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

2026/01/26 by Alexander Dietrich, Lorenzo Merotto, K. Pelz +8 · 1 voice · 6 citations
Biochemistry, Genetics and Molecular Biology · #Benchmarking #Blind deconvolution #Cell Image Analysis Techniques #Complementarity (molecular biology) #Convolution (computer science) #Deconvolution #RNA regulation and disease #Single-cell and spatial transcriptomics #Wiener deconvolution

paper · doi:10.1186/s13059-026-03955-w

published in Genome biology 27(1), 6 (BioMed Central)

openalex created_date 2026/01/26 · openalex publication_date 2026/01/26 · openalex updated_date 2026/08/04

Abstract

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.

Citations

• Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex
• Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris

Cited by

• A Human Kidney Tubuloid Model of Repeated Cisplatin-Induced Cellular Senescence and Fibrosis for Drug Screening. [europepmc]
• Integrating single-cell and single-nucleus datasets improves bulk RNA-seq deconvolution. [europepmc]
• Integration of Bulk and Single-Cell RNA Sequencing Analyses in Biomedicine. [europepmc]
• Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment. [europepmc]
• Local Tumor Microenvironment Niches Correlate With Survival And Immunotherapy Response In Human Glioblastoma [europepmc]

Discussions

• 🖥️🧬🚀 I am excited to share that our #omnideconv study is finally published on Genome Biology: doi.org/10.1186/s130... [bsky, 22 points, 1 comments]

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