AIBN Virtual Seminar Series: Systematic evaluation for metrics of gene expression variability in single-cell RNA sequencing data

We are pleased to host AIBN PhD Student, Huiwen Zheng from the Mar Group to talk about her PhD work as part of the AIBN Virtual Seminar Series. Please take this opportunity and join us virtually.

Date: Thursday, 24 September

Time: 12pm - 12:40pm

Venue: Online via Zoom

The seminar is free, but registration is essential. To register please click here.

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, housekeeping genes, genes known to be variably expressed, 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

Bio

Huiwen Zheng received her bachelor’s degree in Marine Pharmacy at China Pharmaceutical University (China) and her Master of Bioinformatics degree from the University of Queensland. She is a second-year PhD student under Associate Professor Jessica Mar supervision in Computational Biology and Biostatistics at the Australian Institute for Bioengineering and Nanotechnology (AIBN). Her research is mainly focused on analysing large-scale single-cell transcriptomic data to understand the gene-expression heterogeneity in human ageing.

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