ReviewMolecules and cells2023
A Comprehensive Overview of RNA Deconvolution Methods and Their Application.
Review in Molecules and cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
- Identifying clinically relevant cell state interactions in the tumor microenvironment of IDH-mutant gliomas using CSI-TME.Molecular systems biology · 2026Article
- Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer.Nature communications · 2026Article
- QuTILs: Open-Source Image-Based Infiltrating Immune Cell Detection for Research Application.Research square · 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
- Immune involvement in neuropsychiatric disorders: Insights from single-cell transcriptomic studies.Psychiatry and clinical neurosciences · 2026Review
- Evaluating reference-mixture matching in cell-type deconvolution with single-cell RNA-seq references.Briefings in bioinformatics · 2026Article
- Opportunities for RNA sequencing in physiology: from big data to understanding homeostasis and heterogeneity.Function (Oxford, England) · 2026Review
- Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.Genome biology · 2026Article
- Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment.Biotech (Basel (Switzerland)) · 2026Review
- Transcriptome-informed metabolic modeling reveals astrocyte-specific vulnerabilities in mild cognitive impairment and Alzheimer's disease progression.Frontiers in bioinformatics · 2026Article
- Integrative single-cell analysis reveals immunogenic cell death-associated heterogeneity and identifies a robust prognostic signature in osteosarcoma with experimental validation.Frontiers in pharmacology · 2026Article
- Deciphering the Tumor Microenvironment Composition Using Bulk Transcriptomics: A Guide to Recent Advances and Open Challenges.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Multiomics and Genomic Alteration Characterization Identifies VDAC1 as a Mitochondrial-Associated Biomarker in Pancreatic Cancer.Human mutation · 2026Article
- Cell type inference in cell-free nucleic acid liquid biopsy.Nature biotechnology · 2025Review
- Single-cell RNA sequencing highlights the role of epithelial-immune dual features of proximal tubule cells in BK polyomavirus nephropathy.Journal of virology · 2025Article
- Identification of the body fluid donors from mixture stains using bulk transcriptomes data.Briefings in bioinformatics · 2025Article
- Modular inflammation network discovery from large-scale phenotypic screening in genetically heterogeneous mouse brains.Journal of neuroinflammation · 2025Article
- Deciphering the Heterogeneity of Pancreatic Cancer: DNA Methylation-Based Cell Type Deconvolution Unveils Distinct Subgroups and Immune Landscapes.Epigenomes · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumors are surrounded by a variety of tumor microenvironmental cells. Profiling individual cells within the tumor tissues is crucial to characterize the tumor microenvironment and its therapeutic implications. Since single-cell technologies are still not cost-effective, scientists have developed many statistical deconvolution methods to delineate cellular characteristics from bulk transcriptome data. Here, we present an overview of 20 deconvolution techniques, including cutting-edge techniques recently established. We categorized deconvolution techniques by three primary criteria: characteristics of methodology, use of prior knowledge of cell types and outcome of the methods. We highlighted the advantage of the recent deconvolution tools that are based on probabilistic models. Moreover, we illustrated two scenarios of the common application of deconvolution methods to study tumor microenvironments. This comprehensive review will serve as a guideline for the researchers to select the appropriate method for their application of deconvolution.
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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.