ArticleNature communications2019
Accurate estimation of cell-type composition from gene expression data.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers, 1 of them a synthesis that pooled it.
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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
167 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Tumour microenvironment characterisation to stratify patients for hyperthermic intraperitoneal chemotherapy in high-grade serous ovarian cancer (OVHIPEC-1).British journal of cancer · 2024Trial
- CausalCellInfer: Prioritizing Cell-Type-Specific Disease Associations From Genetically Predicted Transcriptomes in Biobank Cohorts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Article
- Integrated inference of cellular compositions and gene expression programs by deconvolution.Cell regeneration (London, England) · 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
- Paired mutation calling and spatial transcriptomics identify cellular neighborhoods associated with the neoplastic outcome of mouse colitis.Nature genetics · 2026Article
- cellGeometry: ultra-fast single-cell deconvolution of bulk RNA-Seq using a geometric solution.Nature communications · 2026Article
- BLASE: bulk linkage analysis for single cell experiments - teasing out the secrets of bulk transcriptomics with trajectory analysis.BMC methods · 2026Article
- Pre-existing antibodies predict protection while mucosal inflammation correlates with symptomaticmedRxiv : the preprint server for health sciences · 2026Article
- A data-driven framework reconstructs the molecular continuum of human MASLD progression.Nature metabolism · 2026Article
- Placental molecular subtypes of severe preeclampsia reveal divergent aging trajectories and fetal growth outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Deep learning for predicting patient drug response by transferring gene-level and cell-level knowledge to tumors.NPJ precision oncology · 2026Article
- Pan-cancer analysis of spatial transcriptomics reveals heterogeneous tumor spatial microenvironment.Cell reports. Medicine · 2026Article
- DADA-EV: domain-adaptive diffusion autoencoder for estimating tissue- and cell-type-specific origin in extracellular vesicle transcriptomes.Briefings in bioinformatics · 2026Article
- A Synthetic Lethality-Informed Multi-Omic Framework for Identifying a Five-Gene Diagnostic Signature in Chronic Obstructive Pulmonary Disease.Current issues in molecular biology · 2026Article
- CSsingle: a unified tool for robust decomposition of bulk and spatial transcriptomic data across diverse single-cell references.Nucleic acids research · 2026Article
- Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2-fusions-driven transcriptional plasticity and therapeutic vulnerabilities.Cell reports. Medicine · 2026Article
107 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly, our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations.
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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.