서울대학교 생물정보학 협동과정 주최로 세미나를 아래와 같이 열고자 하오니 많은 참여 바랍니다.

세미나는 Zoom을 통한 온라인 강의로 예정되어 있으며 강의에 참여하고자 하시는 분께서는 아래의 링크로 참여 부탁드리겠습니다.

*줌으로 참석하기 위해서는 "참석자로 회의 참가" --> 이름, 성, 이메일 주소, 이메일 주소 확인을 입력하시고 등록하시면 회의 참석이 가능합니다.

일시: 2023년 4월 27일 목요일 오전 11시

연사: 김광수 교수님 (서울대학교)

Zoom link: http://snu-ac-kr.zoom.us/s/97788264130

Single cell multiomics data analysis for Korean COVID-19 patients

Single-cell transcriptome analysis has revealed numerous COVID-19 severity-related biomarkers. However, most research focuses on symptom severity, neglecting immune system feature variations within the same severity group and limited information about patients' unique T-cell receptor (TCR) repertoire. In this seminar, we introduce three studies using single-cell samples from Korean COVID-19 patients, highlighting diverse research approaches with single-cell multiomics data. First, we explore heterogeneity in molecular features among patients within the same severity class through cell-cell interaction score-based sample clustering, defining and examining COVID-19 subtypes. Second, we present a "Deep Learning-Based Analysis of T-cell Receptor Repertoire in COVID-19 Patients," using BERT to analyze patient features and predict T-cell receptor CDR3 epitopes. Finally, we investigate "sample-specific network clustering" with COVID-19 scRNA-seq data. We construct networks reflecting differences between COVID-19 and healthy samples and assess associations between sample-specific networks and clinical variables through clustering. This seminar aims to showcase diverse research approaches for better understanding COVID-19 using single-cell multiomics data.