Detection of outliers with Bayesian matrix factorization: application to whole genome data analysis

22nd January 2015

Michael Blum , research associate at CNRS, lab TIMC-IMAG, Grenoble, France

Abstract: We present a new Bayesian hierarchical model based on matrix factorization for detecting outliers in high-dimensional data. Outliers are explicitly modeled using a variance inflation model. The Bayesian framework provides intrinsic probabilities of being an outlier for each element in the sample. Posterior replicates of the parameters are simulated using a MCMC algorithm. In population genetics where many genetic markers are typed in different populations, we show that this model can be used to detect genes targeted by Darwinian selection.

More details on XRCE seminars