DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Park, Chanyoung | - |
dc.contributor.advisor | 박찬영 | - |
dc.contributor.author | Lee, Junseok | - |
dc.date.accessioned | 2023-06-23T19:31:12Z | - |
dc.date.available | 2023-06-23T19:31:12Z | - |
dc.date.issued | 2023 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1032750&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/308795 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2023.2,[iii, 43 p. :] | - |
dc.description.abstract | Single-cell RNA sequencing (scRNA-seq) enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying subgroups of cells with clustering techniques becomes an important task for downstream analysis. However, challenges of scRNA-seq data such as pervasive dropout phenomena hinder obtaining robust clustering outputs. Although existing studies try to alleviate these problems, they fall short of fully leveraging the relationship information and mainly rely on reconstruction-based losses that highly depend on the data quality, which is sometimes noisy. This work proposes a graph-based prototypical contrastive learning method, named scGPCL. Specifically, scGPCL encodes the cell representations using Graph Neural Networks on cell-gene graph that captures the relational information inherent in scRNA-seq data and introduces prototypical contrastive learning to learn cell representations by pushing apart semantically dissimilar pairs and pulling together similar ones. Through extensive experiments on both simulated and real scRNA-seq data, we demonstrate the effectiveness and efficiency of scGPCL. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Single-cell RNA sequencing▼aClustering▼aGraph neural network▼aPrototypical contrastive learning | - |
dc.subject | 단일 세포 시퀀싱▼a군집화▼a그래프 인공 신경망▼a프로토타입 대조 학습 | - |
dc.title | Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning | - |
dc.title.alternative | 그래프 프로토타입 대조 심층 학습을 이용한 단일 세포 시퀀싱 데이터 군집화 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :산업및시스템공학과, | - |
dc.contributor.alternativeauthor | 이준석 | - |
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