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

Alexander Dietrich1, Lorenzo Merotto2, Konstantin Pelz1

  • 1Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany.

Genome Biology
|January 25, 2026
PubMed
Summary
This summary is machine-generated.

Second-generation cell-type deconvolution tools offer flexibility but vary in performance. This study benchmarks these methods, revealing key factors influencing accuracy and robustness for better application.

Keywords:
Bulk RNA-seqCell-type deconvolutionMethod benchmarkSingle-cell RNA-seqTranscriptomicsUnified method accessValidation datasets

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • In silico cell-type deconvolution from bulk transcriptomics is crucial for understanding tissue composition.
  • Second-generation tools leverage single-cell RNA sequencing for custom signatures, enabling broader applications.
  • Assessing the performance of these flexible deconvolution methods presents significant challenges.

Purpose of the Study:

  • To comprehensively benchmark second-generation cell-type deconvolution tools.
  • To identify sources of variation and bias affecting deconvolution performance.
  • To provide a framework for evaluating and optimizing deconvolution methods.

Main Methods:

  • Benchmarking using a diverse panel of real and simulated transcriptomics data.
  • Disentangling sources of variation including cell-type similarity, reference composition, and dataset origin.
  • Comparative analysis of accuracy, scalability, and robustness across different deconvolution tools.

Main Results:

  • Substantial differences in accuracy, scalability, and robustness were observed among second-generation deconvolution tools.
  • Performance variations are influenced by cell-type similarity, reference data characteristics, and the origin of the bulk dataset.
  • Specific factors significantly impact the reliability of cell-type deconvolution outcomes.

Conclusions:

  • State-of-the-art deconvolution tools exhibit distinct strengths and limitations.
  • Understanding data characteristics and confounders is essential for accurate deconvolution.
  • The omnideconv ecosystem simplifies deconvolution tool application, benchmarking, and optimization for the scientific community.