{"doi":"10.1093/bioinformatics/btac684","title":"scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data","abstract":"MOTIVATION: Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell-cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized. RESULTS: The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell-cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms. AVAILABILITY AND IMPLEMENTATION: scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.","journal":"Bioinformatics","year":2022,"id":250792,"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":26,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9557,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":95238,"name":"Hao Cheng","orcid":"0000-0003-3661-1519","position":1,"is_corresponding":false},{"id":235824,"name":"Anjun Ma","orcid":"0000-0001-6269-398X","position":2,"is_corresponding":false},{"id":486530,"name":"Yang Li","orcid":"0000-0002-7677-9028","position":3,"is_corresponding":false},{"id":295202,"name":"Juexin Wang","orcid":"0000-0002-2260-4310","position":4,"is_corresponding":false},{"id":233623,"name":"Dong Xu","orcid":"0000-0002-4809-0514","position":5,"is_corresponding":false},{"id":235827,"name":"Qin Ma","orcid":"0000-0002-3264-8392","position":6,"is_corresponding":false},{"id":893586,"name":"Haocheng Gu","orcid":null,"position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":null,"created_at":"2026-07-19T00:24:32.960657Z","pmid":"36250784","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":[]}