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 and tissue architecture.
We quantify the heterogeneity of tumours and reconstruct their evolutionary history.
We investigate the genetic and protein factors that determine the binding dynamics of nuclear receptors.
We put genomics in a tissue context by integrating tissue phenotypes with paired genomic profiles.
We develop computational and statistical methods to visualize, analyze and reconstruct biological networks.
- Schwarz et al (2014), Phylogenetic quantification of intra-tumour heterogeneity, PLoS Comp Bio 10(4)
- Trinh et al (2014), GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images, Genome Biology, 15:442
- Fletcher et al (2013), Master regulators of FGFR2 signalling and breast cancer risk, Nature Commun 4:2464
- Yuan et al (2012), Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling, Science Transl Med 4
- Bitphylogeny - Bayesian framework for intra-tumour phylogenies
- CRImage - tumour tissue analysis
- GoIFISH - quantify single cell heterogeneity from IFISH images
- MEDICC - intra-tumor copy-number comparisons
- nem - inferring Nested Effects Models from downstream perturbation effects
- RedeR - bridging the gap between statistical computing and network visualization