Keynote Speaker

The analysis of large-scale expression datasets is frequently compromised by hidden structure between samples. In the context of genetic association studies, this structure can be linked to differences between individuals, which can reflect their genetic makeup (such as population structure) or be traced back to environmental and technical factors.

In this talk, I will discuss statistical methods to reconstruct this structure from the observed data to account for it in genetic analyses.

In the second part of this talk I will extend the introduced class of latent variable models to model biological and technical sources of heterogeneity in single-cell transcriptome datasets. In applications to a T helper cell differentiation study, we show how this model allows for dissecting expression patterns of individual genes and reveals new substructure between cells that is linked to cell differentiation.

I will finish with an outlook of modeling challenges and initial solutions that enable combining multiple omics layers that are profiled in the same set of single cells.