SysGen/CBRI Seminar – Jean Yang – 21st October, 2016

Integrated single cell data analysis reveals cell-specific coactivation networks

Prof Jean Yang
University of Sydney

Friday 21st October
ESJ King Theatre, Medical Building, The University of Melbourne

Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behaviour of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression. In this talk, I will describe a versatile modelling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons.

Professor Jean Yang is an applied statistician with expertise in statistical bioinformatics. She was awarded the 2015 Moran Medal in statistics from the Australian Academy of Science in recognition of her work on developing methods for molecular data arising in cutting edge biomedical research. Her research stands at the interface between medicine and methodology development and has centred on the development of methods and the application of statistics to problems in -omics and biomedical research. In particular, her focus is on developing methods for integrating omics and clinical data to answer a variety of scientific questions. As a statistician who works in the bioinformatics area, she enjoys research in a collaborative environment, working closely with scientific investigators from diverse backgrounds.


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