Speaker
Description
Recent advancements in high throughput RNA sequencing technologies have generated unprecedented amounts of high-dimensional genomic data, enabling more detailed analysis. Techniques from network science are widely used to analyze such data, but face significant challenges. One shortcoming of these methods is that many of them use thresholding. The lack of consensus regarding the method for choosing the threshold value leads to substantial variability in downstream results and biological interpretations. Furthermore, many networks are constructed based on pairwise relationships between genes, disregarding potential contributions of higher order interactions. We explore Persistent Homology, a key method from Topological Data Analysis that quanti?es topological features across multiple scales and joint cumulants, that capture higher order dependencies, in order to address these limitations. We investigate advantages of using hypergraphs as a framework for modeling higher order interactions and the concept of Structural Balance as a method for incorporating edge sign information. We will present preliminary results.
