biostudies-literatureLin XU.S. Department of Health & Human Services | NIH | National Center for Advancing Translational SciencesNCATS NIH HHSNCCIH NIH HHSNIDDK NIH HHS227-235https://www.ebi.ac.uk/biostudies/studies/S-EPMC8742847biostudies-literatureUnknown102(3)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 labeLaboratory investigation; a journal of technical methods and pathologyAn active learning approach for clustering single-cell RNA-seq data.PMC8742847UL1 TR003017UL1TR003017R01 DK119198R01 DK102934R01 AT010243Liu HRoy SBGao NLin XWei ZfalseAn active learning approach for clustering single-cell RNA-seq data.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 labe2022-01-01T00:00:00Z2022 Mar2025-04-29T11:02:36.652Z2025-04-06T19:47:56.828ZS-EPMC87428473424461610.1038/s41374-021-00639-w