Student and Faculty Publications
Publication Date
2-28-2023
Journal
Genome Biology
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.
Keywords
Computer Simulation, Data Analysis, Protein Processing, Post-Translational, Cell type-specific differential analysis, Cell type hierarchy, Hierarchical Bayesian model, Microarray data analysis
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Medical Sciences Commons, Oncology Commons
Comments
Supplementary Materials
Data Availability Statement
PMID: 36855165