Biostatistics Virtual Seminar - Dr. Ali Shojaie on Differential Network Analysis
When & Where
January 17, 2023
12:00 PM - 1:15 PM
WebEx or RAS-811 ( View in Google Map)
Contact
- Scott Dyson
- [email protected]
Event Description
Presenter:
Ali Shojaie, PhD
Professor of Biostatistics and Statistics (adjunct)
Associate Chair, Department of Biostatistics
University of Washington
Location: RAS E-811 if you wish to attend in person, otherwise WebEx. The presenter will be connecting via WebEx.
WebEx Link: https://uthealth.webex.com/uthealth/j.php?MTID=m23d314e628d45d5aff4f1dd2699d398c
WebEx Password: kJrBPnGU957
Abstract
Recent evidence suggests that changes in biological networks, e.g., rewiring or disruption of key interactions, may be associated with development of complex diseases. These findings have motivated new research in computational and experimental biology that aim to obtain condition-specific estimates of biological networks, e.g. for normal and tumor samples, and identify differential patterns of connectivity in such networks, known as differential network analysis. In this talk, we primarily focus on testing whether two Gaussian graphical models are the same. We will first illustrate that existing inference procedures for this task may lead to misleading results. To address this shortcoming, we propose a two-step inference framework, for testing the null hypothesis that the edge sets in two networks are the same. The proposed framework is especially appropriate if the goal is to identify nodes or edges that show differential connectivity. Time permitting, we will also discuss how differential network analysis methods can be extended to non-Gaussian settings as well as settings where differences in network edges are functions of other covariates.
Event Site Link
https://uthealth.webex.com/uthealth/j.php?MTID=m23d314e628d45d5aff4f1dd2699d398c
Additional Information
Presenter:
Ali Shojaie, PhD
Professor of Biostatistics and Statistics (adjunct)
Associate Chair, Department of Biostatistics
University of Washington
Location: RAS E-811 if you wish to attend in person, otherwise WebEx. The presenter will be connecting via WebEx.
WebEx Link: https://uthealth.webex.com/uthealth/j.php?MTID=m23d314e628d45d5aff4f1dd2699d398c
WebEx Password: kJrBPnGU957
Abstract
Recent evidence suggests that changes in biological networks, e.g., rewiring or disruption of key interactions, may be associated with development of complex diseases. These findings have motivated new research in computational and experimental biology that aim to obtain condition-specific estimates of biological networks, e.g. for normal and tumor samples, and identify differential patterns of connectivity in such networks, known as differential network analysis. In this talk, we primarily focus on testing whether two Gaussian graphical models are the same. We will first illustrate that existing inference procedures for this task may lead to misleading results. To address this shortcoming, we propose a two-step inference framework, for testing the null hypothesis that the edge sets in two networks are the same. The proposed framework is especially appropriate if the goal is to identify nodes or edges that show differential connectivity. Time permitting, we will also discuss how differential network analysis methods can be extended to non-Gaussian settings as well as settings where differences in network edges are functions of other covariates.