# A course of higher mathematics, vol. 1 by Smirnov V.I.

By Smirnov V.I.

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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.