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Webinar: Modelling Molecule Dropout in single cell RNA-seq Experiment Leads to Improved Identification of Marker Genes

  • 18 Nov 2019
  • 12:00 PM - 1:00 PM (AEDT)
  • via Zoom, AEDT

Please join us for a webinar with presenter A/Professor Agus Salim, 
Mathematics and Statistics, La Trobe University and Baker Heart and Diabetes Institute.

About the webinar:
In the last few years, technological advance has enabled RNA sequencing to be carried out at single-cell level.  Single cell RNA-seq (scRNA-seq) data offers opportunities to discover novel cell-types and better understanding of cell heterogeneity and differentiation pathways. On the other hand, there are inherent challenges in scRNA-seq data analyses, many of which are shared with the bulk data, while some are unique to single-cell data. Owing to the small amount of biological material within a cell and imperfect technology, total and partial molecule dropout is a common phenomenon with scRNA-seq data, resulting in sparse dataset with often more than 90% zero counts. In this talk, I am going to discuss DECENT (Differential Expression with Capture Efficiency adjustment), our approach for modelling molecule dropout using beta-binomial model and demonstrate using four real datasets, how correct modelling of dropout lead to better identification of cell type-specific marker genes.

About the presenter
Dr Agus Salim is Associate Professor at the Department of Mathematics and Statistics, La Trobe University. He came to La Trobe in April 2013 having previously held positions at the Australian National University and National University of Singapore. Agus has over 15 years experience as a biostatistician and has extensive records of collaboration with clinicians and epidemiologists and has received competitive funding from the National Health and Medical Research Council for his applied and methodological works. His main methodological interest is in the area of survival analysis with a focus on analysis of nested case-control data and the analysis of high-throughput data from next-generation sequencing.

To register
This is a free event, but you will need to register. Click here to save your place. After registering, you will receive a confirmation email containing information about joining the meeting.

Would you please note that this event will be held at 12:00 PM AEDT? Please check your time zone!

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