SMS scnews item created by Shila Ghazanfar at Mon 2 Apr 2018 1057
Type: Seminar
Distribution: World
Expiry: 5 Apr 2018
Calendar1: 9 Apr 2018 1300-1500
CalLoc1: CPC Level 3 Large Meeting Room
Auth: sheilag@psheilag2.pc (assumed)

# Statistical Bioinformatics Seminar: Ghazanfar -- Investigating combinatorial expression of delta-protocadherins in single olfactory sensory neurons

The aim of the statistical bioinformatics seminar is to provide a forum for people
working within the broad area of computation and statistics and their application to
various aspects of biology to present their work and showcase their ongoing projects.
It is intended to foster the exchange of ideas and build potential collaborations across
multiple disciplines.

The seminars will be held at 1:00 pm on Mondays at the Charles Perkins Centre, Seminar
Room (Level 3, large meeting room).  The format of the talk is 30~45 minutes plus
questions.

’Meet and Greet’ and afternoon tea with the speaker This year, we will have a chance for
further discussion between audience members and the seminar speakers in the following
hour of the seminar (2:00 pm), with some light afternoon tea.

Monday April 9, 2018 1:00 PM Seminar 2:00 PM Meet and Greet with the speaker Level 3
Large Meeting Room Charles Perkins Centre

Speaker: Shila Ghazanfar (The University of Sydney)

Title: Investigating combinatorial expression of delta-protocadherins in single
olfactory sensory neurons

Abstract: Single cell RNA-Sequencing (scRNA-Seq) has enabled unprecedented insight into
the behaviour of individual cells on the scale of the entire transcriptome.  Such
precision offers an opportunity to explore cell-specific heterogeneity, however two
distinct features arise from such data: (1) hyperinflation of identically zero counts
for the majority of genes for any given cell, and (2) an apparent bimodal distribution
of non-zero counts.  Both features are unique to scRNA-Seq, and warrant further
development of statistical tools in order to answer biological questions of interest.

We propose a mixture modelling framework to classify cells into three transcriptional
states for each gene: (1) no, (2) low, and (3) high gene expression.  This approach has
the potential to reveal the cell-specific dynamics of RNA transcription (bursting) and
degradation, as well as acting as a cross-dataset standardisation.  We utilised a number
of publicly available scRNA-Seq datasets, stemming from mouse neuronal cell populations,
to perform the mixture model comparison, assess highly and lowly variable genes, and to
estimate cell networks via a uniqueness thresholding.

This work is in the context of understanding how olfactory sensory neurons (OSNs)
interact with each other during embryonic development of the mouse olfaction system.  In
particular, we study the role that combinatorial expression of genes in the delta
distinct guiding principles to build a Monte Carlo simulation of this cell-cell adhesion
behaviour, and assess it’s suitability.  This addresses the larger question of how
combinatorial gene expression specifies specific cell types and tissues.

About the speaker: Shila Ghazanfar has recently completed her PhD in Statistical
Bioinformatics at The University of Sydney and is currently a Research Associate in the
Judith and David Coffey Lifelab at the Charles Perkins Centre and School of Mathematics
and Statistics.  Her research interests are in statistical analysis of data arising from
high throughput sequencing technologies such as RNA-Seq in various research contexts.


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