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Systems biology for single cell RNA-Seq data
Single cell RNA-Seq data is challenging to analyse due to problems like dropout and cell type identification. We present a novel clustering
approach that applies mixture models to learn interpretable clusters from RNA-Seq data, and demonstrate how it can be applied to publicly
available scRNA-Seq data from the mouse brain. Having inferred groupings of the cells, we can then attempt to learn networks from the data. These approaches are widely applicable to single cell RNA-Seq datasets where there is a need to identify and characterise sub-populations of cells.
Date:
7 February 2020, 14:00
Venue:
Mathematical Institute, Woodstock Road OX2 6GG
Venue Details:
L3
Speaker:
Dr Tom Thorne (University of Reading)
Organising department:
Mathematical Institute
Organiser:
Sara Jolliffe (University of Oxford)
Organiser contact email address:
sara.jolliffe@maths.ox.ac.uk
Host:
Philip Maini (University of Oxford)
Part of:
Mathematical Biology and Ecology
Booking required?:
Not required
Audience:
Public
Editor:
Sara Jolliffe
