ArticleGenome biology2023
CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- UBD: incorporating uncertainty in cell type proportion estimates from bulk samples to infer cell-type-specific profiles.Briefings in bioinformatics · 2026Article
- scDETECT: a novel statistical model accounting for cell type correlation in single-cell RNA-seq differential expression analysis.Briefings in bioinformatics · 2025Article
- Examining cellular heterogeneity in human DNA methylation studies: Overview and recommendations.STAR protocols · 2025Review
- A systematic evaluation of cell-type-specific differential methylation analysis in bulk tissue.Briefings in bioinformatics · 2025Article
- HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data.Genome biology · 2024Article
- cypress: an R/Bioconductor package for cell-type-specific differential expression analysis power assessment.Bioinformatics (Oxford, England) · 2024Article
- ACCURATE ESTIMATION OF RARE CELL-TYPE FRACTIONS FROM TISSUE OMICS DATA VIA HIERARCHICAL DECONVOLUTION.The annals of applied statistics · 2024Article
- GTM-decon: guided-topic modeling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomes.Genome biology · 2023Article
- CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data.Genome biology · 2023Article
- A comprehensive assessment of cell type-specific differential expression methods in bulk data.Briefings in bioinformatics · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.
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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.