Abstract
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.
Keywords: cdna microarray, data normalization, wavelet regression
Combinatorial Chemistry & High Throughput Screening
Title: Normalization of cDNA Microarray Data Using Wavelet Regressions
Volume: 7 Issue: 8
Author(s): Ju Wang, Jennie Z. Ma and Ming D. Li
Affiliation:
Keywords: cdna microarray, data normalization, wavelet regression
Abstract: 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.
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Cite this article as:
Wang Ju, Ma Z. Jennie and Li D. Ming, Normalization of cDNA Microarray Data Using Wavelet Regressions, Combinatorial Chemistry & High Throughput Screening 2004; 7 (8) . https://dx.doi.org/10.2174/1386207043328274
DOI https://dx.doi.org/10.2174/1386207043328274 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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