CeDAR: Incorporating Cell Type Hierarchy Improves Cell Type-Specific Differential Analyses in Bulk Omics Data

Authors

Luxiao Chen
Ziyi Li
Hao Wu

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

Comments

Supplementary Materials

Data Availability Statement

PMID: 36855165