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Activity Number: 127 - Statistical Methods for Multi-Omic Data Analysis
Type: Topic-Contributed
Date/Time: Monday, August 9, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #317373
Title: Individual-Level Differential Expression Analysis for Single Cell RNA-Seq Data
Author(s): Si Liu* and Mengqi Zhang and Zhen Miao and Fang Han and Raphael Gottardo and Wei Sun
Companies: Fred Hutchinson Cancer Research Center and University of Pennsylvania and University of Washington and University of Washington and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center

Many methods have been developed for differential expression across cells using single cell RNA-seq data, few methods were proposed individual-level differential expression analysis. A straightforward solution is to add up the read counts across all the relevant cells (e.g., cells of a specific cell type) of each individual, and thus reduce the data to a pseudo-bulk RNA-seq data, with one number per gene and per individual. Then any existing methods for differential expression of bulk RNA-seq data can be applied. However, in scRNA-seq data, what is observed per gene and per individual is the distribution of gene expression across multiple cells. Reducing a distribution to a number certainly loses a good amount of information. Is it possible to directly test differential expression using gene expression distribution? We explored a few approaches, by estimating gene expression distribution using negative-binomial regression or denoised distribution estimates given by deep count autoencoder, quantifying divergence of distributions using Jensen-Shannon divergence or Wasserstein distance, and performing kernel-based association test or Permutational Multivariate Analysis of Variance.

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

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