SCDC: A Statistical Approach for Reducing Nuisance Variability Due to Oscillating Genes in Unsynchronized Single-Cell RNA-Seq Experiments

• Activity Number: 491
• Type: Contributed
• Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Jeea Choi* and Christina Kendziorski and Ning Leng and Li-Fang Chu and Ron Stewart and James Thomson
• Companies: University of Wisconsin - Madison and University of Wisconsin and Thomson Lab at the Morgridge Institute for Research and Thomson Lab at the Morgridge Institute for Research and Thomson Lab at the Morgridge Institute for Research and Morgridge Institute for Research
• Keywords: gene expression ; single cell RNA-seq ; polynomial regression ; oscillatory gene

Abstract

Oscillatory gene expression is fundamental to mammalian development and aberrations are common in disease. Single-cell RNA sequencing (scRNA-seq) provides a new avenue to the study of oscillatory gene expression. However, in many studies, oscillations are not of interest (such as those caused by the cell cycle), and the increased variability imposed by them masks the effects of interest. In bulk RNA-seq, the increase in variability caused by oscillatory genes is mitigated by averaging over thousands of cells. However, in typical unsynchronized scRNA-seq, this variability remains. To address this, we developed SCDC to remove increased variability due to oscillating genes in a snapshot (non time-course) scRNA-seq experiment. Simulation and case studies demonstrate that by reducing increased variability due to oscillations, both the power and accuracy of downstream analysis are increased.