Normalization of cDNA microarray data using wavelet regressions

被引:13
|
作者
Wang, J [1 ]
Ma, JZ [1 ]
Li, MD [1 ]
机构
[1] Univ Texas, Hlth Sci Ctr, Program Genom & Bioinformat Drug Addict, Dept Psychiat, San Antonio, TX 78229 USA
关键词
cDNA microarray; data normalization; wavelet regression;
D O I
10.2174/1386207043328274
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Normalization is an essential step in microarray data mining and analysis. For cDNA microarray data, the primary purpose of normalization is removing the intensity-dependent bias across different slides within an experimental group or between multiple groups. The locally weighted regression (lowess) procedure has been widely used for this purpose but can be comparatively time consuming when the dataset becomes relatively large. In this study, we applied wavelet regressions, a new smoothing method for recovering a regression function from data that is supposed to outperform other methods in many cases, such as spline or local polynomial fitting, to normalize two cDNA microarray datasets. Relative to the lowess procedure, we found that wavelet regressions not only produced reliable normalization results but also ran much faster. The computing speed represents one of the most important advantages over other algorithms, especially when one is interested in analyzing a large microarray experiment involving hundreds of slides.
引用
收藏
页码:783 / 791
页数:9
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