017: Examining the Adaptive Immune Response to SARS-CoV-2 by Network Analysis and Machine Learning Techniques

T cells and B cells are the guardians of our immune system against pathogens, foreign substances, and infections, including respiratory viruses such as SARS-CoV-2. Understanding the T and B cell repertoire provides better knowledge of the response mechanism of our immune system. Next-generation sequencing helps us to profile the T and B cell repertoire (Rep-seq). However, it also requires novel statistical approaches and machine learning techniques to analyze those new data types. We applied a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods and cutting-edge machine learning techniques developed by our team to characterize and investigate changes in the landscape of Rep-seq for SARS-CoV-2 data from COVID-19 subjects. We first performed network analysis on the Rep-seq data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified COVID-19-specific/associated clusters based on our customized search algorithms and assessed their relationship with clinical outcomes such as active status and recovery from COVID-19 infection. Furthermore, to identify potential antigen-driven TCRs among disease-specific clusters we designed a new metric incorporating the clonal generation probability and the clonal abundance by using a modified Bayes factor to filter out the false positives. We also validated our findings by comparing our results with an external dataset. Our results demonstrate that our novel approach to analyzing the network architecture of the immune repertoire can reveal potential antigen-driven TCRs responsible for the immune response to the infection.

Immune repertoire sequencing

network analysis

COVID-19

SARS-CoV-2 

Implementation and Analysis

Conference on Statistical Practice (CSP) 2023