Sandra Rodriguez-Zas leads the Laboratory of Statistical Genetics and Bioinformatics at the University of Illinois. She is also a co-Principal Investigator on the BeeSpace Project at UIUC.

Title: Applications of linear models to microarray studies

Abstract: The challenges of analyzing and interpreting microarray data are a consequence of the potentially multiple sources of variation and complex experimental designs.  I will present three examples of application of statistical tools to gene expression information. First, I will compare the outcomes from Restricted Maximum Likelihood and Bayesian approaches to characterize gene expression patterns in different tissues. Second, I will present clustering and semiparametric approaches to identify major gene expression trajectories across physiological stages. Third, I will use linear mixed effects models to compare results from two microarray platforms used to characterize healthy and diseased samples.

Bio: Sandra Rodriguez-Zas is an Associate Professor at the Department of Animal Sciences and is an adjunct professor at the Department of Statistics, University of Illinois at Urbana-Champaign. She obtained her Ph.D. in quantitative genetics at the University of Wisconsin-Madison where she worked on Bayesian analysis of longitudinal data. Her areas of expertise are statistical genomics and bioinformatics and her research interests include modeling of gene expression data, detection of quantitative trait loci and bioinformatics tools to characterize nucleotide and amino acid sequences.