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Bayesian Mixtures And Gene Expression Profiling With Missing Data
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Book Synopsis Bayesian Mixtures and Gene Expression Profiling with Missing Data by : Xiaoqing Chang
Download or read book Bayesian Mixtures and Gene Expression Profiling with Missing Data written by Xiaoqing Chang and published by . This book was released on 2008 with total page 58 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing values are one of the problems encountered in microarray data analysis. For many of the clustering algorithms applied in microarray data analysis, a complete data matrix is required. The traditional approach to solving the missing value problem is to fill in with estimates by imputation. Once the missing value estimates are imputed, they remain fixed during the following clustering process. Poorly estimated missing data points will impair reliability of the cluster analysis. In this particular study, we tested the ability of a novel clustering method based on a Bayesian infinite mixtures model (IMM) to accommodate missing data. In a simulation study and a prostate cancer dataset, by examining the specificity and sensitivity of clusters we demonstrated that the IMM method has increased precision of the cluster analysis without requirement of a prior imputation. IMM is more robust in clustering an incomplete dataset than traditional clustering methods, which require prior imputation.
Book Synopsis Bayesian Analysis of Gene Expression Data by : Bani K. Mallick
Download or read book Bayesian Analysis of Gene Expression Data written by Bani K. Mallick and published by John Wiley & Sons. This book was released on 2009-07-20 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable. This book: Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data. Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications. Accompanied by website featuring datasets, exercises and solutions. Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.
Book Synopsis Bayesian Inference for Gene Expression and Proteomics by : Kim-Anh Do
Download or read book Bayesian Inference for Gene Expression and Proteomics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2012-04-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.
Book Synopsis Bayesian Modeling in Bioinformatics by : Dipak K. Dey
Download or read book Bayesian Modeling in Bioinformatics written by Dipak K. Dey and published by Chapman and Hall/CRC. This book was released on 2010-09-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis. The book explores Bayesian techniques and models for detecting differentially expressed genes, classifying differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also describes applications of mode-oriented stochastic search algorithms, in vitro to in vivo factor profiling, proportional hazards regression using Bayesian kernel machines, and QTL mapping. Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume explores statistical challenges in bioinformatics data analysis and modeling and offers solutions to these problems. It encourages readers to draw on the evolving technologies and promote statistical development in this area of bioinformatics.
Book Synopsis Mixture Models for Microarray Data Analysis by : Zhenyu Jia
Download or read book Mixture Models for Microarray Data Analysis written by Zhenyu Jia and published by . This book was released on 2006 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Inference of Robust Growth Mixture Models with Non-ignorable Missing Data by : Laura Lu
Download or read book Bayesian Inference of Robust Growth Mixture Models with Non-ignorable Missing Data written by Laura Lu and published by . This book was released on 2011 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Inference for Differential Gene Expression Data by : Dabao Zhang
Download or read book Bayesian Inference for Differential Gene Expression Data written by Dabao Zhang and published by . This book was released on 2003 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Topics on Bayesian Analysis of Missing Data by : Yun Kai Jiang
Download or read book Topics on Bayesian Analysis of Missing Data written by Yun Kai Jiang and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation focuses on model selection in logistic regression with incompletely observed data. In particular, methods are presented for using Markov Chain Monte Carlo imputation and Bayesian variable selection to model a binary outcome. We consider multivariate missing covariates, with different types of predictors, such as continuous, counts, and categorical variables. Such type of data is considered in the analysis of Project Talent recorded from a longitudinal study. Roughly 400,000 were selected for the study from United States high school students in grades 9 through 12 during the year 1960; follow-up surveys were conducted 1, 5, and 11 years after graduation. We extend a methodology developed by Yang, Belin, and Boscardin (2005), to this Project Talent for a logistic regression model with incomplete covariates. The idea is to use data information as much as possible to fill in the missing values and study associations between a binary response variable and covariates. According to Yang, Belin, and Boscardin, one approach under a multivariate normal assumption for data, is to conduct Bayesian variable selection and missing data imputation simultaneously within one Gibbs Sampling process, called "Simultaneously Impute And Select" (SIAS). A modified strategy of SIAS is extended to a mixed data structure that allows for categorical, counts, and continuous variables. The first chapter consists of an introduction to some approaches to variable selection for missing data. The fact that missing data arise commonly in statistical analyses, leads to a variety of methods to handle missing data. The missing data mechanism needs to be considered in imputations. The multiple imputation methods and Markov Chain Mote Carlo (MCMC) algorithms are presented as general statistical approaches to missing data analysis. In the MCMC computational toolbox, various implementation methods for imputation are discussed: Metropolis-Hasting, Gibbs Sampler, and Data Augmentation. Compared to model selection methods in frequentist and likelihood inference, Bayesian inference takes an entirely different approach. The frequentist approach only looks at the current data to make inference. The Bayesian approach requires the specification of the prior distribution, which can come from historical data or expert opinion. Stochastic Search Variable Selection (SSVS) and Gibbs Variable Selection (GVS) are reviewed for model selection. Two alternative strategies, Impute Then Select (ITS) and Simultaneously Impute And Select (SIAS), are studied. In the second chapter, imputation and Bayesian variable selection methods for linear regression are extended to a binary response variable that is completely observed, but some covariates have missing values. We focus on extending SIAS strategy to logistic regression models via two alternative imputations, decomposition and Fully Conditional Specification (FCS). The decomposition method breaks a multivariate distribution into a series of univariate ones by decomposing the joint density function p(Y, X1, ..., X[p]) into the product of conditional distributions, using the factorization p(A, B) = p(A[vertical line]B)p(B). The FCS aims to involve iteratively sampling from the conditional distributions for one random variable, given all the others. These two methods are implemented in the imputation step of the SIAS procedure then applied to the Project Talent data. Simulations are also performed to validate these results and demonstrate the superiority of FCS over the decomposition method under certain circumstances. The third chapter presents a new approach for incorporating the sampling weight into imputation and Bayesian variable selection in logistic regression models. We develop the approach that extends SIAS by a Bayesian version of iterative weighted least squares algorithm to include a sampling step based on Gibbs sampler. This approach is illustrated using both simulation studies and Project Talent data.
Book Synopsis Bayesian Infinite Mixture Models for Gene Clustering and Simultaneous Context Selection Using High-throughput Gene Expression Data by : Johannes M. Freudenberg
Download or read book Bayesian Infinite Mixture Models for Gene Clustering and Simultaneous Context Selection Using High-throughput Gene Expression Data written by Johannes M. Freudenberg and published by . This book was released on 2009 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying clustering algorithms to identify groups of co-expressed genes is an important step in the analysis of high-throughput genomics data in order to elucidate affected biological pathways and transcriptional regulatory mechanisms. As these data are becoming ever more abundant the integration with both, existing biological knowledge and other experimental data becomes as crucial as the ability to perform such analysis in a meaningful but virtually unsupervised fashion. Clustering analysis often relies on ad-hoc methods such as k-means or hierarchical clustering with Euclidean distance but model-based methods such as the Bayesian Infinite Mixtures approach have been shown to produce better, more reproducible results. Further improvements have been accomplished by context-specific gene clustering algorithms designed to determine groups of co-expressed genes within a given subset of biological samples termed context. The complementary problem of finding differentially co-expressed genes given two or more contexts has been addressed but relies on the a priori definition of contexts and has not been used to facilitate the clustering of biological samples. Here we describe a new computational method using Bayesian infinite mixture models to cluster genes simultaneously utilizing the concept of differential co-expression as a unique similarity measure to find groups of similar samples. We compute a novel per-gene differential co-expression score that is reproducible and biologically meaningful. To evaluate, annotate, and display clustering results we present the integrated software package CLEAN which contains functionality for performing Clustering Enrichment Analysis, a method to functionally annotate clustering results and to assign a novel gene-specific functional coherence score. We apply our method to a number of simulated datasets comparing it to other commonly used clustering algorithms, and we re-analyze several breast cancer studies. We find that our unsupervised method determines patient groupings highly predictive of clinically relevant factors such as estrogen receptor status, tumor grade, and disease specific survival. Integrating these data with computationally and literature-derived information by applying CLEAN to the corresponding clusterings as well as the DCS signature substantiates these findings. Our results demonstrate the range of applications our methodology provides, offering a comprehensive analysis tool to study gene co-expression and differential co-expression patterns specific to the biological conditions of interest while simultaneously determining subsets of such biological conditions using a unique similarity measure that is complementary to the currently existing methods. It allows us to further our understanding of highly complex diseases such as breast cancer, and it has the potential to greatly facilitate research in many other, not yet as intensively studied areas.
Book Synopsis Bayesian Nonparametrics for Causal Inference and Missing Data by : Michael Joseph Daniels
Download or read book Bayesian Nonparametrics for Causal Inference and Missing Data written by Michael Joseph Daniels and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features Thorough discussion of both BNP and its interplay with causal inference and missing data How to use BNP and g-computation for causal inference and non-ignorable missingness How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions Detailed case studies illustrating the application of BNP methods to causal inference and missing data R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.
Book Synopsis A Bayesian Model for Curve Clustering with Application to Gene Expression Data Analysis by : Chuan Zhou
Download or read book A Bayesian Model for Curve Clustering with Application to Gene Expression Data Analysis written by Chuan Zhou and published by . This book was released on 2003 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Literature Based Bayesian Analysis of Gene Expression Data by : Lijing Xu
Download or read book Literature Based Bayesian Analysis of Gene Expression Data written by Lijing Xu and published by . This book was released on 2010 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Mixture Models for Metagenomic Community Profiling by : Sofia Morfopoulou
Download or read book Bayesian Mixture Models for Metagenomic Community Profiling written by Sofia Morfopoulou and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Mixture Models for Metagenomic Community Profiling by : S. Morfopoulou
Download or read book Bayesian Mixture Models for Metagenomic Community Profiling written by S. Morfopoulou and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Bayesian Approach to Missing Data Estimation in Growth Curves by : E. P. Liski
Download or read book A Bayesian Approach to Missing Data Estimation in Growth Curves written by E. P. Liski and published by . This book was released on 1984 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Methods Under Unknown Prior Distributions with Applications to The Analysis of Gene Expression Data by : Abbas Rahal
Download or read book Bayesian Methods Under Unknown Prior Distributions with Applications to The Analysis of Gene Expression Data written by Abbas Rahal and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The local false discovery rate (LFDR) is one of many existing statistical methods that analyze multiple hypothesis testing. As a Bayesian quantity, the LFDR is based on the prior probability of the null hypothesis and a mixture distribution of null and non-null hypothesis. In practice, the LFDR is unknown and needs to be estimated. The empirical Bayes approach can be used to estimate that mixture distribution. Empirical Bayes does not require complete information about the prior and hyper prior distributions as in hierarchical Bayes. When we do not have enough information at the prior level, and instead of placing a distribution at the hyper prior level in the hierarchical Bayes model, empirical Bayes estimates the prior parameters using the data via, often, the marginal distribution. In this research, we developed new Bayesian methods under unknown prior distribution. A set of adequate prior distributions maybe defined using Bayesian model checking by setting a threshold on the posterior predictive p-value, prior predictive p-value, calibrated p-value, Bayes factor, or integrated likelihood. We derive a set of adequate posterior distributions from that set. In order to obtain a single posterior distribution instead of a set of adequate posterior distributions, we used a blended distribution, which minimizes the relative entropy of a set of adequate prior (or posterior) distributions to a "benchmark" prior (or posterior) distribution. We present two approaches to generate a blended posterior distribution, namely, updating-before-blending and blending-before-updating. The blended posterior distribution can be used to estimate the LFDR by considering the nonlocal false discovery rate as a benchmark and the different LFDR estimators as an adequate set. The likelihood ratio can often be misleading in multiple testing, unless it is supplemented by adjusted p-values or posterior probabilities based on sufficiently strong prior distributions. In case of unknown prior distributions, they can be estimated by empirical Bayes methods or blended distributions. We propose a general framework for applying the laws of likelihood to problems involving multiple hypotheses by bringing together multiple statistical models. We have applied the proposed framework to data sets from genomics, COVID-19 and other data.
Book Synopsis Robust Bayesian Analysis of Gene Expression Microarray Data by : Raphael Gottardo
Download or read book Robust Bayesian Analysis of Gene Expression Microarray Data written by Raphael Gottardo and published by . This book was released on 2005 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt:
