The Markowetz lab for Quantitative Biology develops algorithms and statistics to leverage complex and heterogeneous data sources for biomedical research.
Our main research question is: How do perturbations to cellular mechanisms shape phenotypes?
Research
Natural perturbations like copy number alterations and SNPs can promote cancer development. To identify drivers of cancer we dissect tumor heterogeneity at all levels: stratification at the population level, data fusion at the patient level, and deconvolution of cellular heterogeneity at the sample level.
Experimental perturbations like RNAi are key approaches at the forefront of functional genomics. We develop methods to identify signaling pathways and their re-wiring from downstream effects of gene perturbations and to computationally guide experimental exploration of genetic architecture.
Events
- We continue the series 'From phenotypes to pathways' in Sept 2012.
- Florian gives invited talk at Bioinformatics and Systems Biology - Bridging the Divide, 14-17 Feb 2012, in Leiden.
Publications
RedeR: an R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations M.A.A. Castro et al. Genome Biology 2012, 13:R29 [ PMID:22531049 ]
The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups Curtis et al. Nature 2012 [ PMID:22522925 ]
Patient-specific data fusion defines prognostic cancer subtypes Y. Yuan et al. PLoS Comp Bio 7(10) [ PMID:22028636 ]
Software
- nem - an R/Bioconductor package to infer Nested Effects Models
- RedeR - bridging the gap between statistical computing and network visualization
- HTSanalyzeR - network analysis of high-throughput screens
