Meta-analysis and Combining Information in Genetics and Genomics

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A01=Darlene R. Goldstein
A01=Rudy Guerra
advanced data synthesis
Author_Darlene R. Goldstein
Author_Rudy Guerra
Bayesian
bioinformatics analysis
Category=PB
combining heterogeneous biological datasets
data
Data Set
Differentially Expressed
discovery
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
expression
false
FDR Estimation
fraction
frequentist
Gene Expression Data
gene ontology
genetic association studies
genetics
Genome Wide Linkage Scans
Genome Wide Scans
genome-wide linkage
genomics
Go
Independent Studies
loci
Lod Score
Marker Maps
Maximum Lod Scores
meta-analysis
Meta-analysis Methods
Microarray Data
MRF Model
multi-omics integration
Phylogenetic Network
Posterior Distributions
Probe Set
Protein DNA Binding
Protein Function Prediction
QTL
QTL Effect
QTL Location
QTL Mapping
quantitative
quantitative trait analysis
rate
recombination
Recombination Fraction
Rudy Guerra
statistical genetics methods
trait
Wright Fisher Model

Product details

  • ISBN 9781584885221
  • Weight: 1500g
  • Dimensions: 156 x 234mm
  • Publication Date: 07 Jul 2009
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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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