Abstract
Microarray data are affected by experimental variability, which is a combination of systematic and stochastic variability. The basic task of microarray preprocessing is to extract quantities of interest from the data while correcting for systematic variations and controlling the stochastic variability. In this exercise we explore the concepts of (log-)ratios, the role of background correction, the idea of shrinkage estimation, and the generalized logarithm. Some tools for this are provided by the package vsn.
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© 2008 Springer Science+Business Media, LLC
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Huber, W. (2008). Fold Changes, Log Ratios, Background Correction, Shrinkage Estimation, and Variance Stabilization. In: Bioconductor Case Studies. Use R!. Springer, New York, NY. https://doi.org/10.1007/978-0-387-77240-0_5
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DOI: https://doi.org/10.1007/978-0-387-77240-0_5
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-77239-4
Online ISBN: 978-0-387-77240-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)