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.
Original language | English (US) |
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Article number | 37 |
Journal | Genome biology |
Volume | 24 |
Issue number | 1 |
DOIs | |
State | Published - Dec 2023 |
Keywords
- Cell type hierarchy
- Cell type-specific differential analysis
- Hierarchical Bayesian model
- Microarray data analysis
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
MD Anderson CCSG core facilities
- Biostatistics Resource Group