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Background <p>In silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance.</p> Results <p>Here, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin.</p> Conclusions <p>Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, <i>omnideconv</i>, simplifying the application, benchmarking, and optimization of deconvolution methods.</p>

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omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月26日

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  • Alexander Dietrich,
  • Lorenzo Merotto,
  • Konstantin Pelz,
  • Bernhard Eder,
  • Constantin Zackl,
  • Katharina Reinisch,
  • Frank Edenhofer,
  • Federico Marini,
  • Gregor Sturm,
  • Markus List,
  • Francesca Finotello

摘要

Background

In silico cell-type deconvolution from bulk transcriptomics data is a powerful technique to gain insights into the cellular composition of complex tissues. While first-generation methods used precomputed expression signatures covering limited cell types and tissues, second-generation tools use single-cell RNA sequencing data to build custom signatures for deconvoluting arbitrary cell types, tissues, and organisms. This flexibility poses significant challenges in assessing their deconvolution performance.

Results

Here, we comprehensively benchmark second-generation tools, disentangling different sources of variation and bias using a diverse panel of real and simulated data. Our results reveal substantial differences in accuracy, scalability, and robustness across methods, depending on factors such as cell-type similarity, reference composition, and dataset origin.

Conclusions

Our study highlights the strengths, limitations, and complementarity of state-of-the-art tools, shedding light on how different data characteristics and confounders impact deconvolution performance. We provide the scientific community with an ecosystem of tools and resources, omnideconv, simplifying the application, benchmarking, and optimization of deconvolution methods.