Systematic evaluation for metrics of gene expression variability in single-cell RNA sequencing data

Huiwen Zheng⁰, Atefeh Taherian Fard⁰, Xiao Dong¹, Jessica Mar⁰, Jan Vijg²
(0) Australian Institute for Bioengineering and Nanotechnology, The University of Queensland
(1) Department of Genetics, Albert Einstein College of Medicine
(2) Department of Genetics, Albert Einstein College of Medicine;

Find me on Tues Nov 24th, 1:40-3pm AEDT in Remo, table 105

Abstract
During ageing, transcriptional noise has been shown to increase in multiple organs and tissues. Transcriptional noise is defined as the variability of gene expression, and this property reflects the heterogeneity that results from stochastic cell to cell variation. Although the concept of transcriptional noise is not new, different metrics are being used to measure this and it is unclear what the optimal approach is. With the advent of single cell sequencing techniques, it is now becoming possible to quantify how noise is distributed through the genome. The project focuses on understanding how to accurately model transcriptional noise as a regulatory property of the genome and its contribution to the fundamental feature of ageing.

To conduct a systematic evaluation, we selected 12 different metrics that commonly used in scRNA-seq studies. Performance of these metrics is tested with simulated and experimentally-derived datasets. We investigated the performance of these metrics against different data structures, stably expressed genes,and other properties. Using a publicly available scRNA-seq datasets with multiple tissues and age groups for mice, we intend to investigate how transcriptional noise changes during ageing and between cell types. Through these analysis, the goal is to understand how transcriptional noise impacts the regulatory processes that underlie ageing.