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Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach

Motakis, E. S., Nason, Guy P., Fryzlewicz, Piotr and Rutter, G. A (2006) Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach. Bioinformatics, 22 (20). pp. 2547-2553. ISSN 1367-4803

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Abstract

Many standard statistical techniques are effective on data that are normally distributed with constant variance. Microarray data typically violate these assumptions since they come from non-Gaussian distributions with a non-trivial mean–variance relationship. Several methods have been proposed that transform microarray data to stabilize variance and draw its distribution towards the Gaussian. Some methods, such as log or generalized log, rely on an underlying model for the data. Others, such as the spread-versus-level plot, do not. We propose an alternative data-driven multiscale approach, called the Data-Driven Haar–Fisz for microarrays (DDHFm) with replicates. DDHFm has the advantage of being ‘distribution-free’ in the sense that no parametric model for the underlying microarray data is required to be specified or estimated; hence, DDHFm can be applied very generally, not just to microarray data.

Item Type: Article
Official URL: http://bioinformatics.oxfordjournals.org/
Additional Information: © 2006 The Authors
Library of Congress subject classification: H Social Sciences > HA Statistics
Q Science > QH Natural history > QH301 Biology
Sets: Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 18 Sep 2009 11:13
URL: http://eprints.lse.ac.uk/25222/

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