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Meta-analysis & combining information in genetics (Interdisciplinary statistics series) Chapman & Hall/CRC Mathematical and Computational Biology Series

Langue : Anglais

Auteurs :

Couverture de l’ouvrage Meta-analysis & combining information in genetics (Interdisciplinary statistics series)
With contributions from leading experts, Meta-Analysis and Combining Information in Genetics provides theory, methods, and guiding principles for meta-analysis and combining information for various types of genetic studies. The book begins with an introduction to meta-analysis and combining information as well as statistical genetics, bioinformatics, and proteomics. Addressing the complications inherent in comparing and replicating genetic studies, the contributors carefully examine microarrays, gene mapping, and proteomics. They detail the various techniques used to analyze and combine data from different types of studies. This is the first book devoted to meta-analysis in genetics.

Introductory Material

A brief introduction to meta-analysis, genetics, and genomics Darlene R. Goldstein and Rudy Guerra

Similar Data Types I: Genotype Data

Combining information across genome-wide linkage scans Carol J. Etzel and Tracy J. Costello

Genome search meta-analysis (GSMA): a nonparametric method for meta-analysis of genome-wide linkage studies Cathryn M. Lewis

Heterogeneity in meta-analysis of quantitative trait linkage studies Hans C. van Houwelingen and Jérémie J.P. Lebrec

An empirical Bayesian framework for QTL genome-wide scans Kui Zhang, Howard Wiener, T. Mark Beasley, Christopher I. Amos, and David B. Allison

Similar Data Types II: Gene Expression Data

Composite hypothesis testing: an approach built on intersection-union tests and Bayesian posterior probabilities Stephen Erickson, Kyoungmi Kim, and David B. Allison

Frequentist and Bayesian error pooling methods for enhancing statistical power in small sample microarray data analysis Jae K. Lee, Hyung Jun Cho, and Michael O’Connell

Significance testing for small microarray experiments Charles Kooperberg, Aaron Aragaki, Charles C. Carey, and Suzannah Rutherford

Comparison of meta-analysis to combined analysis of a replicated microarray study Darlene R. Goldstein, Mauro Delorenzi, Ruth Luthi-Carter, and Thierry Sengstag

Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays Jeffrey S. Morris, Chunlei Wu, Kevin R. Coombes, Keith A. Baggerly, Jing Wang, and Li Zhang

Gene ontology-based meta-analysis of genome-scale experiments Chad A. Shaw

Combining Different Data Types

Combining genomic data in human studies Debashis Ghosh, Daniel Rhodes, and Arul Chinnaiyan

An overview of statistical approaches for expression trait loci mapping Christina Kendziorski and Meng Chen

Incorporating GO annotation information in expression trait loci mapping J. Blair Christian and Rudy Guerra

A misclassification model for inferring transcriptional regulatory networks Ning Sun and Hongyu Zhao

Data integration for the study of protein interactions Fengzhu Sun, Ting Chen, Minghua Deng, Hyunju Lee, and Zhidong Tu

Gene trees, species trees, and species networks Luay Nakhleh, Derek Ruths, and Hideki Innan



Statisticians in genetics, clinical trials, and computational biology; students and researchers in computational biology and bioinformatics; and biologists in genetics.

Date de parution :

15.6x23.4 cm

Disponible chez l'éditeur (délai d'approvisionnement : 13 jours).

Prix indicatif 87,67 €

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Date de parution :

Ouvrage de 334 p.

15.6x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 13 jours).

Prix indicatif 112,69 €

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