Multivariate Data Integration Using R (e-bog) af Welham, Zoe Marie
Welham, Zoe Marie (forfatter)

Multivariate Data Integration Using R e-bog

436,85 DKK (inkl. moms 546,06 DKK)
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 integrati...
E-bog 436,85 DKK
Forfattere Welham, Zoe Marie (forfatter)
Udgivet 8 november 2021
Længde 298 sider
Genrer Probability and statistics
Sprog English
Format epub
Beskyttelse LCP
ISBN 9781000472264
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 integrationCovers a wide range of multivariate methods, each designed to answer specific biological questionsIncludes comprehensive visualisation techniques to aid in data interpretationIncludes many worked examples and case studies using real dataIncludes 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.