ArticlePLoS computational biology2020
Deconvolution of heterogeneous tumor samples using partial reference signals.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- IECP: iterative equilibration of cell-type expression profiles improves accuracy of reference-free deconvolution.Bioinformatics (Oxford, England) · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- A robust workflow to benchmark deconvolution of multi-omic data.Genome biology · 2025Article
- DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue.PLoS computational biology · 2025Article
- Unveiling the immune microenvironment of complex tissues and tumors in transcriptomics through a deconvolution approach.BMC cancer · 2025Article
- Missing cell types in single-cell references impact deconvolution of bulk data but are detectable.Genome biology · 2025Article
- GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.Briefings in bioinformatics · 2024Article
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data.Patterns (New York, N.Y.) · 2024Article
- DECOMICS, a shiny application for unsupervised cell type deconvolution and biological interpretation of bulk omic data.Bioinformatics advances · 2024Article
- Deconvolution of tumor composition using partially available DNA methylation data.BMC bioinformatics · 2022Article
- A 1D-3D Hybrid Model of Patient-Specific Coronary Hemodynamics.Cardiovascular engineering and technology · 2022Article
- Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review.Biomolecules · 2021Review
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE).
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