Functional network analysis reveals an immune tolerance mechanism in cancer
Abstract
A major problem in data science is representation of data so that the variables driving key functions can be uncovered and explored. Correlation analysis is widely used to simplify networks of feature variables by reducing redundancies, but makes limited use of the network topology, relying on comparison of direct neighbor variables. The proposed method incorporates relational or functional profiles of neighboring variables along multiple common neighbors, which are fitted with Gaussian mixture models and compared using a data metric based on a version of optimal mass transport tailored to Gaussian mixtures. Hierarchical interactive visualization of the result leads to effective unbiased hypothesis generation. In a cancer gene expression study, this method uncovered an unanticipated immunosuppressive mechanism resembling maternal–fetal immune tolerance.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 29, 2020
- Source ID
- 10.1073/pnas.2002179117
Entities
People
- Allen Tannenbaum
- Arnold J. Levine
- James C Mathews
- Joseph Deasy
- Maryam Pouryahya
- Saad Nadeem
- Zehor Belkhatir
Organizations
- Air Force Office of Scientific Research
- Institute for Advanced Study
- Memorial Sloan Kettering Cancer Center
- National Institutes of Health
- Stony Brook University
- The Breast Cancer Research Foundation