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Related Experiment Videos

Inter-gene correlation on oligonucleotide arrays: how much does normalization matter?

David L Gold1, Jing Wang, Kevin R Coombes

  • 1Department of Biostatistics and Applied Mathematics, M.D. Anderson Cancer Center, The University of Texas, Houston, Texas 77030-4009, USA.

American Journal of Pharmacogenomics : Genomics-Related Research in Drug Development and Clinical Practice
|August 5, 2005
PubMed
Summary
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Normalization methods significantly impact gene co-expression analysis in microarrays. Different normalization techniques yield varying results, highlighting the need for careful selection and interpretation in gene dependency studies.

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Normalization is crucial for microarray data analysis, aiming to reduce technical variations.
  • Despite widespread use, normalization methods remain controversial, with unclear effects on gene co-expression.
  • Standards for comparing normalization methods and understanding their impact are lacking.

Purpose of the Study:

  • To evaluate the effect of different normalization methods on gene-to-gene co-expression in microarray data.
  • To investigate the sensitivity of array results to normalization choices.
  • To explore trends in gene correlation using a novel graphical method.

Main Methods:

  • Applied 1-, 2-, and N-quantile normalization to public microarray datasets.

Related Experiment Videos

  • Utilized datasets quantified with MAS 5.0 and dCHIP software.
  • Introduced a graphical approach for analyzing gene correlation trends.

    • Main Results:

    • Normalization method significantly altered gene dependency distributions.
    • Increasing quantiles reduced intensity-correlated trends in MAS 5.0, but not dCHIP.
    • N-quantile normalization did not substantially improve correlation for known targets in MAS 5.0.

    Conclusions:

    • Normalization critically influences inter-gene dependency estimations in microarrays.
    • Caution is advised when interpreting gene-wise dependencies from microarray data due to normalization variability.
    • Further research is needed to understand normalization's impact on gene dependency.