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Activity Number: 535 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #329086
Title: TWO-SIGMA: a Two-Component Generalized Linear Mixed Model for ScRNA-Seq Association Analysis
Author(s): Eric Van Buren* and Yun Li and Ming Hu and Di Wu
Companies: UNC Chapel Hill and University of North Carolina at Chapel Hill and Cleveland Clinic Foundation and UNC Chapel HIll
Keywords: single-cell RNA sequencing; mixed models; zero-inflation; negative binomial
Abstract:

Two key challenges in any analysis of single cell RNA-Seq (scRNA-Seq) data are excess zeros due to "drop-out" events and substantial overdispersion due to stochastic and systematic differences. Association analysis of scRNA-Seq data is further confronted with the possible dependency introduced by measuring multiple single cells from the same sample. Here, we propose TWO-SIGMA, a new TWO-component SInGle cell Model-based Association analysis method. The first component models the drop-out probability with a mixed effects logistic regression model, and the second component models the (conditional) mean read count with a log-linear negative binomial mixed effects regression model. Our approach is novel in that it simultaneously allows for overdispersion, accommodates dependency in both drop-out probability and mean mRNA abundance at the single-cell level, leads to improved statistical efficiency, and provides highly interpretable coefficient estimates. Simulation studies show advantages in terms of power gain and type-I error control over possible alternative approaches.


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

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