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Activity Number: 661 - Statistical Approaches to High-Dimensional Modeling and Real-World Problems
Type: Contributed
Date/Time: Thursday, August 3, 2017 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract #324887 View Presentation
Title: Models for Dependent Data in Single Cell Gene Expression
Author(s): Andrew McDavid*
Companies: University of Rochester Medical Center - Rochester, NY
Keywords: RNAseq ; dependent data ; single cell ; hurdle model ; hierarchical model ; shrinkage
Abstract:

Gene expression profiling of single cells (scRNAseq) has refined and defined new cell types and states. Initial experiments generated many cellular replicates, but focus is increasingly turning towards population-based studies that rely on nested designs in which a cohort of individuals is repeatedly measured by sampling their cells. It has also been observed that even putatively independent designs will generate dependent data when batch effects are present.

I apply a previously-described two-part, zero-inflated log-Normal random effects model for these dependent data. The variance parameters may be weakly identified in a given transcript, but related across the various transcripts measured. To leverage this property, I propose a hierarchical model that adaptively shrinks the variance parameters towards a global value. This model is shown to be applicable also to traditional (bulk) RNAseq experiments that produce dependent data by replacing the observed likelihood with a zero-inflated negative binomial distribution.


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

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