Meta-analysis and Combining Information in Genetics and Genomics

Rudy Guerra

ISBN 10: 158488522X / ISBN 13: 9781584885221

Published by Taylor & Francis Inc Jul 2009, 2009

About this Item

Neuware - This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources. It addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types, giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations.

Seller Inventory # 9781584885221

Bibliographic Details

• Title: Meta-analysis and Combining Information in Genetics and Genomics
• Publisher: Taylor & Francis Inc Jul 2009
• Publication Date: 2009
• Language: English
• ISBN 10: 158488522X
• ISBN 13: 9781584885221
• Binding: Buch
• Condition: Neu
• Dimensions: 234W x 156H x 0D millimeters 234 millimeters width by 156 millimeters height by 0 millimeters depth
• Weight: 1500 grams

About this title

Synopsis

Novel Techniques for Analyzing and Combining Data from Modern Biological Studies
Broadens the Traditional Definition of Meta-Analysis

With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods, Meta-analysis and Combining Information in Genetics and Genomics looks at how to analyze multiple studies from a broad perspective.

After presenting the basic ideas and tools of meta-analysis, the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types, giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations.

As heterogeneous data sets become more common, biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources.

About the Author

Rudy Guerra is a professor of statistics at Rice University.

Darlene R. Goldstein is a member of the Chair of Statistics research group in the Institut de Mathématiques at the École Polytechnique Fédérale de Lausanne (EPFL).