Computational Methods for Single Cell Biology

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The biological sciences are undergoing a revolution driven by high throughput technologies which allow for the measurement of single cells. A prominent example is the rapid adoption of single cell RNA profiling using sequencing based approaches to explore all facets of biology. More recently other single cell sequencing approaches have been developed, allowing for the measurement of the DNA and epigenetic features of cells. The majority of approaches to date have required that cells be put into a liquid suspension for sequencing. This requires that tissue be broken apart and spatial information such as which cells were neighbouring be discarded. This makes it difficult to study how cells interact with each other. The cutting edge of single cell biology is removing this limitation by combining high throughput measurements with imaging or spatial barcoding. Spatial single cell technologies have enormous potential for advancing our understanding of biological systems. However, the data generated by these technologies are creating analytical challenges that are not adequately addressed by existing computational tools. These challenges range from low level problems in signal processing such as performing automated image analysis to identify cells and their expression profiles, to higher level problems such as extracting interpretable estimates of biologically interesting quantities from the data. Off the shelf machine learning approaches do not adequately address many of these problems. The two most important characteristics of this domain that challenge existing tools are the lack of well annotated datasets to train supervised learning approaches and the desire by biologists to have interpretable computational methods. In addition, the ability to leverage domain knowledge to impose additional structure on problems can significantly improve performance. I will develop novel computational and statistical models to analyze multi-modal single cell datasets to provide an integrated view of biological systems. Our approach will allow us to deeply probe the features of individual cells, while also measuring the spatial context and environment they exist in. The methods I develop will drive new biological discoveries by allowing spatial single cell profiling of complex systems such as tumours.