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