New methods to study tumour heterogeneity with single-cell genomic data
Differences in the behaviour of cancer cells within a single tumour drive tumour growth, responses to treatment, metastasis and health outcomes. This intra-tumoral heterogeneity can now be studied with single-cell genomics at unprecedented resolution, but sophisticated statistical methods are necessary to model cell populations and their behaviour from large, complex datasets. This project will develop new statistical, machine learning and software tools to link DNA, gene expression and other single-cell 'omics' data to accelerate discovery in cancer. We will build on methods developed in the group to assign single-cell transcriptomes to subclones in a cell population using somatic mutations expressed and detectable in the single-cell RNA-sequencing reads (http://dx.doi.org/10.1101/413047). We apply hierarchical Bayesian models to handle the complex data generation processes that produce single-cell genomic data.
Dr Davis McCarthy
Bioinformatics & cellular genomics
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