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 investigate the genetic and protein factors that determine the binding dynamics of nuclear receptors.
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.
- Schwarz et al (2015), Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction, PLoS Med, 12(2)
- Yuan et al (2015), BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies, Genome Biol, 16:36
- Trinh et al (2014), GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images, Genome Biol, 15:442
- 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