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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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COT: an efficient and accurate method for detecting marker genes among many subtypes.

Yingzhou Lu1, Chiung-Ting Wu1, Sarah J Parker2

  • 1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.

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A new Cosine-based One-sample Test (COT) accurately identifies marker genes (MGs) for molecular subtypes. This data-driven method precisely matches the ideal MG definition, improving biological characterization.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Marker genes (MGs) are crucial for identifying and classifying molecular subtypes of tissues or cells.
  • Existing methods often fail to precisely identify MGs, leading to inaccurate classifications.
  • There is a need for accurate and efficient methods to detect ideal MGs.

Purpose of the Study:

  • To develop and present an accurate, data-driven method for detecting marker genes (MGs).
  • To introduce the Cosine-based One-sample Test (COT) for identifying molecular subtypes.
  • To provide a computational tool for enhanced molecular characterization.

Main Methods:

  • Formulated a Cosine-based One-sample Test (COT) in scatter space.
  • Utilized subtype expression profiles for marker gene detection.
  • Applied the method to simulated and real gene expression and proteomics data.

Main Results:

  • COT efficiently and accurately detects marker genes (MGs) across multiple subtypes.
  • The test statistic in COT precisely aligns with the mathematical definition of an ideal MG.
  • Demonstrated the utility of COT on diverse biological datasets.

Conclusions:

  • COT offers an efficient and accurate approach for marker gene detection.
  • The developed Python/R tool facilitates unbiased molecular characterization of subtypes.
  • COT complements existing methods for a more comprehensive analysis.