Optimized LOWESS normalization parameter selection for DNA microarray data

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. Results and discussion: In this work, we discuss how to choose parameters for the LOWESS method. Moreover, we present an optimization approach for obtaining the fraction of data points utilized in the local regression and analyze results for local print-tip normalization. The optimization procedure determines the bandwidth parameter for the local regression by minimizing a cost function that r...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: John A. Berger, Sampsa Hautaniemi, Anna-Kaarina Järvinen, Henrik Edgren, Sanjit K. Mitra, Jaakko Astola
• Keywords: BMCBI 2004, Commonly Utilized Normalization, Microarray Data Normalization, Parameters