ReviewNucleic acids research2024
Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.
Review in Nucleic acids research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparative transcriptomic meta-analysis reveals elevated TCL1A expression in human circulating immune cells across chronic pain conditions.British journal of anaesthesia · 2026Pooled it
- Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution.Nature metabolism · 2025Guideline
- Tumor ecosystem and microbiome features associated with efficacy and resistance to avelumab plus chemoradiotherapy in head and neck cancer.Nature cancer · 2026Trial
- Deconformer: efficient cell-type composition profiling of cfRNA across diverse cell types.Briefings in bioinformatics · 2026Article
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Increasing usable reads in RNA-seq protocols.iScience · 2026Article
- WNK4 restrains endometrial cancer progression by reprogramming macrophage polarization toward an M1-like phenotype.Molecular biology reports · 2026Article
- Benchmarking cell-type deconvolution in cross-platform transcriptomic data.Genome biology · 2026Article
- Transforming subcellular spatial transcriptomics: deep learning models for cell segmentation.Nucleic acids research · 2026Review
- Integrated inference of cellular compositions and gene expression programs by deconvolution.Cell regeneration (London, England) · 2026Article
- iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.PLoS computational biology · 2026Article
- Patient-derived resources for decoding and targeting brain metastases ecosystems.EMBO molecular medicine · 2026Review
- cellGeometry: ultra-fast single-cell deconvolution of bulk RNA-Seq using a geometric solution.Nature communications · 2026Article
- Carotid plaque macrophage burden and inflammatory lipid-associated macrophage markers predict secondary major adverse cardiovascular events after endarterectomy.European heart journal · 2026Article
- BLASE: bulk linkage analysis for single cell experiments - teasing out the secrets of bulk transcriptomics with trajectory analysis.BMC methods · 2026Article
- The molecular ICU: a primer on omics, informatics and the future of precision critical care.Critical care (London, England) · 2026Review
- Methylome-wide association study in blood suggests cell type-specific relationships between DNA methylation and lifetime anxiety.Nature communications · 2026Article
- Estimating tumour immune infiltration: methodological convergence across histology and spatial technologies.Briefings in bioinformatics · 2026Review
- Transcriptomic subtypes in high-grade serous ovarian cancer are driven by tumor cellular composition.bioRxiv : the preprint server for biology · 2026Article
- RESCUE: recovery of unattributed expression patterns in spatial transcriptomics.Nature communications · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.