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

Abstract

AbstractThe 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 the differential expression or methylation status are 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 in 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.