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| differential gene expression analysis in r: Computational Genomics with R Altuna Akalin, 2020-12-16 Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015. |
| differential gene expression analysis in r: Gene Expression Analysis Nalini Raghavachari, Natàlia Garcia-Reyero, 2018-05-17 This volume provides experimental and bioinformatics approaches related to different aspects of gene expression analysis. Divided in three sections chapters detail wet-lab protocols, bioinformatics approaches, single-cell gene expression, highly multiplexed amplicon sequencing, multi-omics techniques, and targeted sequencing. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Gene Expression Analysis: Methods and Protocols aims provide useful information to researchers worldwide. |
| differential gene expression analysis in r: The Analysis of Gene Expression Data Giovanni Parmigiani, Elizabeth S. Garett, Rafael A. Irizarry, Scott L. Zeger, 2006-04-11 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. |
| differential gene expression analysis in r: RNA-seq Data Analysis Eija Korpelainen, Jarno Tuimala, Panu Somervuo, Mikael Huss, Garry Wong, 2014-09-19 The State of the Art in Transcriptome AnalysisRNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript le |
| differential gene expression analysis in r: The Kiwifruit Genome Raffaele Testolin, Hong-Wen Huang, Allan Ross Ferguson, 2016-05-02 This book describes the basic botanical features of kiwifruit and its wild relatives, reports on the steps that led to its genome sequencing, and discusses the results obtained with the assembly and annotation. The core chapters provide essential insights into the main gene families that characterize this species as a crop, including the genes controlling sugar and starch metabolism, pigment biosynthesis and degradation, the ascorbic-acid pathway, fruit softening and postharvest metabolism, allergens, and resistance to pests and diseases. The book offers a valuable reference guide for taxonomists, geneticists and horticulturists. Further, since information gained from the genome sequence is extraordinarily useful in assessing the breeding value of individuals based on whole-genome scans, it will especially benefit plant breeders. Accordingly, chapters are included that focus on gene introgression from wild relatives and genome-based breeding. |
| differential gene expression analysis in r: Bioinformatics and Computational Biology Solutions Using R and Bioconductor Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, Sandrine Dudoit, 2005-12-29 Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. |
| differential gene expression analysis in r: Interactive Web-Based Data Visualization with R, plotly, and shiny Carson Sievert, 2020-01-30 The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics. |
| differential gene expression analysis in r: Genomics Data Analysis David R. Bickel, 2019-09-24 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 |
| differential gene expression analysis in r: Statistical Analysis of Next Generation Sequencing Data Somnath Datta, Dan Nettleton, 2016-09-17 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. |
| differential gene expression analysis in r: Molecular Data Analysis Using R Csaba Ortutay, Zsuzsanna Ortutay, 2017-02-06 This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories. Key features include: • Broad appeal--the authors target their material to researchers in several levels, ensuring that the basics are always covered. • First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology. • Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users. |
| differential gene expression analysis in r: Bayesian Inference for Gene Expression and Proteomics Kim-Anh Do, Peter Müller, Marina Vannucci, 2006-07-24 Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation. |
| differential gene expression analysis in r: Gene Expression Data Analysis Pankaj Barah, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita, 2021-11-08 Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences |
| differential gene expression analysis in r: Machine Learning Paradigms George A. Tsihrintzis, Maria Virvou, Evangelos Sakkopoulos, Lakhmi C. Jain, 2019-07-06 This book is the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at providing, in hard-copy and soft-copy form, books on all aspects of learning, analytics, advanced intelligent systems and related technologies. These disciplines are strongly related and mutually complementary; accordingly, the new series encourages an integrated approach to themes and topics in these disciplines, which will result in significant cross-fertilization, research advances and new knowledge creation. To maximize the dissemination of research findings, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. This book is intended for professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter allows readers to probe further into those application areas that interest them most. |
| differential gene expression analysis in r: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| differential gene expression analysis in r: Algorithms for Minimization Without Derivatives Richard P. Brent, 2013-06-10 DIVOutstanding text for graduate students and research workers proposes improvements to existing algorithms, extends their related mathematical theories, and offers details on new algorithms for approximating local and global minima. /div |
| differential gene expression analysis in r: Modern Statistics for Modern Biology SUSAN. HUBER HOLMES (WOLFGANG.), Wolfgang Huber, 2018 |
| differential gene expression analysis in r: Mixed-Effects Models in S and S-PLUS José C. Pinheiro, Douglas Bates, 2009-04-15 R, linear models, random, fixed, data, analysis, fit. |
| differential gene expression analysis in r: Fusarium wilt Jeffrey Coleman, 2021-10-23 This volume provides a collection of molecular protocols detailing the most common and modern techniques on fusarium wilt. Chapters guide readers through methods on initial isolation, molecular-based identification, genome characterization, generation of mutants, and characterization of interactions with other organisms including host plants. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Fusarium wilt: Methods and Protocols aims to be a valuable resource for mycologists, plant pathologists, microbiologists, geneticists, and other scientists that have an interest in members of the Fusarium oxysporum species complex or closely related fungi. |
| differential gene expression analysis in r: Gene Quantification Francois Ferre, 2012-12-06 Geneticists and molecular biologists have been interested in quantifying genes and their products for many years and for various reasons (Bishop, 1974). Early molecular methods were based on molecular hybridization, and were devised shortly after Marmur and Doty (1961) first showed that denaturation of the double helix could be reversed - that the process of molecular reassociation was exquisitely sequence dependent. Gillespie and Spiegelman (1965) developed a way of using the method to titrate the number of copies of a probe within a target sequence in which the target sequence was fixed to a membrane support prior to hybridization with the probe - typically a RNA. Thus, this was a precursor to many of the methods still in use, and indeed under development, today. Early examples of the application of these methods included the measurement of the copy numbers in gene families such as the ribosomal genes and the immunoglo bulin family. Amplification of genes in tumors and in response to drug treatment was discovered by this method. In the same period, methods were invented for estimating gene num bers based on the kinetics of the reassociation process - the so-called Cot analysis. This method, which exploits the dependence of the rate of reassociation on the concentration of the two strands, revealed the presence of repeated sequences in the DNA of higher eukaryotes (Britten and Kohne, 1968). An adaptation to RNA, Rot analysis (Melli and Bishop, 1969), was used to measure the abundance of RNAs in a mixed population. |
| differential gene expression analysis in r: Optimal Bayesian Classification Lori A. Dalton, Edward R. Dougherty, 2019 The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. This book treats classification, where the underlying random process is a feature-label distribution, and an optimal operator is a Bayes classifier, which is a classifier minimizing the classification error. With sufficient knowledge we can construct the feature-label distribution and thereby find a Bayes classifier. Rarely, do we possess such knowledge. On the other hand, if we had unlimited data, we could accurately estimate the feature-label distribution and obtain a Bayes classifier. Rarely do we possess sufficient data. The aim of this book is to best use whatever knowledge and data are available to design a classifier. The book takes a Bayesian approach to modeling the feature-label distribution and designs an optimal classifier relative to a posterior distribution governing an uncertainty class of feature-label distributions. In this way it takes full advantage of knowledge regarding the underlying system and the available data. Its origins lie in the need to estimate classifier error when there is insufficient data to hold out test data, in which case an optimal error estimate can be obtained relative to the uncertainty class. A natural next step is to forgo classical ad hoc classifier design and simply find an optimal classifier relative to the posterior distribution over the uncertainty class-this being an optimal Bayesian classifier-- |
| differential gene expression analysis in r: Molecular Data Analysis Using R Csaba Ortutay, Zsuzsanna Ortutay, 2016-12-29 This book addresses the difficulties experienced by wet lab researchers with the statistical analysis of molecular biology related data. The authors explain how to use R and Bioconductor for the analysis of experimental data in the field of molecular biology. The content is based upon two university courses for bioinformatics and experimental biology students (Biological Data Analysis with R and High-throughput Data Analysis with R). The material is divided into chapters based upon the experimental methods used in the laboratories. Key features include: • Broad appeal--the authors target their material to researchers in several levels, ensuring that the basics are always covered. • First book to explain how to use R and Bioconductor for the analysis of several types of experimental data in the field of molecular biology. • Focuses on R and Bioconductor, which are widely used for data analysis. One great benefit of R and Bioconductor is that there is a vast user community and very active discussion in place, in addition to the practice of sharing codes. Further, R is the platform for implementing new analysis approaches, therefore novel methods are available early for R users. |
| differential gene expression analysis in r: Computational Network Analysis with R Matthias Dehmer, Yongtang Shi, Frank Emmert-Streib, 2016-12-12 This new title in the well-established Quantitative Network Biology series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis. |
| differential gene expression analysis in r: Chromatin Immunoprecipitation Neus Visa, Antonio Jordán-Pla, 2017-10-14 This up-to-date volume includes protocols that illustrate the broad use of chromatin immunoprecipitation (ChIP) and ChIP-related methods in a variety of biological research areas. The collection also includes protocols designed to improve the performance of ChIP for specific applications. Written in the highly successful Methods in Molecular Biology series format, chapters include introduction to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, as well as tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Chromatin Immunoprecipitation: Methods and Protocols features techniques, including bioinformatic analysis of ChIP data, will be of interest to a very broad research community in the fields of biochemistry, molecular biology, microbiology, and biomedicine. |
| differential gene expression analysis in r: The EM Algorithm and Extensions Geoffrey J. McLachlan, Thriyambakam Krishnan, 2007-11-09 The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm. |
| differential gene expression analysis in r: Plant Germline Development Anja Schmidt, 2017-09-22 This detailed volume explores common and numerous specialized methods to study various aspects of plant germline development and targeted manipulation, including imaging and hybridization techniques to study cell-type specification, cell lineage, signaling and hormones, cell cycle, and the cytoskeleton. In addition, cell-type specific methods for targeted ablation or isolation are provided, protocols to apply “omics” technologies and to perform bioinformatics data analysis, as well as methods relevant for aspects of biotechnology or plant breeding. This includes protocols that are relevant for the targeted manipulation of pathways, for crop plant transformation, or for conditional induction of phenotypes. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Plant Germline Development: Methods and Protocols serves as a comprehensive guide not only to studying basic questions related to different aspects of plant reproductive development but also for state of the art methods, in addition to being a source of inspiration for new approaches and research questions in many laboratories. |
| differential gene expression analysis in r: Applied Bioinformatics Paul Maria Selzer, Richard Marhöfer, Andreas Rohwer, 2008-01-18 At last, here is a baseline book for anyone who is confused by cryptic computer programs, algorithms and formulae, but wants to learn about applied bioinformatics. Now, anyone who can operate a PC, standard software and the internet can also learn to understand the biological basis of bioinformatics, of the existence as well as the source and availability of bioinformatics software, and how to apply these tools and interpret results with confidence. This process is aided by chapters that introduce important aspects of bioinformatics, detailed bioinformatics exercises (including solutions), and to cap it all, a glossary of definitions and terminology relating to bioinformatics. |
| differential gene expression analysis in r: Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman, Jennifer Hill, 2007 This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. |
| differential gene expression analysis in r: Statistical Analysis of Gene Expression Microarray Data Terry Speed, 2003-03-26 Although less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies |
| differential gene expression analysis in r: Biologically Inspired Techniques in Many-Criteria Decision Making Satchidananda Dehuri, Bhabani Shankar Prasad Mishra, Pradeep Kumar Mallick, Sung-Bae Cho, Margarita N. Favorskaya, 2020-01-21 This book addresses many-criteria decision-making (MCDM), a process used to find a solution in an environment with several criteria. In many real-world problems, there are several different objectives that need to be taken into account. Solving these problems is a challenging task and requires careful consideration. In real applications, often simple and easy to understand methods are used; as a result, the solutions accepted by decision makers are not always optimal solutions. On the other hand, algorithms that would provide better outcomes are very time consuming. The greatest challenge facing researchers is how to create effective algorithms that will yield optimal solutions with low time complexity. Accordingly, many current research efforts are focused on the implementation of biologically inspired algorithms (BIAs), which are well suited to solving uni-objective problems. This book introduces readers to state-of-the-art developments in biologically inspired techniques and their applications, with a major emphasis on the MCDM process. To do so, it presents a wide range of contributions on e.g. BIAs, MCDM, nature-inspired algorithms, multi-criteria optimization, machine learning and soft computing. |
| differential gene expression analysis in r: Molecular Biology of the Cell , 2002 |
| differential gene expression analysis in r: Analysing Gene Expression Stefan Lorkowski, Paul M. Cullen, 2006-03-06 This book combines the experience of 225 experts on 900 pages. Scientists worldwide are currently overwhelmed by the ever-increasing number and diversity of genome projects. This handbook is your guide through the jungle of new methods and techniques available to analyse gene expression - the first to provide such a broad view of the measurement of mRNA and protein expression in vitro, in situ and even in vivo. Despite this broad approach, detail is sufficient for you to grasp the principles behind each method. In each case, the authors weigh up the advantages and disadvantages, paying particular attention to the automated, high-throughput processing demanded by the biotech industry. Completely up to date, the book covers such ground-breaking methods such as DNA microarrays, serial analysis of gene expression, differential display, and identification of open reading frame expressed sequence tags. All the methods and necessary equipment are presented visually in more than 300 mainly colour illustrations to assist their step-by-step reproduction in your laboratory. Each chapter is rounded off with its own set of extensive references that provide access to detailed experimental protocols. In short, the bible of analysing gene expression. |
| differential gene expression analysis in r: Applications of RNA-Seq and Omics Strategies Fabio Marchi, Priscila Cirillo, Elvis Cueva Mateo, 2017-09-13 The large potential of RNA sequencing and other omics techniques has contributed to the production of a huge amount of data pursuing to answer many different questions that surround the science's great unknowns. This book presents an overview about powerful and cost-efficient methods for a comprehensive analysis of RNA-Seq data, introducing and revising advanced concepts in data analysis using the most current algorithms. A holistic view about the entire context where transcriptome is inserted is also discussed here encompassing biological areas with remarkable technological advances in the study of systems biology, from microorganisms to precision medicine. |
| differential gene expression analysis in r: Secure Multiparty Computation Ronald Cramer, Ivan Bjerre Damgård, Jesper Buus Nielsen (aut), 2015-07-15 This book provides information on theoretically secure multiparty computation (MPC) and secret sharing, and the fascinating relationship between the two concepts. |
| differential gene expression analysis in r: Introductory Statistics with R Peter Dalgaard, 2008-06-27 This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis. |
| differential gene expression analysis in r: Statistical Genomics Ewy Mathé, Sean Davis, 2016-03-24 This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data. |
| differential gene expression analysis in r: Interactive and Dynamic Graphics for Data Analysis Dianne Cook, Deborah F. Swayne, 2007-12-12 This book is about using interactive and dynamic plots on a computer screen as part of data exploration and modeling, both alone and as a partner with static graphics and non-graphical computational methods. The area of int- active and dynamic data visualization emerged within statistics as part of research on exploratory data analysis in the late 1960s, and it remains an active subject of research today, as its use in practice continues to grow. It now makes substantial contributions within computer science as well, as part of the growing ?elds of information visualization and data mining, especially visual data mining. The material in this book includes: • An introduction to data visualization, explaining how it di?ers from other types of visualization. • Adescriptionofourtoolboxofinteractiveanddynamicgraphicalmethods. • An approach for exploring missing values in data. • An explanation of the use of these tools in cluster analysis and supervised classi?cation. • An overview of additional material available on the web. • A description of the data used in the analyses and exercises. The book’s examples use the software R and GGobi. R (Ihaka & Gent- man 1996, RDevelopment CoreTeam2006) isafreesoftware environment for statistical computing and graphics; it is most often used from the command line, provides a wide variety of statistical methods, and includes high–quality staticgraphics.RaroseintheStatisticsDepartmentoftheUniversityofAu- land and is now developed and maintained by a global collaborative e?ort. |
| differential gene expression analysis in r: Genome Data Analysis Ju Han Kim, 2019-04-30 This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics. |
| differential gene expression analysis in r: Effective Techniques for Bioinformatic Exploration Fazendeiro, Paulo, Leite, Carmelina, 2024-11-01 The field of biology and technology is constantly changing and growing. However, the abundance and intricacy of biological data present significant challenges for researchers, educators, and students. Deciphering this vast sea of information to extract meaningful insights can be difficult. Traditional approaches often fail to provide comprehensive solutions to these intricate problems, leaving many struggling to navigate the complexities of bioinformatics. Effective Techniques for Bioinformatic Exploration brings new clarity to the world of bioinformatics, offering a comprehensive solution to the challenges scholars face. Through its meticulously crafted chapters, this book provides a structured approach to understanding and applying bioinformatics principles. Bridging the gap between theory and practice equips readers with the tools needed to tackle complex biological problems effectively. Whether delving into genomics, proteomics, or machine learning models, this book offers a roadmap for success. This book empowers readers to overcome challenges and make meaningful contributions to the field by embracing the scientific method and showcasing the practical application of bioinformatics techniques. |
| differential gene expression analysis in r: Gene Expression Data Analysis Pankaj Barah, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita, 2021-11-21 Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences |
| differential gene expression analysis in r: Microarray Technology in Practice Steve Russell, Lisa A. Meadows, Roslin R. Russell, 2008-11-21 Using chips composed of thousands of spots, each with the capability of holding DNA molecules corresponding to a given gene, DNA microarray technology has enabled researchers to measure simultaneously gene expression across the genome. As with other large-scale genomics approaches, microarray technologies are broadly applicable across disciplines of life and biomedical sciences, but remain daunting to many researchers. This guide is designed to demystify the technology and inform more biologists about this critically important experimental technique. - Cohesive overview of the technology and available platforms, followed by detailed discussion of experimental design and analysis of microarray experiments - Up-to-date description of normalization methods and current methods for sample amplification and labeling - Deep focus on oligonucleotide design, printing, labeling and hybridization, data acquisition, normalization, and meta-analysis - Additional uses of microarray technology such as ChIP (chromatin immunoprecipitation) with hybridization to DNA arrays, microarray-based comparative genomic hybridization (CGH), and cell and tissue arrays |
DGEAR: Differential Gene Expression Analysis with R
Description Analyses gene expression data derived from experiments to detect differentially ex-pressed genes by employing the concept of majority voting with five different statistical mod …
Introduction to R and Differential Expression Analysis - GitHub …
Use DESeq2 to perform differential expression analysis on the count data and obtain a list of significantly different genes Visualization of differentially expressed genes
Bioinformatics Analysis in R Gene Expression Analysis
Analysis of Gene Expression treatment response 1- Which genes are up/down regulated after treatment? - differential analysis / clustering genes 2 - Which cells are more similar? - …
Introduction to Statistics for Differential Gene Expression …
Principal Component Analysis (PCA) •PCA reduces the number of dimensions (variables) by condensing all variables into a small number of vectors ‒Vector= summary of key information …
Differential Expression Analysis - GitHub Pages
• empirical Bayes methods for differential expression: t-tests, F-tests, posterior odds • analyse log-ratios, log-intensities, log-CPM values • accommodate quality weights in analysis • control of …
Introduction to Differential Gene Expression Analysis in R
kmer-based quantification of Gene Expression (Salmon etc.) - benefits from PE. Transcriptome Analysis – longer paired-end reads (such as 2 x 75 bp). Small RNA Analysis – short single …
edgeR: differential analysis of sequence read count data …
edgeR can be applied to di erential expression at the gene, exon, transcript or tag level. In fact, read counts can be summarized by any genomic feature. edgeR analyses at the exon level
CRISPR Screen and Gene Expression Differential Analysis
CEDA models the sgRNA counts at different levels of gene expression by multi-component normal mixtures, with the model fit by an EM algorithm. Posterior estimates at sgRNA level are …
Introduction to R and Differential Expression Analysis - GitHub …
Use DESeq2 to perform differential expression analysis on the count data and obtain a list of significantly different genes Visualization of differentially expressed genes
edgeR: Empirical Analysis of Digital Gene Expression Data in R
May 3, 2025 · edgeR is a package for the analysis of digital gene expression data arising from RNA sequencing technologies such as SAGE, CAGE, Tag-seq or RNA-seq, with emphasis on …
Workshop hands-on session: Differential expression analysis …
We will start with the read count table and demonstrate how to perform exploratory data analysis, test for differential gene expression and visually explore the results. Pleases note that COPY & …
pagoda2: Single Cell Analysis and Differential Expression
It performs basic tasks such as cell size nor-malization, gene variance normalization, and can be used to identify subpopulations and run dif-ferential expression within individual samples. …
Introduction to Differential Gene Expression Analysis - GitHub …
• RNA-seq, small RNA-seq and ChIP-seq analysis • Genome-wide methylation • WGS, resequencing, exome-seq and CNV studies • Quality assurance and analysis of gene …
Differential gene expression analysis : Example of RNAseq
Differential gene expression analysis using R • Bioconductor https://bioconductor.org/ • Several packages : – limma: t-test – DESeq2: Wald test – edgeR: exact test healthy donors patients All …
edgeR: Empirical Analysis of Digital Gene Expression Data in R
May 3, 2025 · edgeR is a package for the analysis of digital gene expression data arising from RNA sequencing technologies such as SAGE, CAGE, Tag-seq or RNA-seq, with emphasis on …
DGEobj.utils: Differential Gene Expression (DGE) Analysis …
The tools include both analysis work-flow and utility functions: mapping/unit conversion, count normalization, accounting for unknown covariates, and more. This is a complement/cohort to …
2a Differential Expression - bioinformatics.nl
In this exercise we will compare two analysis pipelines to get differentially expressed genes. The first pipeline uses the tools hisat2, stringtie and DESeq2. The reads were mapped to the …
Introduction to differential gene expression (DGE) analysis
Use DESeq2 to perform differential expression analysis on the count data and obtain a list of significantly different genes Visualize results of the analysis
glmmSeq: General Linear Mixed Models for Gene-Level …
This package provides negative binomial and Gaussian mixed effects mod-els to fit gene expression and other biological data across repeated samples.
Introduction to R and Differential Expression Analysis - GitHub …
Use DESeq2 to perform differential expression analysis on the count data and obtain a list of significantly different genes Visualization of differentially expressed genes
DGEAR: Differential Gene Expression Analysis with R
Description Analyses gene expression data derived from experiments to detect differentially ex-pressed genes by employing the concept of majority voting with five …
Introduction to R and Differential Expression Analys…
Use DESeq2 to perform differential expression analysis on the count data and obtain a list of significantly different genes Visualization of differentially expressed …
Bioinformatics Analysis in R Gene Expression Analysis
Analysis of Gene Expression treatment response 1- Which genes are up/down regulated after treatment? - differential analysis / clustering genes 2 - Which cells …
Introduction to Statistics for Differential Gene Expression …
Principal Component Analysis (PCA) •PCA reduces the number of dimensions (variables) by condensing all variables into a small number of vectors ‒Vector= …
Differential Expression Analysis - GitHub Pages
• empirical Bayes methods for differential expression: t-tests, F-tests, posterior odds • analyse log-ratios, log-intensities, log-CPM values • accommodate quality weights in …
