We describe a strategy for analysis of experimentally derived gene expression signatures and their translation to human observational data. Sparse multivariate regression models are used to identify expression signature gene sets representing ``downstream'' biological pathway events following interventions in designed experiments. When translated into in vivo human observational data, analysis using sparse latent factor models can yield multiple quantitative factors characterizing expression patterns that are often more complex than in the controlled, in vitro setting. The estimation of common patterns in expression that reflect all aspects of covariation evident in vivo offers an enhanced, modular view of the complexity of biological associations of signature genes. This can identify substructure in the biological process under experimental investigation and improved biomarkers of clinical outcomes. We illustrate the approach in a detailed study from an oncogene intervention experiment where in vivo factor profiling of an in vitro signature generates biological insights related to underlying pathway activities and chromosomal structure, and leads to refinements of cancer recurrence risk stratification across several cancer studies.

Data and input/output files from the analyses are available here.

• In Vitro analysis details: Text files containing the data, BFRM input input and parameter setting files and all BFRM output files from the oncogene intervention analysis. Further information on running BFRM and using output files is available at the BFRM software page.
• In Vivo analysis details: Text files containing the data, BFRM input input and parameter setting files and all BFRM output files from the breast cancer sparse latent factor analysis. Further information on running BFRM and using output files is available at the BFRM software page.
• Survival analysis details: Text files containing the data, SSS survival analysis input and parameter setting files and all output files for the breast cancer survival analysis. Further information on running SSS and using output files is available at the SSS software page.