Abstract #300509

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JSM 2003 Abstract #300509
Activity Number: 451
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #300509
Title: Fast Lowess for Normalizing Microarray Data
Author(s): Karla V. Ballman*+ and Ann L. Oberg and Douglas W. Mahoney and Terry M. Therneau
Companies: Mayo Clinic and Mayo Clinic and Mayo Clinic & Mayo Foundation and Mayo Clinic
Address: Cancer Center Statistics, Section of Biostatistics, Rochester, MN, 55905-0001,
Keywords: normalization ; microarray data ; lowess
Abstract:

Various methods have been developed for normalizing high-density oligonucleotide arrays so that meaningful comparisons of gene expression levels can be made across arrays (experiments). The most useful methods are those that use data from all arrays to be compared to form the normalization relation, and that account for the non-linear relationship of intensities among arrays. Commonly used nonlinear normalization techniques include cyclic loess, and quantile normalization. We propose a new method, fast lowess, which is similar in concept to cyclic loess normalization but uses a linear models argument to normalize all arrays at once. Results comparing the performance of cyclic loess, quantile normalization, and fast lowess on simulated and real data will be presented. Both fast lowess and cyclic loess appear to produce better normalizations than quantile normalization.

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