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Author : Dabao Zhang
Publisher :
ISBN 13 :
Total Pages : 194 pages
Book Rating : 4.E/5 ( download)
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:
Author : Bani K. Mallick
Publisher : John Wiley & Sons
ISBN 13 : 9780470742815
Total Pages : 252 pages
Book Rating : 4.7/5 (428 download)
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.
Author : Kim-Anh Do
Publisher : Cambridge University Press
ISBN 13 : 052186092X
Total Pages : 437 pages
Book Rating : 4.5/5 (218 download)
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 2006-07-24 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Author :
Publisher :
ISBN 13 :
Total Pages : 26 pages
Book Rating : 4.:/5 (318 download)
Download or read book Bayesian Robust Inference for Differential Gene Expression in CDNA Microarrays with Multiple Samples written by and published by . This book was released on 2004 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the problem of identifying differentially expressed genes under different conditions using cDNA microarrays. Standard statistical methods cannot be used because typically there are thousands of genes and few replicates. Because of the many steps involved in the experimental process, from hybridization to image analysis, cDNA microarray data often contain outliers. For example, an outlying data value could occur because of scratches or dust on the surface, imperfections in the glass, or imperfections in the array production. We develop a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a t-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. The method is illustrated using two publicly available gene expression data sets. We compare our method to five other commonly used techniques, namely the one-sample t-test, the Bonferroni-adjusted t-test, Significance Analysis of Microarrays (SAM), and EBarrays in both its Lognormal-Normal and Gamma-Gamma forms. In an experiment with HIV data, our method performed better than these alternatives, on the basis of between-replicate agreement and disagreement.
Author : Dipak K. Dey
Publisher : CRC Press
ISBN 13 : 1420070185
Total Pages : 466 pages
Book Rating : 4.4/5 (2 download)
Download or read book Bayesian Modeling in Bioinformatics written by Dipak K. Dey and published by CRC Press. This book was released on 2010-09-03 with total page 466 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 c
Author : David J. Balding
Publisher : John Wiley & Sons
ISBN 13 : 1119429250
Total Pages : 1828 pages
Book Rating : 4.1/5 (194 download)
Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1828 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.
Author : Niansheng Tang
Publisher : BoD – Books on Demand
ISBN 13 : 1838803858
Total Pages : 120 pages
Book Rating : 4.8/5 (388 download)
Download or read book Bayesian Inference on Complicated Data written by Niansheng Tang and published by BoD – Books on Demand. This book was released on 2020-07-15 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.
Author : Mohammad Saber Fallah Nezhad
Publisher : BoD – Books on Demand
ISBN 13 : 1789230926
Total Pages : 142 pages
Book Rating : 4.7/5 (892 download)
Download or read book New Insights into Bayesian Inference written by Mohammad Saber Fallah Nezhad and published by BoD – Books on Demand. This book was released on 2018-05-02 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to the mathematical analysis of Bayesian decision-making when the state of the problem is unknown but further data about it can be obtained. The objective of such analysis is to determine the optimal decision or solution that is logically consistent with the preferences of the decision-maker, that can be analyzed using numerical utilities or criteria with the probabilities assigned to the possible state of the problem, such that these probabilities are updated by gathering new information.
Author : Abu Saleh Mosa Faisal
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)
Download or read book GibbSeq2 written by Abu Saleh Mosa Faisal and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of Gene Set Enrichment Analysis (GSEA) for high throughput sequencing data has gained a new dimension in the last decade. Several statistical methods and software tools have been developed for RNA-seq data to perform Differential Expression analysis. A new method ”gibbseq2” is proposed based on log-normal distribution and full Bayesian inference using Gibbs sampling to analyze RNA-seq data for detection of DE gene sets. This statistical method incorporated truncated log-normal distribution to detect the direction of DNA reads. It uses False Discovery Rate (FDR) and the power of the test to measure the performance of the algorithm. By using simulated data, we explored the method’s performance in controlling the type I error rate. This method performed equally or even better than other methods.
Author : Giovanni Parmigiani
Publisher : Springer Science & Business Media
ISBN 13 : 0387216790
Total Pages : 511 pages
Book Rating : 4.3/5 (872 download)
Download or read book The Analysis of Gene Expression Data written by Giovanni Parmigiani and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents practical approaches for the analysis of data from gene expression micro-arrays. It describes the conceptual and methodological underpinning for a statistical tool and its implementation in software. The book includes coverage of various packages that are part of the Bioconductor project and several related R tools. The materials presented cover a range of software tools designed for varied audiences.
Author : Andrew Gelman
Publisher : CRC Press
ISBN 13 : 1439840954
Total Pages : 677 pages
Book Rating : 4.4/5 (398 download)
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Author : Soma Sekhar Dhavala
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (779 download)
Download or read book Bayesian Semiparametric Models for Heterogeneous Cross-platform Differential Gene Expression written by Soma Sekhar Dhavala and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We are concerned with testing for differential expression and consider three different aspects of such testing procedures. First, we develop an exact ANOVA type model for discrete gene expression data, produced by technologies such as a Massively Parallel Signature Sequencing (MPSS), Serial Analysis of Gene Expression (SAGE) or other next generation sequencing technologies. We adopt two Bayesian hierarchical models -- one parametric and the other semiparametric with a Dirichlet process prior that has the ability to borrow strength across related signatures, where a signature is a specific arrangement of the nucleotides. We utilize the discreteness of the Dirichlet process prior to cluster signatures that exhibit similar differential expression profiles. Tests for differential expression are carried out using non-parametric approaches, while controlling the false discovery rate. Next, we consider ways to combine expression data from different studies, possibly produced by different technologies resulting in mixed type responses, such as Microarrays and MPSS. Depending on the technology, the expression data can be continuous or discrete and can have different technology dependent noise characteristics. Adding to the difficulty, genes can have an arbitrary correlation structure both within and across studies. Performing several hypothesis tests for differential expression could also lead to false discoveries. We propose to address all the above challenges using a Hierarchical Dirichlet process with a spike-and-slab base prior on the random effects, while smoothing splines model the unknown link functions that map different technology dependent manifestations to latent processes upon which inference is based. Finally, we propose an algorithm for controlling different error measures in a Bayesian multiple testing under generic loss functions, including the widely used uniform loss function. We do not make any specific assumptions about the underlying probability model but require that indicator variables for the individual hypotheses are available as a component of the inference. Given this information, we recast multiple hypothesis testing as a combinatorial optimization problem and in particular, the 0-1 knapsack problem which can be solved efficiently using a variety of algorithms, both approximate and exact in nature.
Author : Fang Yu
Publisher :
ISBN 13 :
Total Pages : 194 pages
Book Rating : 4.:/5 (19 download)
Download or read book Bayesian Methods for High-throughput Gene Expression Data in Bioinformatics written by Fang Yu and published by . This book was released on 2007 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : David R. Bickel
Publisher : CRC Press
ISBN 13 : 1000706915
Total Pages : 141 pages
Book Rating : 4.0/5 (7 download)
Download or read book Genomics Data Analysis written by David R. Bickel and published by CRC Press. This book was released on 2019-09-24 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research. Key Features: * dice games and exercises, including one using interactive software, for teaching the concepts in the classroom * examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data * gradual introduction to the mathematical equations needed * how to choose between different methods of multiple hypothesis testing * how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates * guidance through the minefield of current criticisms of p values * material on non-Bayesian prior p values and posterior p values not previously published
Author : Mehmet Kocak
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (959 download)
Download or read book Bayesian and Meta- Analyses of Cell-Cycle Gene Expression Data written by Mehmet Kocak and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Gene expression experiments conducted under a variety of conditions can allow for concurrent tests of more than one hypothesis. It is common for such experiments to be conducted independently by different researchers, using possibly different microarray platforms. In the second and fourth chapter of this thesis, we propose a differential meta-analytic procedure to pool the data from various sources and test the relative significance of the hypotheses under consideration. The specific application made in this thesis is to 10 time-course cell-cycle experiments on fission yeast S. Pombe (Oliva et al., 2005; Peng et al., 2005; Rustici et al., 2004), and the hypotheses of interest concern the question of differential expression and periodic regulation of genes. Besides addressing the above differential meta-analysis issue, we explore how time-course gene expression data can be used to test for periodicity. In this context, the commonly used procedures for testing include the Permutation test by de Lichtenberg et al. (2005) and the G-test by Fisher (1929), both of which are designed to evaluate periodicity against noise; however, it is possible that a given gene may have expression that is neither cyclic, nor just noise. In the third chapter, we introduce an Empirical Bayes approach to test for periodicity and compare its performance in terms of sensitivity and specificity with that of the other two methods through simulations and by application to the S. Pombe cell-cycle gene expression data. We use ‘conserved’ and ‘cycling’ genes by Lu et al. (2007) to assess the sensitivity, and CESR genes by Chen et al. (2003) to assess the specificity of our method. Kocak, M., Zhang, G., Narasimhan, G., George, E.O., Pyne, S. (2010) use George and Mudholkar’ (1983) ‘Difference of Two Logit-Sums’ method to pool bivariate P-values across independent experiments, assuming independence within a pair. We propose a Bayesian approach for pooling bivariate P-values across independent experiments, which accounts for potential correlation between paired P-values. We will investigate the operating characteristics of the Bayesian method trough simulations and apply it to the S. Pombe cell-cycle data. .
Author : Somnath Datta
Publisher : Springer
ISBN 13 : 3319072129
Total Pages : 438 pages
Book Rating : 4.3/5 (19 download)
Download or read book Statistical Analysis of Next Generation Sequencing Data written by Somnath Datta and published by Springer. This book was released on 2014-07-03 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.
Author : Omer Berkman
Publisher :
ISBN 13 :
Total Pages : 170 pages
Book Rating : 4.:/5 (495 download)
Download or read book Robust Inference in Bayesian Networks with Application to Gene Expression Time-series Data written by Omer Berkman and published by . This book was released on 2008 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt:
