Exploiting data structure: from gene expression to evolution
Genome scale molecular datasets are often highly structured, with many correlated observations. This general phenomenon can be related to the underlying data generating process. For example, in gene expression assays, groups of genes can be co-regulated/co-expressed through shared transcription factors and signaling pathways. Indeed, many application of gene expression analysis rely on their ability to reflect these unobserved biological processes in order to draw mechanistic conclusions.
In this talk we will discuss several approaches to modeling and exploiting data structure across diverse genomic data types. We will illustrate how data structure can be used to infer unobserved pathway-level effects, improve the predictive power of genomic datasets, and provide insight into the factors driving evolutionary rate covariation.