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Meta-analysis and Combining Information in Genetics and Genomics : Chapman & Hall/CRC Computational Biology Series - Darlene R. Goldstein

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Meta-analysis and Combining Information in Genetics and Genomics

By: Darlene R. Goldstein, Rudy Guerra

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Hardcover | 7 January 2009 | Edition Number 1

At a Glance

Hardcover
360 Pages

Dimensions(cm)
23.4 x 15.6 x 2.54

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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.

Industry Reviews

There is a particularly good chapter comparing different methods for analysing two similar microarray studies! The book would be suitable for someone who is new to the analysis of high dimensional genomic data. --S.E. Lazic and F. Hoffmann-La Roche, Journal of the Royal Statistical Society: Series A, Vol. 174, October 2011 ! the book will be most useful for students and researchers who wish to see what developments are currently in progress in this important area. That said, there is a wealth of material here for the non-expert wishing to move into the area. And, unlike some edited tomes in past ages, the articles here have clearly been carefully meshed to give a coherent picture. --International Statistical Review (2011), 79, 1

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You Can Find This Book In

Non-Fiction Science Biology, Life Sciences Life Sciences in General Genetics excluding Medical Mathematics Probability & Statistics Evolution Engineering & Technology Biochemical Engineering Biotechnology

Introductory Material	p. 1
A brief introduction to meta-analysis, genetics and genomics	p. 3
Introduction	p. 3
Combining information	p. 4
Genetics	p. 8
Genomics	p. 12
Combining information in genetics and genomics	p. 16
Similar Data Types I: Genotype Data	p. 21
Combining information across genome-wide linkage scans	p. 23
Introduction	p. 23
Meta-analysis of genome-wide linkage scans	p. 23
Choice of meta-analysis method	p. 28
Discussion	p. 31
Appendix A	p. 32
Genome search meta-analysis (GSMA): a nonparametric method for meta-analysis of genome-wide linkage studies Cathryn M. Lewis	p. 33
Introduction	p. 33
GSMA: Genome Search Meta-Analysis method	p. 34
Power to detect linkage using GSMA	p. 40
Extensions of GSMA	p. 42
Limitations of the GSMA	p. 43
Disease studies using GSMA	p. 44
GSMA software	p. 46
Conclusions	p. 46
Heterogeneity in meta-analysis of quantitative trait linkage studies	p. 49
Introduction	p. 49
The classical meta-analytic method	p. 51
Extracting relevant information from individual studies	p. 54
Example	p. 58
Discussion	p. 63
An empirical Bayesian framework for QTL genome-wide scans	p. 67
Introduction	p. 67
Methods	p. 69
Results	p. 72
Discussion	p. 79
Similar Data Types II: Gene Expression Data	p. 81
Composite hypothesis testing: an approach built on intersection-union tests and Bayesian posterior probabilities	p. 83
Introduction	p. 83
Composite hypothesis testing	p. 84
Assessing the significance of a composite hypothesis test	p. 86
Measuring Bayesian significance evidence in composite hypothesis testing	p. 88
Combining posterior probabilities in a Bayesian IUT	p. 90
Issues and challenges	p. 91
Summary	p. 92
Software availability	p. 93
Frequentist and Bayesian error pooling methods for enhancing statistical power in small sample microarray data analysis	p. 95
Introduction	p. 95
Local pooled error test	p. 97
Empirical Bayes heterogeneous error model (HEM)	p. 103
Conclusion	p. 112
Significance testing for small microarray experiments	p. 113
Introduction	p. 113
Methods	p. 114
Data	p. 119
Results	p. 121
Discussion	p. 131
Appendix: Array preprocessing	p. 134
Comparison of meta-analysis to combined analysis of a replicated microarray study	p. 135
Introduction	p. 135
Study description	p. 136
Statistical analyses	p. 136
Results	p. 141
Discussion	p. 153
Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays	p. 157
Introduction	p. 157
Combining microarray data across studies and platforms	p. 158
Meta-analysis with Affymetrix oligonucleotide arrays	p. 161
Partial probe sets method	p. 162
Example 1: CAMDA 2003 lung cancer data	p. 163
Full-length transcript-based probe sets method	p. 168
Example 2: Lung cell line data	p. 171
Discussion	p. 174
Gene ontology-based meta-analysis of genome-scale experiments	p. 175
Introduction	p. 175
Ontologies	p. 175
The Gene Ontology	p. 176
Statistical methods	p. 182
Application to stem cell data	p. 189
Conclusions	p. 197
Combining Different Data Types	p. 199
Combining genomic data in human studies	p. 201
Introduction	p. 201
Genomic data integration in cancer	p. 202
Combining data from related technologies: cDNA arrays	p. 203
Combining data from different technologies	p. 206
In vivo/in vitro genomic data integration	p. 209
Software availability	p. 210
Discussion	p. 211
An overview of statistical approaches for expression trait loci mapping	p. 213
Introduction	p. 213
ETL mapping data and methods	p. 214
Evaluation of ETL mapping methods	p. 217
Discussion	p. 222
Incorporating GO annotation information in expression trait loci mapping	p. 225
Introduction	p. 225
Expression trait loci mapping	p. 226
Data	p. 228
Methodology	p. 230
Simulations	p. 233
Results	p. 238
Conclusions	p. 241
A misclassification model for inferring transcriptional regulatory networks	p. 243
Introduction	p. 243
Methods	p. 244
Simulation results	p. 250
Application to yeast cell cycle data	p. 253
Summary	p. 255
Data integration for the study of protein interactions	p. 259
Introduction	p. 259
Data sources	p. 261
Assessing the reliability of protein interaction data	p. 262
Protein function prediction using protein interaction data	p. 266
Discussion	p. 273
Gene trees, species trees, and species networks	p. 275
Introduction	p. 275
Gene tree incongruence	p. 277
Lineage sorting	p. 281
Gene duplication and loss	p. 284
Reticulate evolution	p. 286
Distinguishing lineage sorting from HGT	p. 290
Summary	p. 292
References	p. 295
Index	p. 329
Table of Contents provided by Ingram. All Rights Reserved.

Meta-analysis and Combining Information in Genetics and Genomics

At a Glance

Hardcover

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