SOMX: Statistical OmicsTelephone number:
Email Address: email@example.com
I am a Research Fellow funded by the Lopez-Loreta Foundation (as of June 2021) and joined the MRC Biostatistics Unit in August 2019. I am leading research on Bayesian modelling for high-dimensional data, which involves designing scalable hierarchical approaches for sparse regression, latent factor and graphical modelling. This research is applied to biomedical studies with diverse clinical and biological data – including genome-wide association and molecular quantitative trait locus studies – to help advance our understanding of the molecular processes driving disease risk and progression.
ORCID: 0000-0002-7113-2540Selected papers
- H. Ruffieux, B. Fairfax, I. Nassiri, E. Vigorito, C. Wallace, S. Richardson, L. Bottolo. (2021) EPISPOT : An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies, The American Journal of Human Genetics, 108:1-18.
- H. Ruffieux, A. C. Davison, J. Hager, J. Inshaw, B. Fairfax, S. Richardson, and L. Bottolo. (2020) A global-local approach for detecting hotspots in multiple response regression, The Annals of Applied Statistics, 14:905-928.
- H. Ruffieux, J. Carayol, R. Popescu, M. E. Harper, R. Dent, W. H. M. Saris, A. Astrup, A. C. Davison, J. Hager, and A. Valsesia. (2020) A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma, PLOS Computational Biology, 16:e1007882.
- H. Ruffieux, A. C. Davison, J. Hager, and I. Irincheeva. (2017) Efficient inference for genetic association studies with multiple outcomes, Biostatistics 18: 618–636
- ATLASQTL (fast global-local hotspot QTL detection)
- ECHOSEQ (synthetic-data generator to emulate genotyping and molecular datasets)
- EPISPOT (annotation-driven approach for large-scale joint regression with multiple responses)
- LOCUS (large-scale variational inference for variable selection in sparse multiple-response regression)