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Optimized LOWESS normalization parameter selection for DNA microarray data.

John A Berger1, Sampsa Hautaniemi, Anna-Kaarina Järvinen

  • 1Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA 93106-9560, USA. berger@ece.ucsb.edu

BMC Bioinformatics
|December 14, 2004
PubMed
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Optimizing parameters for locally weighted scatterplot smoothing (LOWESS) normalization in microarray data is crucial. This study presents a novel optimization approach to systematically select LOWESS parameters, improving data reliability and reproducibility.

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Microarray data normalization is essential for reliable analysis.
  • Locally weighted scatterplot smoothing (LOWESS) is a common normalization technique.
  • Arbitrary LOWESS parameter selection can lead to suboptimal results.

Purpose of the Study:

  • To explore and optimize parameter selection for LOWESS normalization.
  • To address the overlooked concern of parameter choice in LOWESS.
  • To enhance the efficiency and accuracy of microarray data normalization.

Main Methods:

  • Developed an optimization approach for LOWESS parameter selection.
  • Utilized a cost function minimizing mean-squared difference for bandwidth determination.

Related Experiment Videos

  • Applied the method to self-self hybridizations and breast cancer microarray datasets.

    • Main Results:

    • Demonstrated the utility of systematic parameter selection on two distinct datasets.
    • Showcased that different LOWESS parameter choices significantly impact normalization results.
    • Validated the optimization approach on both simulated and real-world data.

    Conclusions:

    • The proposed optimization approach provides a plausible solution for estimating LOWESS parameters.
    • The optimization procedure is applicable to real-life microarray data normalization.
    • Systematic parameter selection improves data preprocessing and study reproducibility.