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Activity Number:
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246
- Data Science
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Type:
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Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Computing
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Abstract #319164
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Title:
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Network Visualization and Analysis on T and B Cell Receptors from SARS-CoV-2 Patients
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Author(s):
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Hai Yang* and Li Zhang and Zenghua Fan and Tao He and Lawrence Fong
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Companies:
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UCSF and UCSF and UCSF and San Francisco State University and UCSF
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Keywords:
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Network Analysis;
SARS-CoV-2;
Adaptive Immune Response;
Next generation sequencing;
T and B cell receptors
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Abstract:
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T cells and B cells represent a crucial component of the adaptive immune system and have been shown to mediate immune response to SARS-CoV-2. Next-generation sequencing of the T and B cell receptors (TCR and BCR) can be used to profile the T and B cell repertoire (Rep-seq). We develop a customized analysis pipeline with advanced statistical methods and cutting-edge machine learning techniques to characterize and investigate the landscape of Rep-seq changes over serial time points. We first perform social network analysis on Rep-seq data based on the sequence similarity. Network analysis and visualization allow interrogation of sequence similarity and thereby adds a complementary layer of information to repertoire diversity analysis. Network visualization also helps overview the whole dataset with key features and better present conclusions. Next, we quantify repertoire network by network properties and correlate with the clinical outcomes of interest. Furthermore, we develop a customized workflow to identify the public clones which might be antigen-driven clones of SARS-CoV-2. Therefore, it can help us identify potential antigens to target for future immunotherapies.
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Authors who are presenting talks have a * after their name.
