“Multi-group covariance estimation with applications to ‘omics data’”
Wednesday, May 15, from 4:30 to 5:20 p.m. — Physics/Astronomy Auditorium, room A118
Alexander Franks, Assistant Professor, Department of Statistics and Applied Probability, University of California, Santa Barbara
[Watch a recording of this seminar on YouTube after it occurs.]
Understanding the function of biological molecules requires statistical methods for assessing covariability of features across multiple dimensions. In particular, there is a relative dearth of methods for estimating how correlations between features differ across distinct subgroups (e.g. disease phenotype). To address this problem, we develop a Bayesian model-based method for evaluating heterogeneity among multiple covariance matrices when the number of features is larger than the sample size.
We propose an optimization algorithm for identifying a low-dimensional subspace which explains variation across subgroups and use a Monte Carlo algorithm to estimate the uncertainty in principal components. I will illustrate the utility of our method for exploratory analyses of high-dimensional multivariate gene expression and metabolomics data.
I am an assistant professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. My research interests include covariance estimation, multivariate analysis and high dimensional data, causal inference, missing data, and errors-in-variables models.
My applied research interests include computational and statistical modeling of “omics” data. I am also an active member of the XY Research group, which conducts research in sports statistics with a focus on player-tracking data.
This event is open to the public.