{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lin X"],"funding":["U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences","NCATS NIH HHS","NCCIH NIH HHS","NIDDK NIH HHS"],"pagination":["227-235"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8742847"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["102(3)"],"pubmed_abstract":["Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover undiscovered cell types. Most methods for clustering scRNA-seq data use an unsupervised learning strategy. Since the clustering step is separated from the cell annotation and labeling step, it is not uncommon for a totally exotic clustering with poor biological interpretability to be generated-a result generally undesired by biologists. To solve this problem, we proposed an active learning (AL) framework for clustering scRNA-seq data. The AL model employed a learning algorithm that can actively query biologists for labels, and this manual labe"],"journal":["Laboratory investigation; a journal of technical methods and pathology"],"pubmed_title":["An active learning approach for clustering single-cell RNA-seq data."],"pmcid":["PMC8742847"],"funding_grant_id":["UL1 TR003017","UL1TR003017","R01 DK119198","R01 DK102934","R01 AT010243"],"pubmed_authors":["Liu H","Roy SB","Gao N","Lin X","Wei Z"],"additional_accession":[]},"is_claimable":false,"name":"An active learning approach for clustering single-cell RNA-seq data.","description":"Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover undiscovered cell types. Most methods for clustering scRNA-seq data use an unsupervised learning strategy. Since the clustering step is separated from the cell annotation and labeling step, it is not uncommon for a totally exotic clustering with poor biological interpretability to be generated-a result generally undesired by biologists. To solve this problem, we proposed an active learning (AL) framework for clustering scRNA-seq data. The AL model employed a learning algorithm that can actively query biologists for labels, and this manual labe","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2025-04-29T11:02:36.652Z","creation":"2025-04-06T19:47:56.828Z"},"accession":"S-EPMC8742847","cross_references":{"pubmed":["34244616"],"doi":["10.1038/s41374-021-00639-w"]}}