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Category:
Scientific Computing

Lecturer:
Prof. Susan Holmes, Department of Statistics, School of Humanities & Sciences, Stanford University, Stanford, California, USA

Place:
EMBL, Large Operon, Meyerhofstraße 1

Host:
Wolfgang Huber

Description:
We overcome the heterogeneity of the sources of information (NGS, trees, graphs, histories, metabolomics) using iterative structuration and sparse projection methods. Our models enable effective denoising of NGS data and the removal of contaminants. Tree-aware projections enable the combination of phylogenetic information and temporal and spatial dependencies. I will show examples of how our approach has enabled us to perform high resolution analyses of longitudinal data, and in particular, how we were able to discover biomarkers for preterm birth. These results are medically relevant and were recently confirmed in a completely independent replications study. The research focus of the Holmes group at the Department of Statistics in Stanford is realistic analyses of heterogeneous data and quantification of uncertainty for complex biological data. Our software and data are all open-source and can be downloaded and analysed using the R and D3 tools we have developed (dada2, phyloseq, treelapse).

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