Description
Meta-analysis and Combining Information in Genetics and Genomics
Chapman & Hall/CRC Computational Biology Series
Authors: Guerra Rudy, Goldstein Darlene R.
Language: EnglishSubjects for Meta-analysis and Combining Information in Genetics and...:
Keywords
Lod Score; Data Set; meta-analysis; Gene Expression Data; genetics; QTL; genomics; QTL Effect; genome-wide linkage; Recombination Fraction; Bayesian; Independent Studies; gene ontology; Go; Rudy Guerra; QTL Mapping; frequentist; Probe Set; Genome Wide Linkage Scans; Posterior Distributions; Phylogenetic Network; Protein DNA Binding; Microarray Data; Maximum Lod Scores; Marker Maps; Differentially Expressed; Genome Wide Scans; Protein Function Prediction; Meta-analysis Methods; QTL Location; FDR Estimation; MRF Model; Wright Fisher Model
87.11 €
In Print (Delivery period: 14 days).
Add to cart the book of Guerra Rudy, Goldstein Darlene R.Publication date: 06-2017
· 15.6x23.4 cm · Paperback
232.80 €
Subject to availability at the publisher.
Add to cart the book of Guerra Rudy, Goldstein Darlene R.Publication date: 07-2009
334 p. · 15.6x23.4 cm · Hardback
Description
/li>Contents
/li>Readership
/li>Biography
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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.
Introductory Material. Similar Data Types I: Genotype Data. Similar Data Types II: Gene Expression Data. Combining Different Data Types. References. Index.
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).
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