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Haplotyping and estimation of haplotype frequencies for closely linked biallelic multilocus genetic phenotypes including nuclear family information

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HUMAN MUTATION 2001年第4期17卷 289-295页

作者： Rohde, K Fuerst, R Max Delbruck Ctr Mol Med D-13092 Berlin Germany German Resource Ctr Genome Res Berlin Germany

With the discovery of single nucleotide polymorphisms (SNP) along the genome, genotyping of large samples of biallelic multilocus genetic phenotypes for (fine) mapping of disease genes or for population studies has become standard practice. A genetic trait, however, is mainly caused by an underlying defective haplotype, and populations are best characterized by their haplotype frequencies. Therefore, it is essential to infer from the phase unknown genetic phenotypes in a sample drawn from a population the haplotype frequencies in the population and the underlying haplotype pairs in the sample in order to find disease predisposing genes by some association or haplotype sharing algorithm. Haplotype frequencies and haplotype pairs are estimated via a maximum likelihood approach by a well-known expectation maximization (EM) algorithm, adapting it to a large number (up to 30) of biallelic loci (SNP), and including nuclear family information, if available, into the analysis. Parents are treated as an independent sample from the population. Their genotyped offspring reduces the number of potential haplotype pairs for both parents, resulting in a higher accuracy of the estimation, and may also reduce computation time. In a series of simulations our approach of including nuclear family information has been tested against both the EM algorithm without nuclear family information and an alternative approach using GENEHUNTER for the haplotyping of the families, using the locus-by-locus allele counts of the sample. Our new approach is more precise in haplotyping in cases of a high number of heterozygous loci, whereas for a moderate number of heterozygous positions in the sample all three different approaches gave the same perfect results. Hum Mutat 17:289-295, 2001. (C) zool Wiley-Liss, Inc.

关键词： SNP haplotype frequency estimation expectation maximization algorithm nuclear family information haplotyping

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Adaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain

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JOURNAL OF NEUROSCIENCE METHODS 2013年第1期212卷 43-55页

作者： Kim, Sun Hyung Fonov, Vladimir S. Dietrich, Cheryl Vachet, Clement Hazlett, Heather C. Smith, Rachel G. Graves, Michael M. Piven, Joseph Gilmore, John H. Dager, Stephen R. McKinstry, Robert C. Paterson, Sarah Evans, Alan C. Collins, D. Louis Gerig, Guido Styner, Martin Andreas Univ N Carolina Dept Psychiat Chapel Hill NC 27515 USA McGill Univ Montreal Neurol Inst McConnell Brain Imaging Ctr Montreal PQ Canada Univ Utah Sci Comp & Imaging Inst Sch Comp Salt Lake City UT USA Univ N Carolina Dept Comp Sci Chapel Hill NC USA Univ Washington Dept Radiol Seattle WA 98195 USA Washington Univ Dept Radiol St Louis MO USA Childrens Hosp Philadelphia Dept Pediat Philadelphia PA 19104 USA

The degree of white matter (WM) myelination is rather inhomogeneous across the brain. White matter appears differently across the cortical lobes in MR images acquired during early postnatal development. Specifically at 1-year of age, the gray/white matter contrast of MRT1 and T2 weighted images in prefrontal and temporal lobes is reduced as compared to the rest of the brain, and thus, tissue segmentation results commonly show lower accuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and 12 weighted images to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2 years of age, as appearance homogeneity is greatly improved by the age of 24 months. The IGM was computed as the coefficient of a voxel-wise linear regression model between corresponding intensities at 1 and 2 years. The proposed IGM method revealed low regression values of 1-10% in GM and CSF regions, as well as in WM regions at maturation stage of myelination at 1 year. However, in the prefrontal and temporal lobes we observed regression values of 20-25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes mainly due to myelination. The IGM is applied to cross-sectional MRI datasets of 1-year-old subjects via registration, correction and tissue segmentation of the IGM-corrected dataset. We validated our approach in a small leave-one-out study of images with known, manual 'ground truth' segmentations. Published by Elsevier B.V.

关键词： Myelination expectation maximization algorithm Tissue segmentation Intensity growth map Partial volume estimation

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Adaptive Rejection Metropolis Simulated Annealing for Detecting Global Maximum Regions

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METHODOLOGY AND COMPUTING IN APPLIED PROBABILITY 2016年第1期18卷 1-19页

作者： Zhang, Huaiye Kim, Inyoung Virginia Polytech Inst & State Univ Dept Stat Blacksburg VA 24061 USA

A finite mixture model has been used to fit the data from heterogeneous populations to many applications. An expectation maximization (EM) algorithm is the most popular method to estimate parameters in a finite mixture model. A Bayesian approach is another method for fitting a mixture model. However, the EM algorithm often converges to the local maximum regions, and it is sensitive to the choice of starting points. In the Bayesian approach, the Markov Chain Monte Carlo (MCMC) sometimes converges to the local mode and is difficult to move to another mode. Hence, in this paper we propose a new method to improve the limitation of EM algorithm so that the EM can estimate the parameters at the global maximum region and to develop a more effective Bayesian approach so that the MCMC chain moves from one mode to another more easily in the mixture model. Our approach is developed by using both simulated annealing (SA) and adaptive rejection metropolis sampling (ARMS). Although SA is a well-known approach for detecting distinct modes, the limitation of SA is the difficulty in choosing sequences of proper proposal distributions for a target distribution. Since ARMS uses a piecewise linear envelope function for a proposal distribution, we incorporate ARMS into an SA approach so that we can start a more proper proposal distribution and detect separate modes. As a result, we can detect the maximum region and estimate parameters for this global region. We refer to this approach as ARMS annealing. By putting together ARMS annealing with the EM algorithm and with the Bayesian approach, respectively, we have proposed two approaches: an EM-ARMS annealing algorithm and a Bayesian-ARMS annealing approach. We compare our two approaches with traditional EM algorithm alone and Bayesian approach alone using simulation, showing that our two approaches are comparable to each other but perform better than EM algorithm alone and Bayesian approach alone. Our two approaches detect the global maxi

关键词： Adaptive rejection metropolis sampling expectation maximization algorithm Finite mixture models Simulated annealing

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Power exponential densities for the training and classification of acoustic feature vectors in speech recognition

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2001年第1期10卷 158-184页

作者： Basu, S Micchelli, CA Olsen, P IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA SUNY Albany Dept Math & Stat Albany NY 12222 USA

We consider a parametric family of multivariate density functions formed by mixture models from univariate functions of the type exp(-\x\(alpha)) for modeling acoustic feature vectors used in automatic recognition of speech. The parameter alpha is used to measure the non-Gaussian nature of the data. Previous work has focused on estimating the mean and the variance of the data for a fixed alpha. Here we attempt to estimate the alpha from the data using a maximum likelihood criterion. Among other things, we show that there is a balance between a and the number of data points N that must be satisfied for efficient estimation. Numerical experiments are performed on multidimensional vectors obtained from speech data.

关键词： digamma function expectation maximization algorithm Laplacian densities large vocabulary continuous speech recognition univariate and multivariate density estimation

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MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes

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GENOME BIOLOGY 2014年第8期15卷 419-419页

作者： Zheng, Xiaoqi Zhao, Qian Wu, Hua-Jun Li, Wei Wang, Haiyun Meyer, Clifford A. Qin, Qian Alvin Xu, Han Zang, Chongzhi Jiang, Peng Li, Fuqiang Hou, Yong He, Jianxing Wang, Jun Wang, Jun Zhang, Peng Zhang, Yong Liu, Xiaole Shirley Shanghai Normal Univ Dept Math Shanghai Peoples R China Dana Farber Canc Inst Dept Biostat & Computat Biol Boston MA 02115 USA Harvard Univ Sch Publ Hlth Boston MA 02115 USA Tongji Univ Sch Life Sci & Technol Dept Bioinformat Shanghai 200092 Peoples R China Tongji Univ Adv Inst Shanghai 200092 Peoples R China Dana Farber Canc Inst Ctr Funct Canc Epigenet Boston MA 02115 USA BGI Shenzhen Shenzhen Peoples R China Guangzhou Med Univ Affiliated Hosp 1 Guangzhou Guangdong Peoples R China Univ Copenhagen Dept Biol DK-2200 Copenhagen Denmark King Abdulaziz Univ Princess Al Jawhara Ctr Excellence Res Hereditary Jeddah 21589 Saudi Arabia Macau Univ Sci & Technol Macau 999078 Peoples R China Univ Hong Kong Dept Med Hong Kong Hong Kong Peoples R China Tongji Univ Shanghai Pulm Hosp Sch Med Dept Thorac Surg Shanghai 200092 Peoples R China

We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. MethylPurify can identify differentially methylated regions (DMRs) from individual tumor methylome samples, without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. From patient data, MethylPurify gave satisfactory DMR calls from tumor methylome samples alone, and revealed potential missed DMRs by tumor to normal comparison due to tumor heterogeneity.

关键词： Methylation Level expectation maximization algorithm Differential Methylation Human Mammary Epithelial Cell Methylation Difference

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Doubly selective channel estimation for subband OFDMA using basis expansion models

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Science China(Information Sciences) 2011年第10期54卷 2113-2123页

作者： CHEN DongHua & QIU HongBing School of Telecommunications Engineering,Xidian University,Xi’an 710071,China 1. School of Telecommunications Engineering Xidian University Xi’an 710071 China

This letter considers the channel estimation for subband orthogonal frequency division multiple access (OFDMA) operating over highly time-frequency selective (i.e.,doubly selective) *** on general basis expansion model,two expectation maximization-type channel estimation methods are proposed for subband OFDMA in order to overcome channel time-frequency *** proposed method is compared with conventional approach and is shown to be superior to the conventional one.

关键词： orthogonal frequency division multiple access channel estimation basis expansion model expectation maximization algorithm time-frequency selectivity

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An efficient estimation approach to joint modeling of longitudinal and survival data

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JOURNAL OF APPLIED STATISTICS 2023年第15期50卷 3031-3047页

作者： Krahn, Jody Hossain, Shakhawat Khan, Shahedul Univ Winnipeg Dept Math & Stat Winnipeg MB R3B 2E9 Canada Univ Saskatchewan Dept Math & Stat Saskatoon SK Canada

The joint models for longitudinal and survival data have recently received significant attention in medical and epidemiological studies. Joint models typically combine linear mixed effects models for repeated measurement data and Cox models for survival time. When we are jointly modeling the longitudinal and survival data, variable selection and efficient estimation of parameters are especially important for performing reliable statistical analyzes, both of which are currently lacking in the literature. In this paper we discuss the pretest and shrinkage estimation methods for jointly modeling longitudinal data and survival time data when some of the covariates in both longitudinal and survival components may not be relevant for predicting survival times. In this situation, we fit two models: the full model that contains all the covariates and the subset model that contains a reduced number of covariates. We combine the full model estimators and the estimators that are restricted by a linear hypothesis to define pretest and shrinkage estimators. We provide their numerical mean squared errors (MSE) and relative MSE. We show that if the shrinkage dimension exceeds two, the risk of the shrinkage estimators is strictly less than that of the full model estimators. Our proposed methods are illustrated by extensive simulation studies and a real-data example.

关键词： expectation maximization algorithm maximum likelihood joint modeling longitudinal and survival data pretest and shrinkage estimators

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Improving Community Detection in Time-Evolving Networks Through Clustering Fusion

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CYBERNETICS AND INFORMATION TECHNOLOGIES 2015年第2期15卷 63-74页

作者： Jin, Ran Kou, Chunhai Liu, Ruijuan Donghua Univ Sch Informat Sci & Technol Shanghai 201620 Peoples R China Zhejiang Wanli Univ Sch Comp Sci & Informat Technol Ningbo 315100 Zhejiang Peoples R China Donghua Univ Sch Sci Shanghai 201620 Peoples R China

Traditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to improve the results of community detection. Usually, there are two issues in clustering ensembles: how to generate efficient diversified cluster members, and how to ensembles the results of all members. Specifically: (1) considering the time evolving characteristic of real world networks, we propose to generate clustering members based on the snapshot of networks, where the split based clustering algorithms are performed;(2) considering the difference in the distribution of the cluster centers in each clustering member and the actual distribution, we ensemble the results based on a maximum likelihood method. Moreover, we conduct experiments to show that our method can discover high quality communities.

关键词： Time-evolving network community detection clustering fusion network snapshot maximum likelihood method expectation maximization algorithm

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A two-component nonlinear mixed effects model for longitudinal data, with application to gastric emptying studies

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STATISTICS IN MEDICINE 2010年第17期29卷 1839-1856页

作者： Kim, Inyoung Cohen, Noah D. Roussel, Allen Wang, Naisyin Virginia Polytech Inst & State Univ Dept Stat Blacksburg VA 24061 USA Texas A&M Univ Coll Vet Med Dept Large Anim Clin Sci College Stn TX 77843 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA

Gastric emptying studies are of great interest in human and veterinary medical research to evaluate effects of medications or diets for promoting gastrointestinal motility and to examine unintended side-effects of new or existing medications, diets, or procedures. Summarizing gastric emptying data is important to allow easier comparison between treatments or groups of subjects and comparisons of results among studies. The standard method for assessing gastric emptying is by using scintigraphy and summarizing the nonlinear emptying of the radioisotope. A popular model for fitting gastric emptying data is the power exponential model. This model can only describes a globally decreasing pattern and thus has the limitation of poorly describing localized intragastric events that can occur during emptying. Hence, we develop a new model for gastric emptying studies to improve population and individual inferences using a mixture of nonlinear mixed effects models. One mixture component is based on a power exponential model which captures globally decreasing patterns. The other is based on a locally extended power exponential model which captures both local bumping and rapid decay. We refer to this mixture model as a two-component nonlinear mixed effects model. The parameters in our model have clear graphical interpretations that provide a more accurate representation and summary of the curves of gastric emptying pattern. Two methods are developed to fit our proposed model: one is the mixture of an expectation maximization algorithm and a global two-stage method and the other is the mixture of an expectation maximization algorithm and the Monte Carlo expectation maximization algorithm. We compare our methods using simulation, showing that the two approaches are comparable to one another. For estimating the variance and covariance matrix, the second approach appears approximately more efficient and is also numerically more stable in some cases. Our new model and approaches are

关键词： expectation maximization algorithm global two-stage method random coefficient model

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Role Analysis in Networks Using Mixtures of Exponential Random Graph Models

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JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2015年第2期24卷 520-538页

作者： Salter-Townshend, Michael Murphy, Thomas Brendan Univ Oxford Dept Stat Oxford OX1 3TG England Univ Coll Dublin Sch Math Sci Dublin 2 Ireland

This article introduces a novel and flexible framework for investigating the roles of actors within a network. Particular interest is in roles as defined by local network connectivity patterns, identified using the ego-networks extracted from the network. A mixture of exponential-family random graph models (ERGM) is developed for these ego-networks to cluster the nodes into roles. We refer to this model as the ego-ERGM. An expectation-maximization algorithm is developed to infer the unobserved cluster assignments and to estimate the mixture model parameters using a maximum pseudo-likelihood approximation. We demonstrate the flexibility and utility of the method using examples of simulated and real networks.

关键词： Finite mixture model Ego-network expectation maximization algorithm

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