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| differential 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 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 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 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 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 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 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 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 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 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 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 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 expression analysis in r: Stress and Environmental Regulation of Gene Expression and Adaptation in Bacteria Frans J. de Bruijn, 2016-07-13 Bacteria in various habitats are subject to continuously changing environmental conditions, such as nutrient deprivation, heat and cold stress, UV radiation, oxidative stress, dessication, acid stress, nitrosative stress, cell envelope stress, heavy metal exposure, osmotic stress, and others. In order to survive, they have to respond to these conditions by adapting their physiology through sometimes drastic changes in gene expression. In addition they may adapt by changing their morphology, forming biofilms, fruiting bodies or spores, filaments, Viable But Not Culturable (VBNC) cells or moving away from stress compounds via chemotaxis. Changes in gene expression constitute the main component of the bacterial response to stress and environmental changes, and involve a myriad of different mechanisms, including (alternative) sigma factors, bi- or tri-component regulatory systems, small non-coding RNA’s, chaperones, CHRIS-Cas systems, DNA repair, toxin-antitoxin systems, the stringent response, efflux pumps, alarmones, and modulation of the cell envelope or membranes, to name a few. Many regulatory elements are conserved in different bacteria; however there are endless variations on the theme and novel elements of gene regulation in bacteria inhabiting particular environments are constantly being discovered. Especially in (pathogenic) bacteria colonizing the human body a plethora of bacterial responses to innate stresses such as pH, reactive nitrogen and oxygen species and antibiotic stress are being described. An attempt is made to not only cover model systems but give a broad overview of the stress-responsive regulatory systems in a variety of bacteria, including medically important bacteria, where elucidation of certain aspects of these systems could lead to treatment strategies of the pathogens. Many of the regulatory systems being uncovered are specific, but there is also considerable “cross-talk” between different circuits. Stress and Environmental Regulation of Gene Expression and Adaptation in Bacteria is a comprehensive two-volume work bringing together both review and original research articles on key topics in stress and environmental control of gene expression in bacteria. Volume One contains key overview chapters, as well as content on one/two/three component regulatory systems and stress responses, sigma factors and stress responses, small non-coding RNAs and stress responses, toxin-antitoxin systems and stress responses, stringent response to stress, responses to UV irradiation, SOS and double stranded systems repair systems and stress, adaptation to both oxidative and osmotic stress, and desiccation tolerance and drought stress. Volume Two covers heat shock responses, chaperonins and stress, cold shock responses, adaptation to acid stress, nitrosative stress, and envelope stress, as well as iron homeostasis, metal resistance, quorum sensing, chemotaxis and biofilm formation, and viable but not culturable (VBNC) cells. Covering the full breadth of current stress and environmental control of gene expression studies and expanding it towards future advances in the field, these two volumes are a one-stop reference for (non) medical molecular geneticists interested in gene regulation under stress. |
| differential 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 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 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 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 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 expression analysis in r: Modern Statistics for Modern Biology SUSAN. HUBER HOLMES (WOLFGANG.), Wolfgang Huber, 2018 |
| differential expression analysis in r: Molecular Pathology in Cancer Research Sunil R. Lakhani, Stephen B. Fox, 2017-01-20 The aim of the book is to discuss the application of molecular pathology in cancer research, and its contribution in the classification of different tumors and identification of potential molecular targets, as well as how this knowledge may be translated into clinical practice, and the huge impact this field is likely to have in the next 5 to 10 years. |
| differential 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 expression analysis in r: Biodemography of Aging Anatoliy I. Yashin, Eric Stallard, Kenneth C. Land, 2016-08-22 This volume is a critical exposition of the data and analyses from a full decade of rigorous research into how age-related changes at the individual level, along with other factors, contribute to morbidity, disability and mortality risks at the broader population level. After summarizing the state of our knowledge in the field, individual chapters offer enlightening discussion on a range of key topics such as age trajectory analysis in select and general populations, incidence/age patterns of major chronic illnesses, and indices of cumulative deficits and their use in characterizing and understanding the detailed properties of individual aging. The book features comprehensive statistical analyses of unique longitudinal data sets including the unique resource of the Framingham Heart Study, with its more than 60 years of follow-up. Culminating in penetrating conclusions about the insights gained from the work involved, this book adds much to our understanding of the links between aging and human health. |
| differential expression analysis in r: Communities in Action National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Population Health and Public Health Practice, Committee on Community-Based Solutions to Promote Health Equity in the United States, 2017-04-27 In the United States, some populations suffer from far greater disparities in health than others. Those disparities are caused not only by fundamental differences in health status across segments of the population, but also because of inequities in factors that impact health status, so-called determinants of health. Only part of an individual's health status depends on his or her behavior and choice; community-wide problems like poverty, unemployment, poor education, inadequate housing, poor public transportation, interpersonal violence, and decaying neighborhoods also contribute to health inequities, as well as the historic and ongoing interplay of structures, policies, and norms that shape lives. When these factors are not optimal in a community, it does not mean they are intractable: such inequities can be mitigated by social policies that can shape health in powerful ways. Communities in Action: Pathways to Health Equity seeks to delineate the causes of and the solutions to health inequities in the United States. This report focuses on what communities can do to promote health equity, what actions are needed by the many and varied stakeholders that are part of communities or support them, as well as the root causes and structural barriers that need to be overcome. |
| differential expression analysis in r: Microarray Data Shailaja R. Deshmukh, Sudha G. Purohit, 2007 Functional Genomics, a branch of bioinformatics, is essentially an interdisciplinary subject in which biologists, statisticians and computer experts interact to analyze the microarray data. This book caters to the needs of all the three disciplines. For biologists and computer scientists, it explains concepts of statistics and statistical inference. For Biologists and Statisticians, it provides annotated R programs to analyze microarray data. For Statisticians and Computer scientists, it explains basics of biology relevant to microarray experiment. Thus, the book will be useful to scientists from all the three disciplines, with not much knowledge of other disciplines, to analyze microarray data and interpret the results. |
| differential 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 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 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 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 expression analysis in r: Compstat Wolfgang Härdle, Bernd Rönz, 2012-12-06 This COMPSTAT 2002 book contains the Keynote, Invited, and Full Contributed papers presented in Berlin, August 2002. A companion volume including Short Communications and Posters is published on CD. The COMPSTAT 2002 is the 15th conference in a serie of biannual conferences with the objective to present the latest developments in Computational Statistics and is taking place from August 24th to August 28th, 2002. Previous COMPSTATs were in Vienna (1974), Berlin (1976), Leiden (1978), Edinburgh (1980), Toulouse (1982), Pra~ue (1984), Rome (1986), Copenhagen (1988), Dubrovnik (1990), Neuchatel (1992), Vienna (1994), Barcelona (1996), Bris tol (1998) and Utrecht (2000). COMPSTAT 2002 is organised by CASE, Center of Applied Statistics and Eco nomics at Humboldt-Universitat zu Berlin in cooperation with F'reie Universitat Berlin and University of Potsdam. The topics of COMPSTAT include methodological applications, innovative soft ware and mathematical developments, especially in the following fields: statistical risk management, multivariate and robust analysis, Markov Chain Monte Carlo Methods, statistics of E-commerce, new strategies in teaching (Multimedia, In ternet), computerbased sampling/questionnaires, analysis of large databases (with emphasis on computing in memory), graphical tools for data analysis, classification and clustering, new statistical software and historical development of software. |
| differential 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 expression analysis in r: Free Fatty Acid Receptors Graeme Milligan, Ikuo Kimura, 2017-02-08 This book highlights the important role free fatty acids (FFA) play as potential drug targets. While FFA have long been considered byproducts of cell metabolism, they are now recognized as ligands that regulate cell and tissue function via G-protein-coupled receptors. At least three receptors have been identified for which FFA appear to be the endogenous ligands. |
| differential 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 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 expression analysis in r: Single Cell Methods Valentina Proserpio, 2019 This volume provides a comprehensive overview for investigating biology at the level of individual cells. Chapters are organized into eight parts detailing a single-cell lab, single cell DNA-seq, RNA-seq, single cell proteomic and epigenetic, single cell multi-omics, single cell screening, and single cell live imaging. 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, Single Cell Methods: Sequencing and Proteomics aims to make each experiment easily reproducible in every lab. |
| differential expression analysis in r: Advances in Artificial Intelligence, Computation, and Data Science Tuan D. Pham, Hong Yan, Muhammad W. Ashraf, Folke Sjöberg, 2021-07-12 Artificial intelligence (AI) has become pervasive in most areas of research and applications. While computation can significantly reduce mental efforts for complex problem solving, effective computer algorithms allow continuous improvement of AI tools to handle complexity—in both time and memory requirements—for machine learning in large datasets. Meanwhile, data science is an evolving scientific discipline that strives to overcome the hindrance of traditional skills that are too limited to enable scientific discovery when leveraging research outcomes. Solutions to many problems in medicine and life science, which cannot be answered by these conventional approaches, are urgently needed for society. This edited book attempts to report recent advances in the complementary domains of AI, computation, and data science with applications in medicine and life science. The benefits to the reader are manifold as researchers from similar or different fields can be aware of advanced developments and novel applications that can be useful for either immediate implementations or future scientific pursuit. Features: Considers recent advances in AI, computation, and data science for solving complex problems in medicine, physiology, biology, chemistry, and biochemistry Provides recent developments in three evolving key areas and their complementary combinations: AI, computation, and data science Reports on applications in medicine and physiology, including cancer, neuroscience, and digital pathology Examines applications in life science, including systems biology, biochemistry, and even food technology This unique book, representing research from a team of international contributors, has not only real utility in academia for those in the medical and life sciences communities, but also a much wider readership from industry, science, and other areas of technology and education. |
| differential expression analysis in r: Molecular Biology of the Cell , 2002 |
| differential expression analysis in r: Implementing Reproducible Research Victoria Stodden, Friedrich Leisch, Roger D. Peng, 2014-04-14 In computational science, reproducibility requires that researchers make code and data available to others so that the data can be analyzed in a similar manner as in the original publication. Code must be available to be distributed, data must be accessible in a readable format, and a platform must be available for widely distributing the data and code. In addition, both data and code need to be licensed permissively enough so that others can reproduce the work without a substantial legal burden. Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science. It describes: Computational tools, such as Sweave, knitr, VisTrails, Sumatra, CDE, and the Declaratron system Open source practices, good programming practices, trends in open science, and the role of cloud computing in reproducible research Software and methodological platforms, including open source software packages, RunMyCode platform, and open access journals Each part presents contributions from leaders who have developed software and other products that have advanced the field. Supplementary material is available at www.ImplementingRR.org. |
| differential expression analysis in r: Proteomics Data Analysis Daniela Cecconi, 2021 This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics. |
| differential expression analysis in r: Essentials of Glycobiology Ajit Varki, Maarten J. Chrispeels, 1999 Sugar chains (glycans) are often attached to proteins and lipids and have multiple roles in the organization and function of all organisms. Essentials of Glycobiology describes their biogenesis and function and offers a useful gateway to the understanding of glycans. |
| differential expression analysis in r: Gene Expression Fumiaki Uchiumi, 2022-10-05 Gene expression is dependent on multiple steps, including transcription, RNA processing, and translation. Importantly, recent studies revealed that gene expression is regulated by chromatin structures and non-coding RNA profiles. Elucidating the molecular mechanisms may contribute to the development of novel therapeutics for aging-related diseases, including cancer and neurodegenerative diseases. This book provides a comprehensive overview of gene expression and its role in human disease. It consists of nine chapters organized into two sections on molecular mechanisms in controlling gene expression and the relationships between transcriptional control and human disease. |
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-els. …
edgeR: differential analysis of sequence read count data User's …
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
How to do differential expression analysis from Fastq format …
DESeq: Differential gene expression analysis based on the negative binomial distribution. FastQC aims to provide a simple way to do some quality control checks on raw sequence data coming …
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
Introduction to Statistics for Differential Gene Expression Analysis
In practice, if we are looking at many variables (for example, thousands of genes) the values for fold change can be greatly spaced out and difficult to compare and visualize directly. Thus, we often …
Bioinformatics Analysis in R Gene Expression Analysis
1 - Give you a overview on the use of R/bioconductor tools for gene expression analysis 2 - Show a real example with all steps necessary for gene expression analysis (based on arrays and RNA-seq)
Differential Expression of RNA- Seq Data - University of North …
Follow along with an Differential expression analysis using DESeq2 described in the file DESeq2_handout.docx
EBSeq: An R package for differential expression analysis using …
The empirical Bayes model in Leng et al., 2013 is implemented in an R package called EBSeq (biostat.wisc.edu/~kendzior/EBSEQ). This manual is a guideline for using the add-on EBSeq …
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 …
Differential Expression Analysis using edgeR - GitHub Pages
We performed the integrative transcriptome analysis of human esophageal squamous cell carcinoma (ESCC) using Illumina high-throughput sequencing. A total of 187 million 38bp …
prolfqua: A Comprehensive R-Package for Proteomics …
Jul 22, 2022 · ABSTRACT: Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and diferential expression analysis of proteins. There is a large …
Differential Expression Analysis using RSEM with EBSeq or edgeR
expression analysis between samples. The reason why I am focusing on EBSeq and edgeR is because they are the more popular tools for differential expression analysis.
decode: Differential Co-Expression and Differential Expression …
Description Integrated differential expression (DE) and differential co- expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO- expression and Differential …
Introduction to Differential Gene Expression Analysis in R
Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study. Batch effects are …
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 testing …
2a Differential Expression - bioinformatics.nl
You generally expect the gene expression values to be more similar between replicates than between samples from different conditions. In this section you will perform a simple and quick …
Workshop hands-on session: Differential expression analysis of …
Note: This is intended as a step by step guide for doing basic statistical analysis of RNA-seq data using DESeq2 package, along with other packages from Bioconductor in R. A de-identified RNA …
DGEobj.utils: Differential Gene Expression (DGE) Analysis …
Takes a DGEobj as input and applies a combination of low intensity filters as specified by the user.
Differential Expression Meta-Analysis with DExMA package
Gene expression meta-analysis comprises a set of methods that combine the results of several diferential expression studies into a single common result [1]. Furthermore, this package has the …
edgeR: Empirical Analysis of Digital Gene Expression Data in R
Title Empirical Analysis of Digital Gene Expression Data in R Description Differential expression analysis of sequence count data. Implements a range of statisti-cal methodology based on the …
Count-based differential expression analysis of RNA …
Figure 1 | Count-based differential expression pipeline for RNA-seq data using edgeR and/or DESeq. Many steps are common to both tools, whereas the specific commands are different …
Differential expression analysis for sequence count data
Nov 10, 2010 · R ij, such that the rate that fragments from genei are sequenced is s jr ij. For each genei and all samplesj of condition r,theR ij are i.i.d. with mean q ir and variance v ir. Thus, the …
edgeR: differential expression analysis of digital gene …
edgeR User’s Guide 2.8.2Biological coefficient of variation (BCV).16 2.8.3Estimating BCVs.17 2.8.4Quasi negative binomial.18 2.9Pairwise comparisons between two or more groups …
pagoda2: Single Cell Analysis and Differential Expression
Title Single Cell Analysis and Differential Expression Version 1.0.12 Description Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' pri-marily performs …
tradeSeq: trajectory-based differential expression analysis …
Title trajectory-based differential expression analysis for sequencing data Date 2019-03-17 Version 1.22.0 Description tradeSeq provides a flexible method for fitting regression mod-els …
Evaluation of the molecular mechanisms of histological …
Next, seven gene expression proles were retrieved from tissue samples (control) collected from patients with FL. Differential analysis yielded 237 differently expressed genes, including 81 …
Differential Expression Analysis using RSEM with EBSeq or …
install r-base in your command line, you will have most likely installed R version 3.4.4. This is not the version we want! Run this to remove this version of R: > sudo apt-get remove r-base-core …
TCC: TCC: Differential expression analysis for tag count data …
estimateDE Estimate degrees of differential expression (DE) for individual genes Description This function calculates p-values (or the related statistics) for identifying differentially expressed …
edgeR: differential expression analysis of digital gene …
edgeR User’s Guide 2.8.2Biological coefficient of variation (BCV).16 2.8.3Estimating BCVs.17 2.8.4Quasi negative binomial.18 2.9Pairwise comparisons between two or more groups …
Differential Expression Analysis using edgeR - GitHub Pages
Di erential Expression Analysis using edgeR 8 3.10 Di erential expression Compute exact genewise tests for di erential expression between androgen and control treatments: et< …
DExMA: Differential Expression Meta-Analysis - Bioconductor
Title Differential Expression Meta-Analysis Version 1.14.0 Description performing all the steps of gene expression meta-analysis considering the possible exis-tence of missing genes. It …
DEXA: A Python-based Tool for the Advanced Deciphering of …
RNA-seq analysis (Clark et al., 2014). This analysis is crucial in advancing plant research by providing insights into the molecular mechanisms underlying various biological processes. …
SCBN: A statistical normalization method and differential …
'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang …
Practical Guide to Interpreting RNA-seq Data
Downstream Analysis: Differential Expression 28. IV. Downstream Analysis: Differential Expression 29 Output. Output IV. Downstream Analysis: Differential Expression 29. IV. …
EBSeq: An R package for gene and isoform differential …
Dec 8, 2015 · Title An R package for gene and isoform differential expression analysis of RNA-seq data Version 2.7.0 Date 2015-12-8 Depends blockmodeling, gplots, testthat, R (>= 3.0.0) …
LFQ-Based Peptide and Protein Intensity Differential …
Dec 12, 2022 · differential expression analysis can simultaneously be per-formed across many samples.7 From the bioinformatics data analysis perspective, label-free methods and labeled …
Differential gene expression analysis of spatial …
expression analysis by using statistical approaches often applied in the analysis of non‑spatial scRNA data (e.g., two‑sample t‑tests, Wilcoxon’s rank sum test), hence neglecting the ...
Differential Expression Analysis for RNA-Seq: An Overview …
In this paper, it is thus timely to provide an overview of these analysis methods and tools. For readers with statistical background, we also review the parameter estimation algorithms and …
Performing differential gene expression analysis - Cornell …
FromReadCountstoDifferentialGeneExpression ## YAL064W.B 8.35600217065942 -0.19250559525655 0.439820610433718 ## stat pvalue padj ##
DEsingle: DEsingle for detecting three types of differential …
Dec 1, 2018 · Description DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially …
glmmSeq: General Linear Mixed Models for Gene-Level …
Title General Linear Mixed Models for Gene-Level Differential Expression Version 0.5.5 Description Using mixed effects models to analyse longitudinal gene expression can highlight …
Visualization and PCA with Gene Expression Data - Utah …
M-direction shows differential expression A-direction shows average expression Look for: systematic changes outliers patterns quality / normalization (larger M-variability, curvature) …
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 …
Efficient differential expression analysis of large-scale ...
While differential expression analysis between TB and controls did not identify any available under aCC-BY-NC-ND 4.0 International license. (which was not certified by peer review) is the author ...
iDEP: an integrated web application for differential …
ential Expression and Pathway analysis) encompasses many useful R and Bioconductor packages, vast annota-tion databases, and related web services. The input is a gene-level …
RNA Sequencing Analysis - Sinica
Differential gene expression analysis pipeline 3. DEG analysis in R (DEMO) IMB Bioinformatics Core From RNA -> sequence data Sequencing Short-read: Illumina HiSeq Long-read: …
iDEP: An integrated web application for differential …
in gene expression data from DNA microarray or RNA- Seq and performs exploratory data analysis (EDA), differential expression, and pathway analysis. The key idea of iDEP is to make …
monocle: Clustering, differential expression, and trajectory …
Mar 13, 2024 · performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but …
Differential Gene Expression Pipeline for Whole …
Differential expression analysis 8. Pairwise comparison of both samples is performed on counts.matrix file which identified and clustered the DGE/transcripts according to most …
DiffSegR: An RNA-Seq data driven method for differential …
Jun 7, 2023 · annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the …
Benchmark of Differential Gene Expression Analysis …
in expression evolution (Fukushima and Pollock, 2020). In this review, we focus on the detection of change in gene expression levels across species, in a speci c lineage or between di erent …
tradeSeq: trajectory-based differential expression analysis …
Title trajectory-based differential expression analysis for sequencing data Date 2019-03-17 Version 1.22.0 Description tradeSeq provides a flexible method for fitting regression mod-els …
Differential expression analysis of RNA-Seq data - GitHub …
Differential expression analysis of RNA-Seq data Garrett Dancik, PhD. Data processing for between sample comparison ... – Ritchie ME, PhipsonB, Wu D, Hu Y, Law CW, Shi W, Smyth …
Practical RNA-seq analysis - Massachusetts Institute of …
Feb 13, 2020 · Differential Expression Issues • Given that statistics are – based on complex models – influenced by even more complex biology The p-values may not be accurate but can …
diffcoexp: Differential Co-expression Analysis - Bioconductor
Aug 9, 2023 · tion coefficients under two conditions equals to zero using Fisher’s r-to-Z trans-formation Examples data(gse4158part) allowWGCNAThreads() res=comparecor(exprs.1 = …
An RNA-Seq Protocol for Differential Expression Analysis
An RNA-Seq Protocol for Differential Expression Analysis Nick D.L. Owens,1,2 Elena De Domenico,1,2 and Michael J. Gilchrist1,3 1The Francis Crick Institute, NW1 1ST London, …
Differential expression analysis for sequence count data
R ij, such that the rate that fragments from genei are sequenced is s jr ij. For each genei and all samplesj of condition r,theR ij are i.i.d. with mean q ir and variance v ir. Thus, the count value K …
edgeR: Empirical Analysis of Digital Gene Expression Data in R
Title Empirical Analysis of Digital Gene Expression Data in R Description Differential expression analysis of sequence count data. Implements a range of statisti-cal methodology based on the …
Sex and regional differences in gene expression across the
The differential expression analysis stratified by psychosis and unaffected cohorts focused on the effects of brain region. To address the confounding between brain region and batch effects, we
NOISeq: An R package for di erential expression in RNA-Seq …
the probability for a gene to have di erent expression between conditions. f 0 and f 1 are, respectively, the densities of Z for genes with no change in expression between conditions and …
NOISeq: Exploratory analysis and differential expression
Feb 24, 2014 · Package ‘NOISeq’ June 12, 2025 Type Package Title Exploratory analysis and differential expression for RNA-seq data Version 2.52.0 Date 2014-02-24
decode: Differential Co-Expression and Differential …
Title Differential Co-Expression and Differential Expression Analysis Version 1.2 Description Integrated differential expression (DE) and differential co-expression (DC) analysis on gene …
How to normalize metatranscriptomic count data for …
exists for differential expression analysis on metatranscriptomic data. Several studies and tools apply methods that have been developed for differential expression analysis in transcriptomics …
edgeR: di erential expression analysis of digital gene …
di erential expression analysis of digital gene expression data. Bioinformatics 26, 139{140. Announcement of the edgeR software package. Introduced the terminology coe cient of …
Optimizing differential expression analysis for proteomics …
Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference Hui Peng 1,2,HeWang1,2, Weijia Kong 1,2,JinyanLi3 & Wilson Wen Bin …
Workshop hands-on session: Differential expression …
4. Differential expression analysis Finally we are ready to run the differential expression pipeline. With the data object dds prepared, the DESeq2 analysis can now be run with a single call to …
Monocle3: Cell counting, differential expression, and …
Monocle3: Cell counting, differential expression, and trajectory analysis for single-cell RNA-Seq experiments Cole Trapnell University of Washington, Seattle, Washington, USA …
Differential Expression Analysis using edgeR - GitHub Pages
[1]Robinson, MD.et al. (2010) edgeR: a Bioconductor package for di erential expression analysis of digital gene expres-sion data, Bioinformatics, 26 (1) 139-140. [2]Ma, S. et al. (2012) Identi …
GeoDiver: Differential Gene Expression Analysis & Gene-Set …
Summary: GeoDiver is an online web application for performing Differential Gene Expression Analysis (DGEA) and Generally Applicable Gene-set Enrichment Analysis (GAGE) on gene …
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 …
