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By Alvin Berger, Matthew A. Roberts

Reviewing present stories and formerly unpublished examine from top laboratories world wide, Unraveling Lipid Metabolism with Microarrays demonstrates using microarrays and transcriptomic ways to elucidate the organic functionality of lipids. With contributions from world-class researchers, the ebook makes a speciality of using microarrays to check and comprehend lipid metabolism.

With assurance that spans the applied sciences of genomics, transriptomics, and meatabolomics, the textual content includes reports of released paintings, presents a clean examine new info, and provides formerly unpublished paintings. It explores the function of fatty acids in gene expression and a number of the results lipids have at the telephone cycle, ldl cholesterol metabolism, and insulin secretion. Taking a proteomic method of lipids, the booklet covers a wide selection of topics, all associated with the research of lipid metabolism

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Extra info for Understanding Lipid Metabolism with Microarrays and Other Omic Approaches

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More flexible methods take into account intensity-dependent dye bias, as well as spatial variation in intensities across the slide, generally using some form of local regression, usually lowess or loess (two different types of local regression methods Copyright © 2005 by Marcel Dekker 12 Goldstein and Delorenzi whose names derive from “locally weighted scatterplot smoothing” [29,30,75 – 77]). Classical (global) regression draws a single line through all points of a data set; every point, no matter how far from the A value of interest, has some influence on the predicted M value for the given A.

As a typical microarray experiment measures expression levels for several thousands of genes simultaneously, we are faced with an extreme form of multiple testing when assessing the statistical significance of the results. 01. This finding cannot be characterized as “significant”, as it is inevitable that such small p-values will occur by chance when considering a large set of genes, most of which will not be differentially expressed. Special problems arising from the multiplicity aspect include defining an appropriate false positive (type I error) rate for the overall experiment and devising powerful multiple testing procedures that control this error rate and account for the joint Copyright © 2005 by Marcel Dekker Statistical Design and Data Analysis 19 distribution of the gene expression levels.

An alternative to using all genes is to normalize on the basis of a subset of genes, for example, control sequences spotted for the purpose of normalization or a set of constantly expressed socalled “housekeeping” genes. One problem is that there is less information in a smaller number of genes, so the normalizing transformation will not be as stable. A different problem is with the concept of housekeeping genes, as there does not appear to be a set of constantly expressed genes. These are further reasons why restricted coverage arrays remain problematic.

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