Seminars

In this talk, I describe two novel nonparametric, inference-based 
approaches for analyses of genetic and genomic heterogeneity 
associated with groups of similar phenotype.  A common theme between  them is the construction of testable hypotheses within the confine of very few samples from data of very high dimension. With a modest  sample size, I discuss a distance-based approach to analysis of  genetic heterogeneity based on population sequence data.  In the more  extreme case of data on several single sample groups to be compared from a microarray experiment, I introduce the concept of stochastic linear hypotheses that generalizes the Mann-Whitney Wilcoxon rank sum test to accommodate greater than two group comparisons in a multivariate setting.  In each case, I also discuss bioinformatics approaches to characterize observed heterogeneity differences among  groups of similar phenotype, either in terms of either locations  within a sequence or genes within a genome.  As motivation for the  methods, I examine two separate problems, one for relating sequence  differences in a region of the HIV genome to drug resistance, and a  second for relating gene expression differences to hypothesized  pathways for immunogenetic analysis of T cells.