Scaffold: Data Generation–Based Simulation Framework for Single-Cell RNA-Seq Data

• Activity Number: 186 - Statistical Methods for Assessing Genomic Heterogeneity
• Type: Topic Contributed
• Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Rhonda Bacher* and Parker Knight and Christina Kendziorski
• Companies: University of Florida and University of Florida and University of Wisconsin-Madison
• Keywords: Single-cell; simulation; RNA-seq; R package

Abstract

Single cell RNA-sequencing (scRNA-seq) is now a widely-used tool that facilitates study of the transcriptome at the resolution of a single cell. Yet, substantial technical variability and biases in the data present challenges in analysis and can obscure biological signals. To study the effect of each experimental step on technical artifacts unique to scRNA-seq, we developed a first principles simulation framework (R/Scaffold). By modeling each step of the experiment mathematically, we find that equalizing the concentration of cDNA libraries prior to pooling, a step not consistently performed in single-cell experiments, improves gene detection rates, enhances biological signals, and reduces technical artifacts in scRNA-seq data. We demonstrate our results on experimental and with analysis of publicly available data.