Abstract Number:
2500
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Liying Chen (1), Satwik Acharyya (1), Chunyu Luo (2), Yang Ni (3), Veera Baladandayuthapani (1)
Institutions:
(1) University of Michigan, N/A, (2) University of Pennsylvania, N/A, (3) Texas A&M University, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
Probabilistic graphical models are powerful tools to infer, interpret, and visualize complex biological systems. However, most existing graphical models assume homogeneity across samples, limiting their application in heterogeneous contexts e.g. tumor and spatial heterogeneity. We propose a general and flexible Bayesian approach called Graphical Regression (GraphR) which incorporates intrinsic heterogeneity at different scales such as discrete, continuous and spatial, enables sparse network estimation at sample-specific level, has higher precision compared to existing approaches and is computationally efficient for analyses of large genomic datasets. We employ GraphR to analyze four diverse multiomic and spatial transcriptomics datasets to infer inter- and intra-sample genomic networks and delineate several novel biological discoveries. We have developed the GraphR R-package and a user-friendly Shiny App for analysis and dynamic network visualization.
Keywords:
Heterogeneous graphical models|Genomics|Spatial transcriptomics|Variable selection|Variational Bayes|
Sponsors:
Section on Statistics in Genomics and Genetics
Tracks:
Miscellaneous
Can this be considered for alternate subtype?
Yes
Are you interested in volunteering to serve as a session chair?
No
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.
I understand