Optimized LOWESS normalization parameter selection for DNA microarray data |
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| Authors: | John?A?Berger mailto:berger@ece.ucsb.edu" title=" berger@ece.ucsb.edu" itemprop=" email" data-track=" click" data-track-action=" Email author" data-track-label=" " >Email author,Sampsa?Hautaniemi,Anna-Kaarina?J?rvinen,Henrik?Edgren,Sanjit?K?Mitra,Jaakko?Astola |
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| Affiliation: | (1) Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA;(2) Institute of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland;(3) Biomedicum Biochip Center, University of Helsinki, P.O. Box 63, 00014 Helsinki, Finland;(4) Medical Biotechnology Group, VTT Technical Research Center of Finland and University of Turku, P.O. Box 106, 20521 Turku, Finland |
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| Abstract: | ![]()
Background Microarray data normalization is an important step for obtaining data that are reliable and usable for subsequent analysis. One of the most commonly utilized normalization techniques is the locally weighted scatterplot smoothing (LOWESS) algorithm. However, a much overlooked concern with the LOWESS normalization strategy deals with choosing the appropriate parameters. Parameters are usually chosen arbitrarily, which may reduce the efficiency of the normalization and result in non-optimally normalized data. Thus, there is a need to explore LOWESS parameter selection in greater detail. |
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