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Activity Number: 318 - Advances on the Analysis of Single-Cell Sequencing Data
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract #330973
Title: Multi-Sample Differential Expression Analysis of RNA-Seq Single-Cell Data
Author(s): George Tseng* and Li Zhu and An-Shun Tai and Wei Chen
Companies: University of Pittsburgh and University of Pittsburgh and National Tsing Hua University and University of Pittsburgh

Single-cell RNA sequencing provides transcriptomic profiles at unprecedented high-resolution but with a cost of high technical noise. With the rapid technology development, it is now economically practical to sequence single cells from multiple subjects with different biological conditions to better reveal the heterogeneity at the cell level. Existing tools aiming to identify differentially expressed genes often fail to consider technical variation and subject variation, leading to inaccurate findings. We propose a generalization of zero-inflated negative binomial model which corrects both confounding variations. Applying our model to two real data sets and a synthesized data showed improved performance than existing methods. ZINBsubj is available at gihub

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

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