Identifying the molecular components that matter : a statistical modelling approach to linking functional genomics data to cell physiology

摘要: Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured single experiments, have contributed to reshape biological investigations. One the most important issues analysis generated large datasets is selection relatively small sub-sets variables that are predictive physiological state a cell tissue. In this thesis, truly multivariate variable framework using diverse functional data has been developed, characterized, and tested. This also used prove it possible predict tumour from molecular adjacent normal cells. allows us identify novel genes involved communication. Then, network inference technique networks representing cell-cell communication prostate cancer inferred. The these revealed interesting properties suggests crucial role directional signals controlling interplay between Experimental verification performed our laboratory provided evidence one identified could suppressor gene. conclusion, findings methods reported thesis further understanding interaction not only by applying extending previous work, but proposing approaches applied any data.