Introduction To Bioinformatics With R
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Introduction To Bioinformatics With R
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Author : Edward Curry
language : en
Publisher: CRC Press
Release Date : 2020-11-02
Introduction To Bioinformatics With R written by Edward Curry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-02 with Mathematics categories.
In biological research, the amount of data available to researchers has increased so much over recent years, it is becoming increasingly difficult to understand the current state of the art without some experience and understanding of data analytics and bioinformatics. An Introduction to Bioinformatics with R: A Practical Guide for Biologists leads the reader through the basics of computational analysis of data encountered in modern biological research. With no previous experience with statistics or programming required, readers will develop the ability to plan suitable analyses of biological datasets, and to use the R programming environment to perform these analyses. This is achieved through a series of case studies using R to answer research questions using molecular biology datasets. Broadly applicable statistical methods are explained, including linear and rank-based correlation, distance metrics and hierarchical clustering, hypothesis testing using linear regression, proportional hazards regression for survival data, and principal component analysis. These methods are then applied as appropriate throughout the case studies, illustrating how they can be used to answer research questions. Key Features: · Provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. · Describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook, from basic principles · Presents walk-throughs of data analysis tasks using R and example datasets. All R commands are presented and explained in order to enable the reader to carry out these tasks themselves. · Uses outputs from a large range of molecular biology platforms including DNA methylation and genotyping microarrays; RNA-seq, genome sequencing, ChIP-seq and bisulphite sequencing; and high-throughput phenotypic screens. · Gives worked-out examples geared towards problems encountered in cancer research, which can also be applied across many areas of molecular biology and medical research. This book has been developed over years of training biological scientists and clinicians to analyse the large datasets available in their cancer research projects. It is appropriate for use as a textbook or as a practical book for biological scientists looking to gain bioinformatics skills.
R Programming For Bioinformatics
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Author : Robert Gentleman
language : en
Publisher: CRC Press
Release Date : 2008-07-14
R Programming For Bioinformatics written by Robert Gentleman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-14 with Mathematics categories.
Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.Drawing on the author's first-hand exper
Computational Genomics With R
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Author : Altuna Akalin
language : en
Publisher: CRC Press
Release Date : 2020-12-16
Computational Genomics With R written by Altuna Akalin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-16 with Mathematics categories.
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.
Multivariate Data Integration Using R
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Author : Kim-Anh Lê Cao
language : en
Publisher: CRC Press
Release Date : 2021-11-08
Multivariate Data Integration Using R written by Kim-Anh Lê Cao and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-08 with Mathematics categories.
Large biological data, which are often noisy and high-dimensional, have become increasingly prevalent in biology and medicine. There is a real need for good training in statistics, from data exploration through to analysis and interpretation. This book provides an overview of statistical and dimension reduction methods for high-throughput biological data, with a specific focus on data integration. It starts with some biological background, key concepts underlying the multivariate methods, and then covers an array of methods implemented using the mixOmics package in R. Features: Provides a broad and accessible overview of methods for multi-omics data integration Covers a wide range of multivariate methods, each designed to answer specific biological questions Includes comprehensive visualisation techniques to aid in data interpretation Includes many worked examples and case studies using real data Includes reproducible R code for each multivariate method, using the mixOmics package The book is suitable for researchers from a wide range of scientific disciplines wishing to apply these methods to obtain new and deeper insights into biological mechanisms and biomedical problems. The suite of tools introduced in this book will enable students and scientists to work at the interface between, and provide critical collaborative expertise to, biologists, bioinformaticians, statisticians and clinicians.
Statistics And Data Analysis For Microarrays Using R And Bioconductor
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Author : Sorin Draghici
language : en
Publisher: CRC Press
Release Date : 2016-04-19
Statistics And Data Analysis For Microarrays Using R And Bioconductor written by Sorin Draghici and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Computers categories.
Richly illustrated in color, Statistics and Data Analysis for Microarrays Using R and Bioconductor, Second Edition provides a clear and rigorous description of powerful analysis techniques and algorithms for mining and interpreting biological information. Omitting tedious details, heavy formalisms, and cryptic notations, the text takes a hands-on, example-based approach that teaches students the basics of R and microarray technology as well as how to choose and apply the proper data analysis tool to specific problems. New to the Second EditionCompletely updated and double the size of its predecessor, this timely second edition replaces the commercial software with the open source R and Bioconductor environments. Fourteen new chapters cover such topics as the basic mechanisms of the cell, reliability and reproducibility issues in DNA microarrays, basic statistics and linear models in R, experiment design, multiple comparisons, quality control, data pre-processing and normalization, Gene Ontology analysis, pathway analysis, and machine learning techniques. Methods are illustrated with toy examples and real data and the R code for all routines is available on an accompanying downloadable resource. With all the necessary prerequisites included, this best-selling book guides students from very basic notions to advanced analysis techniques in R and Bioconductor. The first half of the text presents an overview of microarrays and the statistical elements that form the building blocks of any data analysis. The second half introduces the techniques most commonly used in the analysis of microarray data.
Xml For Bioinformatics
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Author : Ethan Cerami
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-06-02
Xml For Bioinformatics written by Ethan Cerami and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-02 with Computers categories.
Introduction The goal of this book is to introduce XML to a bioinformatics audience. It does so by introducing the fundamentals of XML, Document Type De?nitions (DTDs), XML Namespaces, XML Schema, and XML parsing, and illustrating these concepts with speci?c bioinformatics case studies. The book does not assume any previous knowledge of XML and is geared toward those who want a solid introduction to fundamental XML concepts. The book is divided into nine chapters: Chapter 1: Introduction to XML for Bioinformatics. This chapter provides an introduction to XML and describes the use of XML in biological data exchange. A bird’s-eye view of our ?rst case study, the Distributed Annotation System (DAS), is provided and we examine a sample DAS XML document. The chapter concludes with a discussion of the pros and cons of using XML in bioinformatic applications. Chapter 2: Fundamentals of XML and BSML. This chapter introduces the fundamental concepts of XML and the Bioinformatic Sequence Markup Language (BSML). We explore the origins of XML, de?ne basic rules for XML document structure, and introduce XML Na- spaces. We also explore several sample BSML documents and visualize these documents in the TM Rescentris Genomic Workspace Viewer.
An Introduction To Applied Multivariate Analysis With R
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Author : Brian Everitt
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-04-23
An Introduction To Applied Multivariate Analysis With R written by Brian Everitt and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-23 with Mathematics categories.
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Statistical Analysis Of Microbiome Data With R
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Author : Yinglin Xia
language : en
Publisher: Springer
Release Date : 2018-10-06
Statistical Analysis Of Microbiome Data With R written by Yinglin Xia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-06 with Computers categories.
This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.
An Introduction To Clustering With R
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Author : Paolo Giordani
language : en
Publisher: Springer Nature
Release Date : 2020-08-27
An Introduction To Clustering With R written by Paolo Giordani and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-27 with Mathematics categories.
The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.
Bioinformatics And Computational Biology Solutions Using R And Bioconductor
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Author : Robert Gentleman
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-29
Bioinformatics And Computational Biology Solutions Using R And Bioconductor written by Robert Gentleman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-29 with Computers categories.
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.