Using experimentation, computation and theory, we characterize the influence of genomic variation on patient phenotypes (like survival) and intermediate phenotypes (like gene expression, transcription factor binding or tissue architecture).
We quantify the heterogeneity of tumours and reconstruct their evolutionary history.
We put genomics in a tissue context by quantifying content and composition of the tumour microenvironment.
We develop computational and statistical methods to visualize, analyze and reconstruct biological networks.
- Macintyre*, Goranova* et al (2018), Copy-number signatures and mutational processes in ovarian carcinoma, Nature Genetics
- Sivakumar*, de Santiago* et al (2017), Master regulators of oncogenic KRAS response in pancreatic cancer: an integrative network biology analysis, PLoS Medicine, 14(1)
- Marass et al (2017), A phylogenetic latent feature model for clonal deconvolution, AOAS 10(4)
- Ross and Markowetz (2016), OncoNEM: Inferring tumour evolution from single-cell sequencing data, Genome Biology, 17:69
- VULCAN - infer cofactors from ChIP-seq differential binding
- OncoNEM - Clonal evolution trees from single cell data
- Bitphylogeny - Bayesian framework for intra-tumour phylogenies
- MEDICC - intra-tumor copy-number comparisons
- nem - inferring Nested Effects Models from downstream perturbation effects
- RedeR - statistical computing and network visualization