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Activity Number:
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338
- Novel Bayesian Methods in Genetic and Genomic Studies
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #322120
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Title:
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Incorporating Cell-Type Hierarchy Improves Cell-Type Specific Differential Analyses in Bulk Omics Data
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Author(s):
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Luxiao Chen* and Ziyi Li and Hao Wu
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Companies:
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Emory University and The University of Texas MD Anderson Cancer Center and Emory University
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Keywords:
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Cell type specific differential analysis;
Bayesian hierarchical modeling;
Cell type hierarchy;
Microarray
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Abstract:
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The bulk high-throughput omics data contain signals from a mixture of different cell types. Recent developments in deconvolution methods makes it possible to draw cell type specific inferences from the 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 to account for the cell type hierarchy in cell type specific differential analyses in bulk data. Extensive simulation and real data analyses demonstrate that such an approach significantly improves the accuracy and power in detecting the cell type-specific differential signals compared with existing methods, especially in cell types with low abundance.
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Authors who are presenting talks have a * after their name.
