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

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.

SUBMITTER: Chen L

PROVIDER: S-EPMC9972684 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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

Chen Luxiao L Li Ziyi Z Wu Hao H

Genome biology 20230228 1

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 a ...[more]

PMID: 36855165

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