Bioinformatics Seminar
Time:
Venue: Na
26 September 2017
NaStatistical inference from cancer sequencing data
Simon TavareCancer Research UK Cambridge Institute
Poolseq and cancer sequencing experiments produce aggregated data over different individuals or cells ; for which conventional population genetics analysis methods do not seem appropriate. I will describe our ongoing attempts to understand what can be learned from site frequency data (e.g. ; the numbers of SNVs appearing in various proportions of cells of a tumour sample) obtained from such data. I will describe some theoretical aspects of modelling cancer evolution ; in particular what seems to be easy to infer and what seems to be hard. The methodology comes from the ABC and coalescent part of the subject ; with due allowance for the cancer setting. Time permitting ; I will also outline our recently funded project from the CRUK Grand Challenge competition ; which will provide novel in-situ data in 3.5D on a vast scale ; along with some novel statistical problems.;