The focus of my group is the development of computational statistical methods for applications in genetics and genomics. The main areas of work include:
- Translational Computational Oncogenomics. The development of cutting edge computational statistical methods and tools that can be widely used by specialists and non-specialists alike for research and clinical practice in cancer.
- Single Cell Informatics. Developing novel statistical techniques for single cell genomics.
- Data-driven Statistics. Developing novel statistical ideas and generic techniques that are inspired by real data analysis problems in genetics and genomics. Developing computational techniques formulated on sound statistical principals for the the analysis of very large datasets commonly found in genomics (Big Data) and learning biologically relevant features (Deep or Representation Learning).
A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes
KaramiNejadRanjbar M. et al, (2020), eLife, 9
Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma.
Mourikis TP. et al, (2019), Nature communications, 10
Bayesian statistical learning for big data biology.
Yau C. and Campbell K., (2019), Biophys Rev
A descriptive marker gene approach to single-cell pseudotime inference.
Campbell KR. and Yau C., (2019), Bioinformatics, 35, 28 - 35
Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data.
Campbell KR. and Yau C., (2018), Nat Commun, 9