ArticleGenome biology2026
omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- From stem cells to somites: Revealing genetic and exogenous factors of human embryogenesis.Stem cell reports · 2026Article
- Benchmarking cell-type deconvolution in cross-platform transcriptomic data.Genome biology · 2026Article
- Longitudinal transcriptomic analysis of mucosa in ulcerative colitis after anti-tumor necrosis factor withdrawal compared to continued treatment.Journal of Crohn's & colitis · 2026Article
- Cross-cohort projection of clinically anchored latent risk enables multi-omics interpretation without refitting.Briefings in bioinformatics · 2026Article
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
- Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment.Cell reports. Medicine · 2026Article
- Integrating single-cell and single-nucleus datasets improves bulk RNA-seq deconvolution.Cell reports methods · 2026Article
- Local Tumor Microenvironment Niches Correlate With Survival And Immunotherapy Response In Human Glioblastoma.bioRxiv : the preprint server for biology · 2026Article
- A Human Kidney Tubuloid Model of Repeated Cisplatin-Induced Cellular Senescence and Fibrosis for Drug Screening.Advanced healthcare materials · 2026Article
- Cellular deconvolution of the brain with topological magnetic resonance image analysis.bioRxiv : the preprint server for biology · 2025Article
- Benchmarking porcine pancreatic ductal organoids for drug screening applications.EMBO molecular medicine · 2025Article
- Unifying DNA methylation-basedBioinformatics advances · 2025Article
- Visualizing stability: a sensitivity analysis framework for t-SNE embeddings.Frontiers in bioinformatics · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundIn 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.
resultsHere, 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.
conclusionsOur 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.
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