ArticleGenome biology2021
A benchmark for RNA-seq deconvolution analysis under dynamic testing environments.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
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Who cites it
96 citing papers in PubMed.
- Benchmarking cell-type deconvolution in cross-platform transcriptomic data.Genome biology · 2026Article
- Same-Slide Spatial Multiomics Integration with IN-DEPTH Reveals Tumor Virus-Linked Spatial Reorganization of the Tumor Microenvironment.Cancer discovery · 2026Article
- PRISM: Prior-enhanced Inference for Spatial Transcriptomic Cell Type Mapping.Bioinformatics (Oxford, England) · 2026Article
- Circulating extracellular microRNAs as tissue-specific biomarkers of human health and disease.Nature communications · 2026Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
- Integration of Bulk and Single-Cell RNA Sequencing Analyses in Biomedicine.International journal of molecular sciences · 2026Review
- A Prognostic Signature for Lung Adenocarcinoma in Patients Who Have Never Smoked.Cancer discovery · 2026Article
- Evaluating reference-mixture matching in cell-type deconvolution with single-cell RNA-seq references.Briefings in bioinformatics · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Construction of a Multitissue Cell Atlas Reveals Cell-Type-Specific Regulation of Molecular and Complex Phenotypes in Pigs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data.Genome biology · 2026Article
- A review on in-silico analysis of immune cell trafficking and interactions with the tumour microenvironment.Frontiers in oncology · 2026Review
- Integrative multi-omics and radiomics reveal a TMSB10-driven cell state for non-invasive assessment and precision stratification in breast cancer.Frontiers in immunology · 2026Article
- A robust workflow to benchmark deconvolution of multi-omic data.Genome biology · 2025Article
- Deep learning-based cell type profiles reveal signatures of Alzheimer's disease resilience and resistance.Brain : a journal of neurology · 2025Article
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- A prognostic signature for lung adenocarcinoma in people who have never smoked.bioRxiv : the preprint server for biology · 2025Article
- Impact of Cattle Breed in scRNA-Seq Reference on Muscle Fiber Type Deconvolution from Bulk RNA-Seq: A Comparison of Software Tools.Biotech (Basel (Switzerland)) · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- ARTdeConv: adaptive regularized tri-factor non-negative matrix factorization for cell type deconvolution.NAR genomics and bioinformatics · 2025Article
36 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
backgroundDeconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions.
resultsTo systematically reveal the pitfalls and challenges of deconvolution analyses, we investigate the impact of several technical and biological factors including simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks. These frameworks cover comparative analysis of 11 popular deconvolution methods under 1766 conditions.
conclusionsWe provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.
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