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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
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Independent surrogate variable analysis to deconvolve confounding factors in large-scale microarray profiling

Andrew E Teschendorff1, Joanna Zhuang, Martin Widschwendter

  • 1Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, London WC1E 6BT, UK. a.teschendorff@ucl.ac.uk

Bioinformatics (Oxford, England)
|April 8, 2011
PubMed
Summary

Independent Surrogate Variable Analysis (ISVA) identifies confounding factors in large-scale studies. This method improves feature selection accuracy in DNA methylation and mRNA expression datasets, outperforming existing approaches.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Large-scale omics studies face challenges with confounding factors, impacting statistical significance and feature selection.
  • Surrogate Variable Analysis (SVA) is a framework to address these confounding issues.
  • Existing methods may struggle with low signal-to-noise ratios and complex biological data.

Purpose of the Study:

  • To introduce Independent Surrogate Variable Analysis (ISVA), a modified SVA approach.
  • To identify features correlating with phenotypes despite confounding factors.
  • To enhance the robustness of feature selection in high-dimensional data.

Main Methods:

  • ISVA is based on viewing data as an interference pattern of independent effects and noise.
  • The method was tested using simulated data to assess confounder identification.
  • Performance was evaluated on large-scale Illumina Infinium DNA methylation and mRNA expression datasets.

Main Results:

  • ISVA effectively identifies confounders and outperforms methods that do not adjust for them.
  • The approach improves confounder identifiability in DNA methylation data, addressing beadchip and conversion efficiency issues.
  • ISVA demonstrates robust feature selection, even with model misspecification and heterogeneous phenotypes across multiple datasets.

Conclusions:

  • ISVA offers improved performance in identifying confounders compared to existing methods.
  • The method enhances feature selection robustness in DNA methylation and mRNA expression studies.
  • ISVA is a valuable tool for analyzing large-scale omics data subject to confounding factors.