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Normalization and Technical Variation in Gene Expression Measurements.

Walter Liggett1

  • 1National Institute of Standards and Technology, Gaithersburg, MD 20899-8980.

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|June 9, 2016
PubMed
Summary

This study introduces novel methods for gene expression normalization and technical variation analysis in microarrays. These techniques improve data accuracy and identify inherent limitations in the normalization process.

Keywords:
MAQCcalibration linearityclassifier performancehigh-density microarraysinter-laboratory studylatent variablesmicroarray quality controlsimultaneous F testssources of variationstatistical dependence

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Gene expression measurements are crucial for biological research.
  • Technical variation can significantly impact the accuracy of these measurements.
  • Normalization methods aim to reduce technical variation in microarray data.

Purpose of the Study:

  • To develop and demonstrate improved data-analysis methods for gene expression normalization.
  • To characterize the limitations of normalization in reducing technical variation.
  • To assess the linearity of probe responses in microarray experiments.

Main Methods:

  • Developed an improved normalization method for multiple assays using a parametric model.
  • Created a method to characterize the effectiveness of normalization in reducing technical variation.
  • Applied these methods to data from the Microarray Quality Control (MAQC) project using four microarray platforms.

Main Results:

  • The improved normalization method was applied to MAQC project materials.
  • A statistically significant but small lack of linearity in probe responses was detected.
  • A source of technical variation that cannot be eliminated by normalization was identified.

Conclusions:

  • The developed methods provide insights into gene expression normalization and technical variation.
  • Limitations exist in normalization's ability to eliminate all technical variation.
  • Further strategies are needed to address unremovable sources of variation in microarray data.