Single cell genomics

Short talk: Unlocking insights into cellular senescence through single cell transcriptomics of ageing mesenchymal stem cells

Unlocking insights into cellular senescence through single cell transcriptomics of ageing mesenchymal stem cells Atefeh Taherian Fard,Jessica Mar Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, QLD 4072, Australia Abstract Aging is a complex biological process. The heterogeneity of ageing phenotype is driven by the complex and dynamic nature through which several key molecular and cellular traits arise and interact. To understand the heterogeneity associated with aging, and the complications associated with age-related diseases, research into clinical treatments, including Mesenchymal Stem Cell (MSC) therapy, is underway.

Short talk: TriCycle: Transferable Representation and Inference of cell cycle

TriCycle: Transferable Representation and Inference of cell cycle Shijie Zheng,Genevieve Stein-O’Brien,Jared Slosberg,Jonathan Augustin,Loyal Goff,Kasper D. Hansen Johns Hopkins Bloomberg School of Public Health Abstract Background: The cell-cycle has been the subject of substantial interest in the single-cell expression era, both as a biological variable of interest and as a possible confounder for other comparisons of interest. Even though several computational methods using single-cell RNAseq data for inference of cell cycle have been proposed, some can only assign cells to a discretized stage ignoring the continuous nature of cell cycle, while other continuous assignment methods are only applicable to deep sequencing single cell data.

Short talk: treekoR: An automated framework for elucidating hierarchical relationships in high dimensional cytometry data

treekoR: An automated framework for elucidating hierarchical relationships in high dimensional cytometry data Adam Chan,Jean Yang,Ellis Patrick The University of Sydney Abstract High throughput single cell technologies which measure a high number of parameters for up to millions of cells holds the promise to discover novel biological relationships between different patient conditions with effective analytical workflows. Whether identifying cell clusters is done via unsupervised clustering, or through manually gating cell subsets, the proportions of these cell types relative to the whole sample are able to be analysed to understand characteristics driving different patient conditions.

Short talk: scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data

scShapes: A statistical framework for identifying distribution shapes in single-cell RNA-sequencing data Malindrie Dharmaratne University of Queensland Abstract We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modelling gene expression read counts using generalized linear models. We model each gene independently under each treatment condition using the error distributions Poisson, Negative Binomial, Zero-inflated Poisson and Zero-inflated Negative Binomial with log link function and model-based normalization for differences in sequencing depth.