Leveraging statistical shapes in genomics: looking beyond what’s Normal.

Abstract

In genomics, we often assume that continuous data, such as gene expression, follows a single type of statistical distribution. However, we rarely stop to question the validity of this assumption, and whether it applies uniformly to every gene measured in the genome. This talk includes studies from my group where a focus on the shape of gene expression distributions, specifically shape diversity, revealed new insights into biology. These studies highlight the value of studying the shape of a gene’s expression distribution as a means to model heterogeneity in the genome. These insights would not have been uncovered using standard approaches that assume a single distribution applies to everyone in a patient cohort. Collectively, this work raises new questions and opportunities to investigate how diversity in statistical shapes can help explain heterogeneity in genomics. 

Biography

Associate Professor Jessica Mar is a Group Leader at the University of Queensland. She leads a computational biology group that investigates how variability in the genome contributes to the regulation of diseases like cancer, or phenotypes like pluripotency in stem cells. A/Prof Mar received her PhD in Biostatistics from Harvard University in 2008. She was a postdoctoral fellow at the Dana-Farber Cancer Institute in Boston (2008-11), and an Assistant Professor at Albert Einstein College of Medicine in New York (2011-2018).  

Having relocated back to Australia as an ARC Future Fellow in July 2018, her research program has a dual focus that concentrates on modelling the aging process using single cell bioinformatics, and understanding cancer genomics through novel statistical methods. A/Prof Mar has received several awards, including a Fulbright scholarship (2003), the Metcalf Prize for Stem Cell Research from the National Stem Cell Foundation of Australia (2017), and the LaDonne H. Shulman Award for Teaching Excellence at Albert Einstein College of Medicine (2017).