University of Cambridge > Talks.cam > MRC Biostatistics Unit Seminars > "Breaking non-identifiability using genetic information : an application to metabolite data and gene expression"

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

"Breaking non-identifiability using genetic information : an application to metabolite data and gene expression"

Download to your calendar using vCal

If you have a question about this talk, please contact Alison Quenault .

We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive convergence bounds for this estimator and show that it is well-behaved in the high-dimensional regime as well as “sparsistent” (i.e. capable of recovering the graph structure). We then show how proximal gradient algorithms and semi-definite programming techniques can be employed to fit the model to thousands of variables. Through extensive simulations, we illustrate the conditions required for identifiability and show that there is a wide range of situations in which this model performs significantly better than its counterparts, e.g. it can accommodate more latent variables. Finally, the suggested method is applied to two datasets comprising genetic variants and metabolites levels. We show our results are well replicated across datasets and assess their biological relevance using an external source of validation. We also apply it to a human gene expression dataset.

This talk is part of the MRC Biostatistics Unit Seminars series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Room GS15 at the William Gates Building and on Zoom: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon The Lightfoot Room, Divinity School, St John’s College, St John’s Street, Cambridge CB2 1TP and ZOOM Average on Saturdays: 7850.47443182 kWh

Other talks

Title to be confirmed Cooperation for Nature Group Work Genomic Medicine CPC C2D3 Computational Biology Annual Symposium 2026 The Jiangmen Underground Neutrino Observatory (JUNO): Status and First Physics Results
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity