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Related Experiment Videos

Effect of data normalization on fuzzy clustering of DNA microarray data.

Seo Young Kim1, Jae Won Lee, Jong Sung Bae

  • 1Research Institute for Basic Science, Chonnam National University, Gwangju, 500-757, Korea. gong@chonnam.ac.kr

BMC Bioinformatics
|March 15, 2006
PubMed
Summary

Fuzzy C-Means (FCM) clustering with Lowess normalization improves gene expression analysis, especially for overlapping or noisy microarray data, outperforming hard clustering methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray technology enables high-throughput gene expression profiling.
  • Analyzing gene expression data requires sophisticated data mining techniques like clustering.
  • Hard clustering methods struggle with overlapping gene expression patterns common in microarray data.

Purpose of the Study:

  • To evaluate the effectiveness of Fuzzy C-Means (FCM) clustering for microarray data analysis.
  • To assess the impact of different data normalization methods on clustering results.
  • To compare FCM with hard clustering methods on noisy and overlapping datasets.

Main Methods:

  • Applied Fuzzy C-Means (FCM) clustering to normalize microarray and simulated datasets.
  • Utilized three normalization methods: scale and location transformations, and Lowess normalization.

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  • Determined optimal FCM parameters, including the fuzzification parameter, in relation to normalization techniques.

    • Main Results:

    • Clustering outcomes are dependent on the normalization method and data noisiness.
    • The optimal fuzzification parameter for FCM is sensitive to the normalization method used, particularly with high sample variation.
    • Lowess normalization demonstrated greater robustness for gene clustering compared to scale and location adjustments.

    Conclusions:

    • Fuzzy C-Means (FCM) clustering offers advantages over hard clustering for identifying co-regulated genes with overlapping expression patterns.
    • Lowess normalization is recommended for robust gene clustering in microarray analysis, especially with noisy or variable samples.
    • FCM provides a practical approach for discovering gene subsets strongly associated with specific clusters.