ArticleGenome biology2023
Performance of computational algorithms to deconvolve heterogeneous bulk ovarian tumor tissue depends on experimental factors.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 43 citations in OpenAlex.
- Discovering reference-missing cell types in bulk transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
- Targeting the HMGB1-IL32 pathway to alleviate T cell exhaustion in epithelial ovarian cancer.GeroScience · 2026Article
- CMDdemux: an efficient single cell demultiplexing method.Nucleic acids research · 2026Article
- Transcriptomic subtypes in high-grade serous ovarian cancer are driven by tumor cellular composition.bioRxiv : the preprint server for biology · 2026Article
- Integrating single-cell and single-nucleus datasets improves bulk RNA-seq deconvolution.Cell reports methods · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data.Genome biology · 2026Article
- Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.Genome biology · 2026Article
- Deconvolved tumor adipocyte proportions and high grade serous ovarian carcinoma survival.bioRxiv : the preprint server for biology · 2026Article
- A robust workflow to benchmark deconvolution of multi-omic data.Genome biology · 2025Article
- The applications of single-cell multiomics in drug screening.Pharmaceutical science advances · 2025Review
- NADCell death & disease · 2025Article
- scRNA-seq Can Identify Different Cell Populations in Ovarian Cancer Bulk RNA-seq Experiments.International journal of molecular sciences · 2025Article
- Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles.Scientific reports · 2025Article
- Can AI reveal the next generation of high-impact bone genomics targets?Bone reports · 2025Review
- lute: estimating the cell composition of heterogeneous tissue with varying cell sizes using gene expression.BMC genomics · 2025Article
- Identification of a deubiquitinating gene-related signature in ovarian cancer using integrated transcriptomic analysis and machine learning framework.Discover oncology · 2025Article
- Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex.Genome biology · 2025Article
- Missing cell types in single-cell references impact deconvolution of bulk data but are detectable.Genome biology · 2025Article
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundSingle-cell gene expression profiling provides unique opportunities to understand tumor heterogeneity and the tumor microenvironment. Because of cost and feasibility, profiling bulk tumors remains the primary population-scale analytical strategy. Many algorithms can deconvolve these tumors using single-cell profiles to infer their composition. While experimental choices do not change the true underlying composition of the tumor, they can affect the measurements produced by the assay.
resultsWe generated a dataset of high-grade serous ovarian tumors with paired expression profiles from using multiple strategies to examine the extent to which experimental factors impact the results of downstream tumor deconvolution methods. We find that pooling samples for single-cell sequencing and subsequent demultiplexing has a minimal effect. We identify dissociation-induced differences that affect cell composition, leading to changes that may compromise the assumptions underlying some deconvolution algorithms. We also observe differences across mRNA enrichment methods that introduce additional discrepancies between the two data types. We also find that experimental factors change cell composition estimates and that the impact differs by method.
conclusionsPrevious benchmarks of deconvolution methods have largely ignored experimental factors. We find that methods vary in their robustness to experimental factors. We provide recommendations for methods developers seeking to produce the next generation of deconvolution approaches and for scientists designing experiments using deconvolution to study tumor heterogeneity.
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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.