014: Prioritizing Network Properties of T-cell Receptor Repertoire – A Novel Approach to Select Network Signatures from TCR Repertoire Data

T-cells are one of the key components of the adaptive immune system. T-cell Receptors (TCR) are a group of protein complexes found on the surface of T-cells. TCRs are responsible for recognizing and binding to certain antigens found on abnormal cells or potentially harmful pathogens. Once the TCRs bind to the pathogens, the T-cells attack these cells and help the body fight infection, cancer, or other diseases. TCR repertoires, which are continually shaped throughout the lifetime of an individual in response to pathogenic exposure, can serve as a fingerprint of an individual's current immunological profile. The similarity among TCRs sequence directly influences the antigen recognition breadth. Network analysis, which allows interrogation of sequence similarity, thereby adds an important layer of information. Due to the heterogeneous nature of TCR network properties, it is extremely difficult to perform statistical inference or machine learning directly between subjects. In this paper, we proposed a novel method to prioritize the network properties that are associated with the outcome of interest, based on features extracted from heterogeneous global/local network properties. We also proposed schemes to select the top features associated and simulated the network properties using the real data. Extensive simulation studies and real data analysis were performed to demonstrate the proposed methods. Performance measures including F-1 score, false discovery rate, sensitivity, power, and stability were calculated for each model and are used for model comparison.

T-cell receptor

Feature Selection

Heterogenous Network Data Analysis

Simulation Study

Lasso, Group Lasso, Exclusive Lasso

Cross Validation, Permutation Tuning 

Implementation and Analysis

Conference on Statistical Practice (CSP) 2023