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Activity Number: 32 - Computational and Statistical Methods for Single-Cell Transcriptomics and Epigenomics Analyses
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #313674
Title: Gene Set Testing Methods for Single Cell RNAseq (ScRNAseq) Data
Author(s): Di Wu* and Hunyong Cho and Eric Van Buren and Chuwen Liu and Yun Li
Companies: University of North Carolina at Chapel Hill and and and University of North Carolina, Chapel Hill and University of North Carolina at Chapel Hill
Keywords: Gene set test; single cell; zero inflation

We have developed three gene set testing methods to analyze single cell RNAseq data. Two-Sigma-G is a competitive test to test whether the gene in a prior defined gene set, e.g., from a pathway or other researchers’ experiments, are more differentially expressed comparing to the randomly selected gene sets. It employs the Two-Sigma framework based on zero-inflated negative binomial distribution and allowing random effects due to the fact that many cells are from one biological sample particularly when there are multiple samples in a sample group. Bi-ZINB was a bivariate zero-inflated negative biological model we developed to allow more flexibility of rationale to generate excess zeros for a pair of genes. We further developed gene set test based on it to understand whether the gene-wise dependence in a pathway are larger than randomly selected gene sets to identify active regulatory pathways from scRNAseq data in a sample. In the third method, we explore the relation between the gene-level variance and gene sets. Simulations have been run for model fitting and the control of type I, and type II error in the tests. Methods are applied in a well-designed HIV related scRNAseq dataset.

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

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