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Tissue-specific RMA models to incrementally normalize Affymetrix GeneChip data.

Steven A Eschrich1, Andrew M Hoerter, Gregory C Bloom

  • 1Department of Biomedical Informatics, H Lee Moffitt Cancer Center&Research Institute, Tampa, FL 33612, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 24, 2009
PubMed
Summary
This summary is machine-generated.

This study shows that incremental normalization using the Robust Multi-array Average (RMA) method converges to a stable model for homogenous samples. This finding supports maintaining large gene expression data warehouses without constant renormalization.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression classifiers are vital for predicting disease diagnosis, prognosis, and therapy response.
  • Personalized medicine aims to predict patient response from single gene expression microarrays.
  • Microarray normalization is a critical step, with Affymetrix GeneChip arrays often requiring model-based algorithms using all data.

Purpose of the Study:

  • To investigate the incremental application of the Robust Multi-array Average (RMA) normalization procedure.
  • To assess the convergence of tissue-specific RMA normalization models with new data.
  • To determine if RMA normalization models stabilize over time with homogenous samples.

Main Methods:

  • Experimenting with the RMA normalization procedure in an incremental manner, adding new chips to existing models.
  • Developing tissue-specific normalization models with minimal differences from batch normalization.
  • Analyzing several large datasets of patient samples to evaluate model convergence.

Main Results:

  • RMA normalization models demonstrate convergence towards a common model when applied to homogenous samples.
  • Incremental updates to RMA normalization models result in stable, predictable outcomes.
  • The study generated datasets with subtle variations from standard batch normalization.

Conclusions:

  • Incremental RMA normalization is effective for homogenous biological samples.
  • This approach allows for the maintenance of large gene expression data warehouses without continuous renormalization.
  • The findings support the feasibility of building and updating comprehensive microarray sample repositories.