Speaker: Professor Rebecka Jörnsten, Chalmers University of Technology/University of Gothenburg
Venue: Seminar room Pascal, Gamma floor 6, SciLifeLab, Tomtebodavägen 23A, Solna
Statistical network modeling techniques have the potential to increase our understanding of cancer genomics data. Here, we analyze multiple TCGA data sets via a generalized sparse inverse covariance model, carefully addressing such challenges as unbalanced sample sizes, local network topology, model selection and robust estimation.
The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer.
The modeling results are available at cancerlandscapes.org, where the derived networks can be explored as interactive web content and be compared with several pathway and pharmacological databases.
I will also present a novel analysis pipeline, NetCoR, that summarizes network estimation uncertainty via candidate graph structures, serving as an analogue for high-dimensional confidence intervals. This paradigm has multiple benefits; (i) The local number of candidate networks captures the confidence in the estimated structure (and this confidence level is also found to be linked to better overlap with known pathways); (ii) This method provides a fair and efficient way to compare different estimation methods.
This is joint work with Jose Sanchez, Alexandra Jauhiainen and the Nelander lab, SciLifeLab, Uppsala.
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