ArticleBMC cancer2019
Estimation of immune cell content in tumor using single-cell RNA-seq reference data.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 47 citations in OpenAlex.
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- Germinal Center-Like Tertiary Lymphoid Structures Mark Immune Responsiveness and Enable Checkpoint Immunotherapy in Bladder Cancer.Oncology research · 2026Article
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- Spatial Profiling Identifies Tumor-Associated Stroma Enrichment and MIF as Potential Immunotherapy Targets in Primary Ewing Sarcomas.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- SmartImpute is a targeted imputation framework for single-cell transcriptome data.Cell reports methods · 2025Article
- Identification of prognostic-related tumor microenvironment genes in lung adenocarcinoma and establishment of a prognostic prediction model.Translational lung cancer research · 2025Article
- Deconer: An Evaluation Toolkit for Reference-based Deconvolution Methods Using Gene Expression Data.Genomics, proteomics & bioinformatics · 2025Article
- Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in osteoarthritis by integrating bioinformatics and machine learning.Frontiers in immunology · 2025Article
- Review
- Age-associated microenvironmental changes highlight the role of PDGF-C in ERNature cancer · 2023Article
- Testing for Phylogenetic Signal in Single-Cell RNA-Seq Data.Journal of computational biology : a journal of computational molecular cell biology · 2023Article
- A subnetwork-based framework for prioritizing and evaluating prognostic gene modules from cancer transcriptome data.iScience · 2023Article
- A comprehensive assessment of cell type-specific differential expression methods in bulk data.Briefings in bioinformatics · 2023Article
- Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer.Cancer research · 2023Article
- Single-cell Characterization of the Cellular Landscape of Acral Melanoma Identifies Novel Targets for Immunotherapy.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022Article
- Single-cell RNA sequencing technologies and applications: A brief overview.Clinical and translational medicine · 2022Review
- Article
- Single-Cell Characterization of the Immune Microenvironment of Melanoma Brain and Leptomeningeal Metastases.Clinical cancer research : an official journal of the American Association for Cancer Research · 2021Article
- Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe rapid development of single-cell RNA sequencing (scRNA-seq) provides unprecedented opportunities to study the tumor ecosystem that involves a heterogeneous mixture of cell types. However, the majority of previous and current studies related to translational and molecular oncology have only focused on the bulk tumor and there is a wealth of gene expression data accumulated with matched clinical outcomes.
resultsIn this paper, we introduce a scheme for characterizing cell compositions from bulk tumor gene expression by integrating signatures learned from scRNA-seq data. We derived the reference expression matrix to each cell type based on cell subpopulations identified in head and neck cancer dataset. Our results suggest that scRNA-Seq-derived reference matrix outperforms the existing gene panel and reference matrix with respect to distinguishing immune cell subtypes.
conclusionsFindings and resources created from this study enable future and secondary analysis of tumor RNA mixtures in head and neck cancer for a more accurate cellular deconvolution, and can facilitate the profiling of the immune infiltration in other solid tumors due to the expression homogeneity observed in immune cells.
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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.