Speaker: Professor Rebecka Jörnsten, Chalmers University of Technology/University of Gothenburg

Venue: Seminar room Pascal, Gamma floor 6, SciLifeLab, Tomtebodavägen 23A, Solna

Abstract: 
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.

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Ok!Read More
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may have an effect on your browsing experience.
Necessary
Always Enabled

Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.

Non-necessary

Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.