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Related Experiment Videos

Variance-Preserving Estimation of Intensity Values Obtained From Omics Experiments.

Adèle H Ribeiro1, Julia Maria Pavan Soler2, Roberto Hirata1

  • 1Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.

Frontiers in Genetics
|October 17, 2019
PubMed
Summary

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Robust omics analysis methods are crucial for reliable results. This study introduces a novel spot quantification and parameter selection approach for microarray data, enhancing reproducibility and identifying key differentially expressed genes in cancer pathways.

Area of Science:

  • Bioinformatics
  • Genomics
  • Statistical Analysis

Background:

  • Omics studies often lack reliability and reproducibility due to challenges in multi-omics analysis.
  • Conventional methods neglect pixel-level uncertainties in signal intensity estimation, potentially introducing noise and artifacts.
  • Existing normalization techniques like LOWESS may remove relevant biological variation.

Purpose of the Study:

  • To develop a robust spot quantification method for omics data that incorporates pixel-level variability.
  • To propose a parsimonious parameter selection method for normalization that accounts for data characteristics like heteroskedasticity.
  • To improve the reliability and reproducibility of multi-omics analyses, particularly in microarray data.

Main Methods:

  • A novel spot quantification technique considering pixel-level variability in signal intensity estimation.
Keywords:
delta methodoptimal LOWESS normalizationparameter selectionpixel-level uncertaintyspot quantificationtwo-color microarrayvariability preservation

Related Experiment Videos

  • A parameter selection method for normalization, designed to be parsimonious and address data heteroskedasticity.
  • Application of the proposed methods to real intestinal metaplasia microarray data.

    • Main Results:

    • The proposed methods provide a more robust and conservative analysis compared to conventional approaches.
    • Fewer, but more reliable, differentially expressed genes were identified.
    • Variability preservation enabled the discovery of novel differentially expressed genes, including those in cancer pathways.

    Conclusions:

    • The developed methods enhance the reliability and reproducibility of omics data analysis.
    • The approach successfully identified differentially expressed genes involved in cancer pathways and confirmed known molecular markers.
    • This work contributes to overcoming existing challenges in multi-omics analysis for more accurate biological insights.