Meta-analysis and combining information in genetics and by Rudy Guerra, Darlene R. Goldstein

By Rudy Guerra, Darlene R. Goldstein

content material: Pt. zero. Introductory fabric --
1. short creation to meta-analysis, genetics, and genomics / Darlene R. Goldstein and Rudy Guerra --
Pt I. related information varieties I: Genotype info --
2. Combining info throughout genome-wide linkage scans / Carol J. Etzel and Tracy J. Costello --
three. Genome seek meta-analysis (GSMA): a nonparametric approach for meta-analysis of genome-wide linkage stories / Cathryn M. Lewis --
four. Heterogeneity in meta-analysis of quantitative trait linkage reviews / Hans C. van Houwelingen and Jeremie J. P. Lebrec --
five. empirical Bayesian framework for QTL genome-wide scans / Kui Zhang ... [et al.] --
Pt. II. comparable facts varieties II: Gene Expression facts --
6. Composite speculation trying out: an technique equipped on intersection-union exams and Bayesian posterior chances / Stephen Erickson, Kyoungmi Kim and David B. Allison --
7. Frequentist and Bayesian mistakes pooling equipment for reinforcing statistical energy in small pattern microarray info research / Jae ok. Lee, Hyung Jun Cho and Michael O'Connell --
eight. importance trying out for small microarray experiments / Charles Kooperberg ... [et al.] --
nine. comparability of meta-analysis to mixed research of a replicated microarray examine / Darlene R. Goldstein ... [et al.] --
10. replacement probe set definitions for combining microarray info throughout stories utilizing varied types of Affymetrix oligonucleotide arrays / Jeffrey S. Morris ... [et al.] --
eleven. Gene ontology-based meta-analysis of genome-scale experiments / Chad A. Shaw --
Pt. III. Combining varied information forms --
12. Combining genomic information in human experiences / Debashis Ghosh, Daniel Rhodes and Arul Chinnaiyan --
thirteen. evaluate of statistical ways for expression trait loci mapping / Christina Kendziorski and Meng Chen --
14. Incorporating cross annotation info in expression trait loci mapping / J. Blair Christian and Rudy Guerra --
15. misclassification version for inferring transcriptional regulatory networks / Ning sunlight and Hongyu Zhao --
sixteen. info integration for the examine of protein interactions / Fengzhu solar ... [et al.] --
17. Gene timber, species timber, and species networks / Luay Nakhleh, Derek Ruths and Hideki Innan.

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COMBINING INFORMATION IN GENETICS AND GENOMICS 17 The chapters in Part I of this book describe methods for combining results of genomewide linkage scans. In Chapter 2, Etzel and Costello review several methods for meta-analysis of genome-wide scans and provide practical advice for a variety of typical situations. Lewis (Chapter 3) also gives an overview of the problem and details the widely used GSMA method. van Houwelingen and Lebrec (Chapter 4) explore the issue of heterogeneity in the meta-analysis of quantitative trait linkage studies, giving examples and adapting classical parameter estimate combination methods to the QTL mapping scenario.

Quantile normalization forces equality of quantiles across samples. Such a normalization is appropriate assuming that the true distributions of intensities are the same in all samples (of course, the same probe may occur at different quantiles across samples). , 2003b). For parameter identifiability, it is assumed that j αj = 0. The model is fit via median polish (Tukey, 1977); the estimated chip effect µ ˆi is the RMA value of the probe set for chip i. 1 for additional examples), but more commonly it is results rather than primary data that are combined.

Org), along with a practical application combining information at the other end of the spectrum, from gene lists obtained from different microarray experiments. 4 Combining different data types Many different types of high-throughput biomolecular data are now routinely generated. In addition to the genotype and gene expression data analyzed in Parts I and II, there are now assays for protein-DNA binding, protein-protein interactions, copy number variation (CNV), and sequence determination. It is clear that the standard techniques used in meta-analysis cannot be straightforwardly applied to the problem of combining these diverse, heterogeneous data.

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