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Related Experiment Videos

Faster cyclic loess: normalizing RNA arrays via linear models.

Karla V Ballman1, Diane E Grill, Ann L Oberg

  • 1Division of Biostatistics, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. ballman@mayo.edu

Bioinformatics (Oxford, England)
|May 29, 2004
PubMed
Summary

A new normalization technique, fastlo, provides results comparable to cyclic loess and quantile normalization. This fast, model-based method offers improved versatility for data analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Normalization

Background:

  • Normalization is crucial for accurate high-throughput data analysis.
  • Existing methods like cyclic loess and quantile normalization have limitations in speed or versatility.

Purpose of the Study:

  • To develop a novel normalization technique, fastlo.
  • To achieve normalization results similar to cyclic loess.
  • To match the speed of quantile normalization.

Main Methods:

  • Development of the fastlo normalization algorithm.
  • Model-based approach for normalization.
  • Implementation in Splus/R.

Main Results:

  • Fastlo achieves normalization accuracy comparable to cyclic loess and quantile normalization.

Related Experiment Videos

  • Fastlo demonstrates significant speed improvements, being at least an order of magnitude faster than cyclic loess.
  • Fastlo offers greater versatility due to its model-based nature.

    • Conclusions:

    • Fastlo is an efficient and versatile normalization method.
    • The Splus/R function for fastlo is available from the authors.
    • Fastlo presents a valuable alternative for researchers requiring rapid and adaptable data normalization.