DMS Statistics and Data Science Seminar

Speaker: Antony Pearson (Auburn University)

Title: Quantifying Structure within Unstructured Symbolic Data

Abstract: Modern biological research is epitomized by “omics” experiments, which produce millions to billions of symbolic outcomes in the form of reads (i.e., DNA sequences of a few dozen to a few hundred nucleotides). Unfortunately, these intrinsically non-numerical datasets are often highly contaminated, and the possible sources of contamination are usually poorly characterized. The latter contrasts with continuous datasets, where it is often well-justified to assume that the distribution of contaminating samples is Gaussian. To overcome hurdles associated with these data, I will introduce the notion of “latent weights,” which measure the largest expected fraction of samples from a contaminated probabilistic source that conforms to a model in a well-structured class of desired models. As proof of concept, I use latent weights to reevaluate a long-standing assumption used in most modern DNA methylation analysis.

Location: https://auburn.zoom.us/j/83299681626?pwd=SjlGNi9MWWhEMExGM0c0QzBPK0hMZz09

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See Statistics & Data Science Seminar - Auburn University College of Sciences and Mathematics for complete calendar