Friday, July 7, 2023, 9:30am
Gene networks hold immense importance in understanding the underlying mechanisms that govern cellular activities and organismal behavior. As the true gene interaction is not observable, people often resort to observable gene expression data to statistically infer the gene network. In this thesis, we address the immense challenges in the statistical gene network, including 1) benchmark tool for gene network estimation; 2) nonlinear gene network estimation methods; 3) the application of gene networks in Autism associated gene understanding.
I will present Chapter 4 of my thesis, which focuses on the final challenge. To better understand autism spectrum disorders (ASD), we focus on two types of ASD-related genes: Differential Expression genes (DE) which are differentially expressed in ASD versus neurotypical brains, and TADA genes, which are identified by unusual patterns of genetic mutations. While TADA genes are thought to be "active", DE genes are thought to be either active (cause of ASD) or reactive (outcome of ASD). We aim to dive deep into the mechanism of DE genes: discriminating the "active" ones from "reactive". Relying on the conjecture that active DE gene modules are enriched with TADA genes, while reactive ones are not, we develop a network-assisted approach to bridge these two sources of information and identify an assortment of unique "active" and "reactive" DE gene communities. Our work brings new insights toward understanding the role genes play in the development of ASD and how ASD affects gene expression as well.
Thesis Committee:
Kathryn Roeder (Co-chair)
Jing Lei (Co-chair)
Alessandro Rinaldo
Andrej Risteski
Wei Chen (School of Public Health, University of Pittsburgh)
Zoom Participation. See announcement.
Event Type: Thesis Orals
Room Number: Virtual Presentation - ET
Building: Remote Access - Zoom
Speaker's Name: JINJIN TIAN
Speaker Website: jinjint.github.io
Speaker's Professional Title: Ph.D. Candidate, Joint Ph.D. Program in Statistics and Machine Learning, Carnegie Mellon University
Talk Title: Advances in Statistical Gene Networks
For More Information: stidle@andrew.cmu.edu
Affiliations: Machine Learning Department (MLD)
Organization(s): SCS, Department of Statistics and Data Science