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Related Experiment Videos

The effects of normalization on the correlation structure of microarray data.

Xing Qiu1, Andrew I Brooks, Lev Klebanov

  • 1Department of Biostatistics and Computational Biology, University of Rochester, New York 14642, USA. Xing_Qiu@urmc.rochester.edu

BMC Bioinformatics
|May 21, 2005
PubMed
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Gene expression data often shows strong correlations between genes. Normalization methods can reduce these correlations, but they do not completely eliminate the underlying structure in microarray datasets.

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Genomics

Background:

  • Stochastic dependence in gene expression is critical for statistical inference in microarray analysis.
  • Assumptions of weak between-gene correlations may impact methods for detecting differentially expressed genes.
  • The impact of between-gene correlations on statistical inference methods remains underexplored.

Purpose of the Study:

  • To systematically investigate the correlation structure of t-statistics between genes in microarray data.
  • To evaluate the effects of different normalization methods on gene-wise correlations.
  • To understand how normalization impacts true and spurious correlations in gene expression data.

Main Methods:

  • Analysis of correlation between t-statistics of different genes.

Related Experiment Videos

  • Utilized a large microarray dataset of childhood leukemia and simulated datasets.
  • Applied and assessed four distinct normalization methods.

    • Main Results:

    • Identified long-range correlations affecting thousands of genes based on t-statistics.
    • Demonstrated that normalization methods significantly reduce correlations between gene t-statistics.
    • Observed that normalization affects both true (gene interactions) and spurious (noise-induced) correlations.

    Conclusions:

    • Normalization effectively reduces, but does not entirely eliminate, correlations between gene t-statistics in real-world microarray data.
    • The inherent long-range correlation structure persists even after normalization.
    • Understanding and addressing gene expression correlations is crucial for accurate statistical inference in genomics.