ArticleGenome research2021
Likelihood-based deconvolution of bulk gene expression data using single-cell references.
Article in Genome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- Evaluating reference-mixture matching in cell-type deconvolution with single-cell RNA-seq references.Briefings in bioinformatics · 2026Article
- A guide to transcriptomic deconvolution in cancer.Nature reviews. Cancer · 2026Review
- Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment.Biotech (Basel (Switzerland)) · 2026Review
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue.PLoS computational biology · 2025Article
- A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples.Genome research · 2025Article
- Deep learning based deconvolution methods: A systematic review.Computational and structural biotechnology journal · 2025Review
- Deconvolution from bulk gene expression by leveraging sample-wise and gene-wise similarities and single-cell RNA-Seq data.BMC genomics · 2024Article
- Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.BMC bioinformatics · 2024Article
- Machine learning integrative approaches to advance computational immunology.Genome medicine · 2024Review
- Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges.Nucleic acids research · 2024Review
- Heterogeneity and molecular landscape of melanoma: implications for targeted therapy.Molecular biomedicine · 2024Review
- DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data.Patterns (New York, N.Y.) · 2024Article
- Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single-Cell Reference and Domain Adaptive Matching.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data.Journal of the American Statistical Association · 2024Article
- DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples.bioRxiv : the preprint server for biology · 2023Article
- Loss of Krüppel-like factor 9 deregulates both physiological gene expression and development.Scientific reports · 2023Article
- Applications of single-cell RNA sequencing in drug discovery and development.Nature reviews. Drug discovery · 2023Review
- Deconstructing Olfactory Epithelium Developmental Pathways in Olfactory Neuroblastoma.Cancer research communications · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Direct comparison of bulk gene expression profiles is complicated by distinct cell type mixtures in each sample that obscure whether observed differences are actually caused by changes in the expression levels themselves or are simply a result of differing cell type compositions. Single-cell technology has made it possible to measure gene expression in individual cells, achieving higher resolution at the expense of increased noise. If carefully incorporated, such single-cell data can be used to deconvolve bulk samples to yield accurate estimates of the true cell type proportions, thus enabling one to disentangle the effects of differential expression and cell type mixtures. Here, we propose a generative model and a likelihood-based inference method that uses asymptotic statistical theory and a novel optimization procedure to perform deconvolution of bulk RNA-seq data to produce accurate cell type proportion estimates. We show the effectiveness of our method, called RNA-Sieve, across a diverse array of scenarios involving real data and discuss extensions made uniquely possible by our probabilistic framework, including a demonstration of well-calibrated confidence intervals.
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Registered trials
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.