence structure.     

A novel validation and calibration method for motion capture systems based on micro-triangulation

Motion capture systems are widely used to measure human kinematics. Nevertheless, users must consider system errors when evaluating their results. Most validation techniques for these systems are based on relative distance and displacement measurements. In contrast, our study aimed to analyse the absolute volume accuracy of optical motion capture systems by means of engineering surveying reference measurement of the marker coordinates (uncertainty: 0.75 mm). The method is exemplified on an 18 camera OptiTrack Flex13 motion capture system. The absolute accuracy was defined by the root mean square error (RMSE) between the coordinates measured by the camera system and by engineering surveying (micro-triangulation). The original RMSE of 1.82 mm due to scaling error was managed to be reduced to 0.77 mm while the correlation of errors to their distance from the origin reduced from 0.855 to 0.209. A simply feasible but less accurate absolute accuracy compensation method using tape measure on large distances was also tested, which resulted in similar scaling compensation compared to the surveying method or direct wand size compensation by a high precision 3D scanner. The presented validation methods can be less precise in some respects as compared to previous techniques, but they address an error type, which has not been and cannot be studied with the previous validation methods.     

A hierarchical Bayesian approach to modeling embryo implantation following in vitro fertilization

In vitro fertilization and embryo transfer (IVF-ET) is considered a method of last resort for treating infertility. Oocytes taken from a woman are fertilized in vitro, and one or more resulting embryos are transferred into the uterus, with the hope that at least one will implant and result in pregnancy. Successful implantation depends on both embryo viability and uterine receptivity. This has led to the development of the EU model for embryo implantation, wherein uterine receptivity is characterized by a latent binary variable U and embryo viability is characterized by a latent binomial variable E representing the number of viable embryos among those selected for transfer. The observed number of implantations is the product of E and U. Zhou and Weinberg (1998) developed a regression formulation of the EU model in which embryo viabilities are independent within patients. We extend their methodology to a Bayesian hierarchical framework that allows for correlation between the embryo viabilities and gives explicit characterization of patient-level heterogeneity. When some subjects have zero implantations, the likelihood for the hierarchical EU model is relatively flat and therefore using prior information for key parametersis needed. This provides a key motivation for adopting a Bayesian approach. The model is used to assess the effect of hydrosalpinx on embryo implantation in a cohort of 288 women undergoing IVF-ET because of tubal disease. Hydrosalpinx is a build-up of fluid in the Fallopian tubes, which sometimes leaks to the uterus and may reduce the likelihood of implantation. The EU model is well suited to this question because hydrosalpinx is thought to affect implantation by reducing uterine receptivity only. Our analysis indicates substantial subject-level heterogeneity with respect to embryo viability, suggesting the utility of a multi-level model.     

Dose curves for radiation-induced cytogenetic damage in human lymphocytes

Insufficient fitness of experimental data with convenient linear and linear-quadratic models of "dose-effect" dependence for the number of chromosome aberrations of different types were revealed. Proposed method of approximation of the experimental "dose-effect" dependence with piece-linear splines allows to obtain more accurate results in the most cases and therefore is more preferable than the linear and linear-quadratic models.     

A Bayesian hierarchical approach for combining case-control and prospective studies

Motivated by the absolute risk predictions required in medical decision making and patient counseling, we propose an approach for the combined analysis of case-control and prospective studies of disease risk factors. The approach is hierarchical to account for parameter heterogeneity among studies and among sampling units of the same study. It is based on modeling the retrospective distribution of the covariates given the disease outcome, a strategy that greatly simplifies both the combination of prospective and retrospective studies and the computation of Bayesian predictions in the hierarchical case-control context. Retrospective modeling differentiates our approach from most current strategies for inference on risk factors, which are based on the assumption of a specific prospective model. To ensure modeling flexibility, we propose using a mixture model for the retrospective distributions of the covariates. This leads to a general nonlinear regression family for the implied prospective likelihood. After introducing and motivating our proposal, we present simple results that highlight its relationship with existing approaches, develop Markov chain Monte Carlo methods for inference and prediction, and present an illustration using ovarian cancer data.