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Quantitative noise analysis for gene expression microarray experiments.

Y Tu1, G Stolovitzky, U Klein

  • 1IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA. yuhai@us.ibm.com

Proceedings of the National Academy of Sciences of the United States of America
|October 22, 2002
PubMed
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This study quantifies noise in DNA microarray experiments, finding hybridization noise significantly impacts gene expression levels. Understanding this noise improves the accuracy of detecting gene expression changes.

Area of Science:

  • Molecular Biology
  • Genomics
  • Bioinformatics

Background:

  • DNA microarrays are crucial for gene expression analysis.
  • Distinguishing true signals from experimental noise is a key challenge.
  • Oligonucleotide-based microarrays involve complex steps like reverse transcription, cRNA labeling, and hybridization.

Purpose of the Study:

  • To perform a detailed noise analysis of oligonucleotide-based DNA microarray experiments.
  • To differentiate and quantify noise originating from sample preparation and hybridization.
  • To understand the dependence of noise on gene expression levels.

Main Methods:

  • Utilized replicate experiments with bifurcations at different assay steps.
  • Separated noise sources from sample preparation and hybridization processes.

Related Experiment Videos

  • Quantitatively characterized noise strength and its relation to expression levels.

    • Main Results:

    • Sample preparation noise is minimal, indicating accurate amplification.
    • Hybridization noise shows strong dependence on gene expression levels.
    • Hybridization noise exhibits Poisson-like characteristics at high expression and complex behavior at low expression, likely due to cross-hybridization.

    Conclusions:

    • Noise analysis is critical for accurate DNA microarray data interpretation.
    • Hybridization noise is a major contributor to variability, especially at lower expression levels.
    • A novel method for evaluating gene expression fold-change significance based on noise characteristics was proposed.