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Activity Number: 561 - Fine-Scale Inference from Aggregate-Level Genomic Data
Type: Invited
Date/Time: Thursday, August 6, 2020 : 3:00 PM to 4:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #309500
Title: CARseq: Cell Type Aware Analysis of RNA-Seq Data
Author(s): Wei Sun*
Companies: Fred Hutchinson Cancer Research Center

RNA-seq data are often collected from bulk tissue samples that are composed of multiple cell types. Studies of cell type composition have recently attracted increasing research interest and led to new method development for cell type composition estimation using gene expression data. Given such cell type composition estimates, we propose two avenues for cell type-aware analysis of gene expression data. One is to assess the association between cell type composition and clinical outcomes or individual characteristics (e.g., genetic variants). We have developed an approach to evaluate this association using the composition of all the cell types, thus aggregating association signals across cell types. The other direction is to conduce cell type-specific differential expression analysis or cell type-specific eQTL mapping, and for the latter, we combine both total expression and allele-specific expression.

Authors who are presenting talks have a * after their name.

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