{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Lee J"],"pubmed_abstract":["<h4>Motivation</h4>Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types 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.<h4>Results</h4>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 gra"],"journal":["Bioinformatics (Oxford, England)"],"pagination":["btad342"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10246584"],"repository":["biostudies-literature"],"pubmed_title":["Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning."],"pmcid":["PMC10246584"],"pubmed_authors":["Kim S","Hyun D","Lee N","Lee J","Kim Y","Park C"],"additional_accession":[]},"is_claimable":false,"name":"Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning.","description":"<h4>Motivation</h4>Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types 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.<h4>Results</h4>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 gra","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-04T08:06:43.609Z","creation":"2025-04-04T08:06:43.609Z"},"accession":"S-EPMC10246584","cross_references":{"pubmed":["37233193"],"doi":["10.1093/bioinformatics/btad342"]}}