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The effect of normalization on microarray data analysis.

Yuanyuan Ding1, Dawn Wilkins

  • 1University of Mississippi, Computer and Information Science Department, University, Mississippi 38677, USA.

DNA and Cell Biology
|December 9, 2004
PubMed
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Background subtraction is crucial for microarray data quality. While most normalization methods yield similar results, the chosen method impacts gene selection outcomes.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray analysis requires robust data preprocessing.
  • Normalization and background subtraction are key steps.
  • Understanding their impact on downstream analysis is essential.

Purpose of the Study:

  • To evaluate the impact of background subtraction on microarray data.
  • To compare the effects of various normalization methods.
  • To assess how preprocessing choices influence gene selection.

Main Methods:

  • Statistical tests (t-test, Wilcoxon signed rank, sign test) were used.
  • Comparisons were made between background-subtracted and non-subtracted data.
  • Different normalization techniques were applied and compared.

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Main Results:

  • Background subtraction significantly affects data, particularly for low-quality datasets.
  • Most normalization methods produce comparable normalized data.
  • Gene selection results differ based on the normalization method used.

Conclusions:

  • Background subtraction is a critical preprocessing step.
  • While normalization methods may yield similar data, their impact on gene selection is notable.
  • Careful consideration of normalization is necessary for reliable gene selection in microarray studies.