The Analysis of Gene Expression Data
The Analysis of Gene Expression Data Books
Product Description
This book presents matter-of-fact approaches for the analysis of data from gene expression microarrays. Each chapter describes the conceptual and matter-of-fact underpinning for a statistical tool and its implementation in software. Methods cover all aspects of statistical analysis of microarrays, from annotation and filtering to clustering and classification. Chapters are written by the developers of the software. All software packages described are free to academic users. The book includes coverage of various packages that are part of the Bioconductor project and several correlated R tools. The materials presented cover a range of software tools calculated for varied audiences. Some chapters describe simple menu-driven software in a user-friendly make, and are calculated to be accessible to microarray data analysts without formal quantitative training. Most chapters are directed at microarray data analysts with master-level training in computer science, biostatistics or bioinformatics. A minority of more advanced chapters are proposed for doctoral students and researchers. The team of editors is from the Johns Hopkins Schools of Medicine and Broadcast Health and has been caught up with developing methods and software for microarray data analysis since the inception of this technology. Giovanni Parmigiani is Associate Professor of Oncology, Pathology and Biostatistics. He is the author of the book on “Modeling in Medical choice Making,” a fellow of the ASA, and a recipient of the Savage Awards for Bayesian statistics. Elizabeth S. Garrett is Assistant Professor of Oncology and Biostatistics, and recipient of the Abbey Award for statistical education. Rafael A Irizarry is Assistant Professor of Biostatistics, and recipient of the Noether Award for non-parametric statistics. Scott L. Zeger is Professor and chair of Biostatistics. He is co-author of the book “Longitudinal Data Analysis,” a fellow of the ASA and recipient of the Spiegelman Award for broadcast health statistics.
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I thumbed through a copy of this book, but I admit I haven’t read it. It just doesn’t meet my needs.
It’s about a number of software packages available for analyzing microarray and correlated kinds of data. Different sections of the book seemed to range from brief surveys to honestly detailed how-to chapters, by different authors, describing different packages.
I’m interested in microarray data analysis, but I want the more theoretical material – the basic computations and the principles behind them. Details of statistical and analytic techniques were too scattered, if bestow at all, to hold my interest.
If you want the biological or medical answers from the programs without worrying too much about how they’re derived, maybe this book will help you. Or maybe not, I’m not the one to judge – three stars, just because I had to give some number.
Rating: 3 / 5
Some of the software commands in this book are already outdated, esp. in bioconductor. If you need to question a microarray data and you do not know much about the algorithms, this book gives an overview what is available. To get more insights about the theory, can check out EXPLORATORY AND ANALYSIS OF DNA MICROARRAY AND PROTEIN ARRAY or the research papers published by the authors. There is no one best method for analyzing microarray data, so don’t guess a miracle from this book or any others.
Rating: 4 / 5