SMS scnews item created by Shila Ghazanfar at Mon 20 Nov 2017 0904
Type: Seminar
Distribution: World
Expiry: 21 Nov 2017
Calendar1: 20 Nov 2017 1300-1400
CalLoc1: CPC Seminar Room Level 5
Auth: sheilag@10.17.88.210 (sgha9047) in SMS-WASM

# Statistical Bioinformatics Seminar: Mason -- Modelling transcriptional variability in single cell RNA-seq data during human embryogenesis captures changes in the regulation of critical developmental genes

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.

Monday November 20, 2017 (PLEASE NOTE: Special location - Level 5 Large
Meeting Room, Usual time: 1pm - 2pm)

Speaker: Elizabeth Mason (The University of Melbourne)

Title: Modelling transcriptional variability in single cell RNA-seq data
during human embryogenesis captures changes in the regulation of critical
developmental genes

Abstract: Human development is a temporally and spatially ordered series of
events that occur with remarkable precision; the same DNA blueprint gives
rise to more than 250 sharply defined cell phenotypes. At the functional
phenotype level embryogenesis appears predictable because we observe the
average behaviour of many individual cells, even as the number of cells,
the range of phenotypes and transcriptional complexity increases during
the course of development. When we evaluate single molecules and transcripts
that the stochastic nature of gene expression is revealed, for example in
single cell RNA-seq experiments (scRNA-seq). Current methods reduce scRNA-seq
data to a well-defined trajectory based on the abundance of key regulators of
phenotype, and differential abundance between cells in a given phenotype is
used to identify sub-populations. Here we present an alternative approach:
that measuring the transcriptional variability at the gene level informs the
level of regulation imposed on it, reflecting an intrinsic property of
development that is often overlooked. While linear models have been a
successful framework to characterize differences in abundance between
phenotypes on average, they do not account for stochastic differences
captured by scRNA-seq experiments. Accurately determining abundance and
variability is further complicated by the sparseness of non-zero expression
values. To address these challenges and evaluate gene expression during
human pre-implantation embryogenesis, we applied a statistical mixture
model to scRNA-seq data. Fitting the model on a gene-by-gene basis allowed
us to evaluate shifts in the proportion of cells expressing a given gene (Î»),
and also the mean (Î¼) and standard deviation (Ïƒ) of expression. From here, a
correlation based analysis evaluated whether abundance (Î¼) and variability (Ïƒ)
capture different aspects of transcriptional regulation. While each metric
largely identified the same genes, the number and nature of relationships
between them differed. Indeed, genes sharing correlated patterns of variability
during development were enriched for motifs associated with developmental
transcription factors (e.g. HIC2, PPARG, E2F4 and ZNF692). Variability was
more effective than abundance at specifically detecting regulatory
relationships during development, and with less redundancy. Our approach
provides a gene-centric platform to evaluate population-based parameters
of gene expression, while preserving the complexity of scRNA-seq data.

About the speaker: Lizzi began her career in human genomics as a laboratory
manager and laboratory technician with Professor Greg Gibson (Centre for
Integrative Genomics, Georgia Tech University). She conducted 2 investigations
in Australia which identified maternal influences on development of the neonate
immune system, and uncovered population structure of the leukocyte transcriptome.
Together with scientists at Emory University, Greg and Lizzi initiated the CIGâ€™s
involvement in the WHOLE (Wellness and Health Omics Linked to the Environment)
study of Predictive Health Genomics in Atlanta (USA) which is currently in
its 6th year. Lizzi has recently completed a PhD in systems biology of human
stem cells at the Australian Institute for Bioengineering and Nanotechnology
at the University of Queensland. Her PhD project formed an international
collaboration with Professor Christine Wells (University of Melbourne AUS),
stem cell biologists Professors Martin Pera (Jackson Laboratory USA) and
Ernst Wolvetang (University of Queensland AUS), biostatistician Assistant
Professor Jessica Mar (Albert Einstein College of Medicine, USA) and
computational biologist Professor John Quackenbush (Harvard University, USA).
Her primary focus is evaluating whether molecular variability in stem cell
populations describes an important, but until now hidden predictor of cellular
behaviour and phenotype. Phenotypic heterogeneity in clonally derived cell
populations is ubiquitous, and biologically relevant information is often
masked by using population-averaging techniques, versus individual cell
based measurements. She has developed new network approaches which incorporate
gene expression variance, with the goal of identifying genetic elements which
stabilize a cell phenotype, and push a cell to transition between phenotypes.
During her PhD Lizzi has been invited to present her work in departmental
seminars at the Harvard Stem Cell Institute, the Lieber Brain Institute at
Johns Hopkins University, and the Black Family Stem Cell Institute at Mt Sainai
Hospital New York. She was also one of 12 international scientists who were
invited to participate in the Radcliffe Exploratory Workshop for Variation at
Harvard University in 2011. She is currently based with Professor Christine
Wells in the Centre for Stem Cell Systems at the University of Melbourne,
where she is working on applied statistical methods to evaluate molecular
variability in single cell RNA-seq data.


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