Statistical Methods For Microarray Data Analysis

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.

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

Microarrays for simultaneous measurement of redundancy of RNA species are used in fundamental biology as well as in medical research. Statistically,a microarray may be considered as an observation of very high dimensionality equal to the number of expression levels measured on it. In Statistical Methods for Microarray Data Analysis: Methods and Protocols, expert researchers in the field detail many methods and techniques used to study microarrays, guiding the reader from microarray technology to statistical problems of specific multivariate data analysis. Written in the highly successful Methods in Molecular BiologyTM series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Statistical Methods for Microarray Data Analysis: Methods and Protocols aids scientists in continuing to study microarrays and the most current statistical methods.

Microarrays for simultaneous measurement of redundancy of RNA species are used in fundamental biology as well as in medical research. Statistically,a microarray may be considered as an observation of very high dimensionality equal to the number of expression levels measured on it. In Statistical Methods for Microarray Data Analysis: Methods and Protocols, expert researchers in the field detail many methods and techniques used to study microarrays, guiding the reader from microarray technology to statistical problems of specific multivariate data analysis. Written in the highly successful Methods in Molecular BiologyTM series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Statistical Methods for Microarray Data Analysis: Methods and Protocols aids scientists in continuing to study microarrays and the most current statistical methods.

Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data – from getting good data to obtaining meaningful results. Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference. Features many examples throughout using real data from microarray experiments. Computational techniques are integrated into the text. Takes a very practical approach, suitable for statistically-minded biologists. Supported by a Website featuring colour images, software, and data sets. Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.

As microarray technology has matured, data analysis methods have advanced as well. Methods Of Microarray Data Analysis III is the third book in this pioneering series dedicated to the existing new field of microarrays. While initial techniques focused on classification exercises (volume I of this series), and later on pattern extraction (volume II of this series), this volume focuses on data quality issues. Problems such as background noise determination, analysis of variance, and errors in data handling are highlighted. Three tutorial papers are presented to assist with a basic understanding of underlying principles in microarray data analysis, and twelve new papers are highlighted analyzing the same CAMDA'02 datasets: the Project Normal data set or the Affymetrix Latin Square data set. A comparative study of these analytical methodologies brings to light problems, solutions and new ideas. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state of art of microarray data analysis.

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,

After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.