Molecular Data Analysis Using R

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Molecular Data Analysis Using R

Csaba Ortutay,Zsuzsanna Ortutay
Molecular Data Analysis Using R Book PDF

Author : Csaba Ortutay,Zsuzsanna Ortutay
Publisher : John Wiley & Sons
Page : 354 pages
File Size : 50,9 Mb
Release : 2017-02-06
Category : Medical
ISBN : 9781119165026

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Molecular Data Analysis Using R by Csaba Ortutay,Zsuzsanna Ortutay Pdf

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.

Computer Simulation and Data Analysis in Molecular Biology and Biophysics

Victor Bloomfield
Computer Simulation and Data Analysis in Molecular Biology and Biophysics Book PDF

Author : Victor Bloomfield
Publisher : Springer Science & Business Media
Page : 325 pages
File Size : 52,8 Mb
Release : 2009-06-05
Category : Science
ISBN : 9781441900838

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Computer Simulation and Data Analysis in Molecular Biology and Biophysics by Victor Bloomfield Pdf

This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive.

Microarray Data

Shailaja R. Deshmukh,Sudha G. Purohit
Microarray Data Book PDF

Author : Shailaja R. Deshmukh,Sudha G. Purohit
Publisher : Alpha Science International, Limited
Page : 354 pages
File Size : 50,8 Mb
Release : 2007
Category : Business & Economics
ISBN : STANFORD:36105131675089

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Microarray Data by Shailaja R. Deshmukh,Sudha G. Purohit Pdf

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.

Analysis of Phylogenetics and Evolution with R

Emmanuel Paradis
Analysis of Phylogenetics and Evolution with R Book PDF

Author : Emmanuel Paradis
Publisher : Springer Science & Business Media
Page : 221 pages
File Size : 55,7 Mb
Release : 2006-11-25
Category : Science
ISBN : 9780387351001

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Analysis of Phylogenetics and Evolution with R by Emmanuel Paradis Pdf

This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.

Data Analysis in Molecular Biology and Evolution

Xuhua Xia
Data Analysis in Molecular Biology and Evolution Book PDF

Author : Xuhua Xia
Publisher : Springer Science & Business Media
Page : 280 pages
File Size : 53,5 Mb
Release : 2007-05-08
Category : Science
ISBN : 9780306468933

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Data Analysis in Molecular Biology and Evolution by Xuhua Xia Pdf

Data Analysis in Molecular Biology and Evolution introduces biologists to DAMBE, a proprietary, user-friendly computer program for molecular data analysis. The unique combination of this book and software will allow biologists not only to understand the rationale behind a variety of computational tools in molecular biology and evolution, but also to gain instant access to these tools for use in their laboratories. Data Analysis in Molecular Biology and Evolution serves as an excellent resource for advanced level undergraduates or graduates as well as for professionals working in the field.

Primer to Analysis of Genomic Data Using R

Cedric Gondro
Primer to Analysis of Genomic Data Using R Book PDF

Author : Cedric Gondro
Publisher : Springer
Page : 270 pages
File Size : 55,7 Mb
Release : 2015-05-18
Category : Medical
ISBN : 9783319144757

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Primer to Analysis of Genomic Data Using R by Cedric Gondro Pdf

Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher’s website.

Data Analysis for the Life Sciences with R

Rafael A. Irizarry,Michael I. Love
Data Analysis for the Life Sciences with R Book PDF

Author : Rafael A. Irizarry,Michael I. Love
Publisher : CRC Press
Page : 461 pages
File Size : 45,6 Mb
Release : 2016-10-04
Category : Mathematics
ISBN : 9781498775861

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Data Analysis for the Life Sciences with R by Rafael A. Irizarry,Michael I. Love Pdf

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.

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Robert Gentleman,Vincent Carey,Wolfgang Huber,Rafael Irizarry,Sandrine Dudoit
Bioinformatics and Computational Biology Solutions Using R and Bioconductor Book PDF

Author : Robert Gentleman,Vincent Carey,Wolfgang Huber,Rafael Irizarry,Sandrine Dudoit
Publisher : Springer Science & Business Media
Page : 478 pages
File Size : 50,5 Mb
Release : 2005-12-29
Category : Computers
ISBN : 9780387293622

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Bioinformatics and Computational Biology Solutions Using R and Bioconductor by Robert Gentleman,Vincent Carey,Wolfgang Huber,Rafael Irizarry,Sandrine Dudoit Pdf

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.

Using R at the Bench

Martina Bremer,Rebecca W. Doerge
Using R at the Bench Book PDF

Author : Martina Bremer,Rebecca W. Doerge
Publisher : Unknown
Page : 0 pages
File Size : 53,8 Mb
Release : 2015
Category : Bioinformatics
ISBN : 1621821129

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Using R at the Bench by Martina Bremer,Rebecca W. Doerge Pdf

Using R at the Bench: Step-by-Step Data Analytics for Biologists is a convenient bench-side handbook for biologists, designed as a handy reference guide for elementary and intermediate statistical analyses using the free/public software package known as "R." The expectations for biologists to have a more complete understanding of statistics are growing rapidly. New technologies and new areas of science, such as microarrays, next-generation sequencing, and proteomics, have dramatically increased the need for quantitative reasoning among biologists when designing experiments and interpreting results. Even the most routine informatics tools rely on statistical assumptions and methods that need to be appreciated if the scientific results are to be correct, understood, and exploited fully. Although the original Statistics at the Bench is still available for sale and has all examples in Excel, this new book uses the same text and examples in R. A new chapter introduces the basics of R: where to download, how to get started, and some basic commands and resources. There is also a new chapter that explains how to analyze next-generation sequencing data using R (specifically, RNA-Seq). R is powerful statistical software with many specialized packages for biological applications and Using R at the Bench: Step-by-Step Data Analytics for Biologists is an excellent resource for those biologists who want to learn R. This handbook for working scientists provides a simple refresher for those who have forgotten what they once knew and an overview for those wishing to use more quantitative reasoning in their research. Statistical methods, as well as guidelines for the interpretation of results, are explained using simple examples. Throughout the book, examples are accompanied by detailed R commands for easy reference.

Statistics and Data Analysis for Microarrays Using R and Bioconductor

Sorin Draghici
Statistics and Data Analysis for Microarrays Using R and Bioconductor Book PDF

Author : Sorin Draghici
Publisher : CRC Press
Page : 1036 pages
File Size : 46,7 Mb
Release : 2016-04-19
Category : Computers
ISBN : 9781439809761

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Statistics and Data Analysis for Microarrays Using R and Bioconductor by Sorin Draghici Pdf

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,

Molecular Evolution

Ziheng Yang
Molecular Evolution Book PDF

Author : Ziheng Yang
Publisher : Oxford University Press
Page : 509 pages
File Size : 52,6 Mb
Release : 2014
Category : Science
ISBN : 9780199602605

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Molecular Evolution by Ziheng Yang Pdf

This book presents and explains modern statistical methods and computational algorithms for the comparative analysis of genetic sequence data in the fields of molecular evolution, molecular phylogenetics, statistical phylogeography, and comparative genomics. The book offers numerous examples of real data analysis and numerical calculations to illustrate the theory, in addition to the working problems at the end of each chapter. The coverage of maximum likelihood and Bayesian methods are in particular up-to-date, comprehensive, and authoritative.

Applied Statistical Genetics with R

Andrea S. Foulkes
Applied Statistical Genetics with R Book PDF

Author : Andrea S. Foulkes
Publisher : Springer Science & Business Media
Page : 264 pages
File Size : 54,7 Mb
Release : 2009-04-28
Category : Science
ISBN : 9780387895543

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Applied Statistical Genetics with R by Andrea S. Foulkes Pdf

Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.

Introduction to Bioinformatics with R

Edward Curry
Introduction to Bioinformatics with R Book PDF

Author : Edward Curry
Publisher : CRC Press
Page : 311 pages
File Size : 47,8 Mb
Release : 2020-11-02
Category : Mathematics
ISBN : 9781351015301

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Introduction to Bioinformatics with R by Edward Curry Pdf

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.

Chemometrics with R

Ron Wehrens
Chemometrics with R Book PDF

Author : Ron Wehrens
Publisher : Springer Science & Business Media
Page : 286 pages
File Size : 54,5 Mb
Release : 2011-01-20
Category : Science
ISBN : 9783642178412

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Chemometrics with R by Ron Wehrens Pdf

"Chemometrics with R" offers readers an accessible introduction to the world of multivariate statistics in the life sciences, providing a complete description of the general data analysis paradigm, from exploratory analysis to modeling to validation. Several more specific topics from the area of chemometrics are included in a special section. The corresponding R code is provided for all the examples in the book; scripts, functions and data are available in a separate, publicly available R package. For researchers working in the life sciences, the book can also serve as an easy-to-use primer on R.

Statistical Learning in Genetics

Daniel Sorensen
Statistical Learning in Genetics Book PDF

Author : Daniel Sorensen
Publisher : Springer Nature
Page : 696 pages
File Size : 42,5 Mb
Release : 2023-09-19
Category : Mathematics
ISBN : 9783031358517

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Statistical Learning in Genetics by Daniel Sorensen Pdf

This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, where millions of genetic markers for each individual are coupled with medical records, generating an unparalleled resource for linking human genetic variation to human biology and disease. Similar developments have taken place in animal and plant breeding, where genetic marker information is combined with production traits. An important task for the statistical geneticist is to adapt, construct and implement models that can extract information from these large-scale data. An initial step is to understand the methodology that underlies the probability models and to learn the modern computer-intensive methods required for fitting these models. The objective of this book, suitable for readers who wish to develop analytic skills to perform genomic research, is to provide guidance to take this first step. This book is addressed to numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, considerably more detail in explanations and derivations is offered. It is written in a concise style and examples are used profusely. A large proportion of the examples involve programming with the open-source package R. The R code needed to solve the exercises is provided. The MarkDown interface allows the students to implement the code on their own computer, contributing to a better understanding of the underlying theory. Part I presents methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models. Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on False Discovery Rate assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods. Part III consists of exercises and their solutions. Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus.