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<p>Cell-free DNA (cfDNA) is a rich source of biomarkers for various pathophysiological conditions. Preanalytical variables, such as the library preparation protocol or sequencing platform, are major confounders of cfDNA analysis. We present DAGIP, a novel data correction method that builds on optimal transport theory and deep learning, which explicitly corrects for the effect of such preanalytical variables and can infer technical biases. Our method improves cancer detection and copy number alteration analysis by alleviating the sources of variation that are not of biological origin. It also enhances fragmentomic analysis of cfDNA. DAGIP allows the integration of cohorts from different studies.</p>

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DAGIP: alleviating cell-free DNA sequencing biases with optimal transport

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月7日

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  • Antoine Passemiers,
  • Stefania Tuveri,
  • Tatjana Jatsenko,
  • Adriaan Vanderstichele,
  • Pieter Busschaert,
  • An Coosemans,
  • Dirk Timmerman,
  • Sabine Tejpar,
  • Peter Vandenberghe,
  • Diether Lambrechts,
  • Daniele Raimondi,
  • Joris Robert Vermeesch,
  • Yves Moreau

摘要

Cell-free DNA (cfDNA) is a rich source of biomarkers for various pathophysiological conditions. Preanalytical variables, such as the library preparation protocol or sequencing platform, are major confounders of cfDNA analysis. We present DAGIP, a novel data correction method that builds on optimal transport theory and deep learning, which explicitly corrects for the effect of such preanalytical variables and can infer technical biases. Our method improves cancer detection and copy number alteration analysis by alleviating the sources of variation that are not of biological origin. It also enhances fragmentomic analysis of cfDNA. DAGIP allows the integration of cohorts from different studies.