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 advent to meta-analysis, genetics, and genomics / Darlene R. Goldstein and Rudy Guerra --
Pt I. comparable facts varieties I: Genotype information --
2. Combining info throughout genome-wide linkage scans / Carol J. Etzel and Tracy J. Costello --
three. Genome seek meta-analysis (GSMA): a nonparametric strategy for meta-analysis of genome-wide linkage experiences / Cathryn M. Lewis --
four. Heterogeneity in meta-analysis of quantitative trait linkage experiences / Hans C. van Houwelingen and Jeremie J. P. Lebrec --
five. empirical Bayesian framework for QTL genome-wide scans / Kui Zhang ... [et al.] --
Pt. II. related info varieties II: Gene Expression facts --
6. Composite speculation checking out: an procedure equipped on intersection-union exams and Bayesian posterior possibilities / Stephen Erickson, Kyoungmi Kim and David B. Allison --
7. Frequentist and Bayesian errors pooling tools 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 learn / Darlene R. Goldstein ... [et al.] --
10. substitute probe set definitions for combining microarray info throughout experiences 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 info forms --
12. Combining genomic information in human reports / Debashis Ghosh, Daniel Rhodes and Arul Chinnaiyan --
thirteen. assessment of statistical ways for expression trait loci mapping / Christina Kendziorski and Meng Chen --
14. Incorporating pass annotation details 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. facts integration for the research of protein interactions / Fengzhu sunlight ... [et al.] --
17. Gene bushes, species timber, and species networks / Luay Nakhleh, Derek Ruths and Hideki Innan.
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Extra info for Meta-analysis and combining information in genetics and genomics
Examples of such variation include differences in sample preparation, scanning intensities, and variability among arrays. Ideally, any observed differences in gene expression remaining after normalization are due to biologically meaningful differential expression rather than experimental artifacts. Many methods have been proposed for normalization of M -values from two-channel arrays. The simplest methods, such as scaling or median-centering, are generally not adequate for removing technical artifacts.
This view is somewhat undermined by the fact that the stability of the ranking depends on the rank, the differences between underlying values and sample size. The measure that is ranked in the original analysis will also affect the ordering, and independent studies may well have used different statistics. Ranking does, however, take care of the problem of different measurement types and scales for different types of microarrays. 6 Combining decisions At the crudest level, the conclusions of statistical hypothesis tests can be combined.
2) Under the null hypothesis, the distribution of Q is approximately χ2k−1 . 2). A major limitation of this approach, though, is the low power of the test, due to small sample sizes or a small number of studies, to detect even substantial heterogeneity. 10. 3) is typically favored. Where possible, heterogeneity should be scrutinized rather than ignored, with an aim toward explaining important study differences (Bailey, 1987). 3 Combining parameter estimates Parameter estimates are typically combined using either a fixed effects or random effects model, depending on the absence or presence of heterogeneity between results of the different studies (Cooper and Hedges, 1994).