CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data

• Type of Material: Article
• Authors: Luxiao Chen, Emory University; Ziyi Li, The University of MD Anderson Cancer Center; Hao Wu, Emory University
• Language: English
• Date: 2023-12-01
• Publisher: BioMed Central Ltd
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s13059-023-02857-5
• Title of Journal or Parent Work: Genome Biology
• Volume: 24
• Issue: 1
• Start Page: 37
• End Page: 37
• Grant/Funding Information: HW and LC were partially supported by National Institutes of Health R01GM122083 and R01GM141392. HW was also partially supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDB38050100. ZL was partially supported by National Institutes of Health R03CA270725.
• Keywords: Protein Processing, Post-Translational; Computer Simulation; Hierarchical Bayesian model; Microarray data analysis; Cell type-specific differential analysis; Cell type hierarchy; Data Analysis
• Research Categories: Biology, Biostatistics

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.