By MATTHEW.A. ROBERTS, Alvin Berger, Matthew A. Roberts
ISBN-10: 0203997093
ISBN-13: 9780203997093
ISBN-10: 0824758110
ISBN-13: 9780824758110
ISBN-10: 8002281160
ISBN-13: 9788002281160
ISBN-10: 8457961772
ISBN-13: 9788457961778
Reviewing present reports and formerly unpublished study from major laboratories around the globe, Unraveling Lipid Metabolism with Microarrays demonstrates using microarrays and transcriptomic ways to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 specializes in using microarrays to review and comprehend lipid metabolism. With insurance that spans the applied sciences of genomics, transriptomics, and meatabolomics, the textual content comprises studies of released paintings, presents a clean examine new information, and provides formerly unpublished paintings. It explores the position 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 taking a look at lipids, the ebook covers a wide selection of matters, all associated with the learn of lipid metabolism.
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Additional resources for Unraveling Lipid Metabolism With Microarrays
Sample text
A general principle though is that, insofar as possible, the design should yield greatest precision for those comparisons of most interest, which suggests direct rather than indirect comparisons of the relevant samples. [14,20] In the microarray context, replication allows the researcher to identify sources of variability as well as outliers, reduce variation through averaging, permit generalization to a population of interest, and make the conclusions robust. Each kind of replication involves a different type and degree of variability.
Care taken in advance saves time and money, and allows researchers to achieve the greatest benefits from their efforts. DATA PREPROCESSING The raw data resulting from a microarray experiment consist of image files produced by the array scanner. The preprocessing steps of image analysis and normalization are required to reliably quantify the fluorescence intensities. 8 Goldstein and Delorenzi These steps must be carried out prior to any analysis comparing expression levels within or between slides.
This tradeoff can be visualized with a so-called “volcano plot”, which plots the value of a test statistic, such as t (or its nominal p-value), against the log fold change M. Some combination of M and t could better advance biological knowledge while using resources efficiently. There are several ways to deal with the problem of too small a denominator for t. [86] Other methods aim to pool information across genes, either empirically or using a statistical model, thereby stabilizing resulting estimates.
Unraveling Lipid Metabolism With Microarrays by MATTHEW.A. ROBERTS, Alvin Berger, Matthew A. Roberts
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