{"doi":"10.1093/bib/bbad132","title":"TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level","abstract":"Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.","journal":"Briefings in Bioinformatics","year":2023,"id":338309,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9201,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1070720,"name":"Wei Guo","orcid":"0000-0002-3163-9742","position":1,"is_corresponding":false},{"id":60427,"name":"Chen Li","orcid":"0000-0002-1847-754X","position":2,"is_corresponding":false},{"id":660307,"name":"Long-Chen Shen","orcid":"0000-0002-0045-4745","position":3,"is_corresponding":false},{"id":396809,"name":"Robin B. Gasser","orcid":"0000-0002-4423-1690","position":4,"is_corresponding":false},{"id":258542,"name":"Jiangning Song","orcid":"0000-0001-8031-9086","position":5,"is_corresponding":false},{"id":45431,"name":"Dijun Chen","orcid":"0000-0002-7456-2511","position":6,"is_corresponding":false},{"id":626617,"name":"Dong‐Jun Yu","orcid":"0000-0002-6786-8053","position":7,"is_corresponding":false},{"id":314002,"name":"Yan Liu","orcid":"0000-0003-4242-4840","position":0,"is_corresponding":true}],"reference_count":65,"raw_metadata":null,"created_at":"2026-07-19T01:10:35.973121Z","pmid":"37080771","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}