{"id":"https://openalex.org/W7154587672","doi":"https://doi.org/10.1016/j.array.2026.100808","title":"CCVGAE: A centroid-coupled variational graph attention autoencoder for stable and interpretable single-cell representation learning","display_name":"CCVGAE: A centroid-coupled variational graph attention autoencoder for stable and interpretable single-cell representation learning","publication_year":2026,"publication_date":"2026-04-16","ids":{"openalex":"https://openalex.org/W7154587672","doi":"https://doi.org/10.1016/j.array.2026.100808"},"language":"en","primary_location":{"id":"doi:10.1016/j.array.2026.100808","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.100808","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1016/j.array.2026.100808","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013096981","display_name":"Zeyu Fu","orcid":"https://orcid.org/0009-0001-8329-0108"},"institutions":[{"id":"https://openalex.org/I151075929","display_name":"Army Medical University","ror":"https://ror.org/05w21nn13","country_code":"CN","type":"education","lineage":["https://openalex.org/I151075929"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zeyu Fu","raw_affiliation_strings":["State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China"],"raw_orcid":"https://orcid.org/0009-0001-8329-0108","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China","institution_ids":["https://openalex.org/I151075929"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yiyao Liu","orcid":"https://orcid.org/0009-0002-7030-802X"},"institutions":[{"id":"https://openalex.org/I151075929","display_name":"Army Medical University","ror":"https://ror.org/05w21nn13","country_code":"CN","type":"education","lineage":["https://openalex.org/I151075929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiyao Liu","raw_affiliation_strings":["State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China"],"raw_orcid":"https://orcid.org/0009-0002-7030-802X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China","institution_ids":["https://openalex.org/I151075929"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133778806","display_name":"Junping Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I151075929","display_name":"Army Medical University","ror":"https://ror.org/05w21nn13","country_code":"CN","type":"education","lineage":["https://openalex.org/I151075929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junping Wang","raw_affiliation_strings":["State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China","institution_ids":["https://openalex.org/I151075929"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133773613","display_name":"Song Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I151075929","display_name":"Army Medical University","ror":"https://ror.org/05w21nn13","country_code":"CN","type":"education","lineage":["https://openalex.org/I151075929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Song Wang","raw_affiliation_strings":["State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Trauma and Chemical Poisoning, Institute of Combined Injury, Chongqing Engineering Research Center for Nanomedicine, College of Preventive Medicine, Army Medical University, Chongqing, 400038, China","institution_ids":["https://openalex.org/I151075929"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5013096981"],"corresponding_institution_ids":["https://openalex.org/I151075929"],"apc_list":{"value":1350,"currency":"USD","value_usd":1350},"apc_paid":{"value":1350,"currency":"USD","value_usd":1350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.85523499,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"30","issue":null,"first_page":"100808","last_page":"100808"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.32850000262260437,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.32850000262260437,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.19519999623298645,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.16189999878406525,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7822999954223633},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4932999908924103},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4763000011444092},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.46239998936653137},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40119999647140503},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.33629998564720154}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7822999954223633},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6693000197410583},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5213000178337097},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4932999908924103},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4763000011444092},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.46239998936653137},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34220001101493835},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32679998874664307},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2517000138759613}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.array.2026.100808","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.100808","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.array.2026.100808","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.array.2026.100808","pdf_url":null,"source":{"id":"https://openalex.org/S4210194039","display_name":"Array","issn_l":"2590-0056","issn":["2590-0056"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Array","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W1541594543","https://openalex.org/W1895802579","https://openalex.org/W1958742527","https://openalex.org/W1965375035","https://openalex.org/W1983612778","https://openalex.org/W1990974808","https://openalex.org/W2023538413","https://openalex.org/W2038818733","https://openalex.org/W2071359561","https://openalex.org/W2093681208","https://openalex.org/W2154699417","https://openalex.org/W2160304959","https://openalex.org/W2346243632","https://openalex.org/W2505384565","https://openalex.org/W2612519453","https://openalex.org/W2746479894","https://openalex.org/W2763574125","https://openalex.org/W2794891507","https://openalex.org/W2800392236","https://openalex.org/W2901677030","https://openalex.org/W2912390237","https://openalex.org/W2912600324","https://openalex.org/W2926902798","https://openalex.org/W2927507212","https://openalex.org/W3036600937","https://openalex.org/W3112600195","https://openalex.org/W3129866267","https://openalex.org/W3138479716","https://openalex.org/W4205514149","https://openalex.org/W4206042814","https://openalex.org/W4212836561","https://openalex.org/W4220765701","https://openalex.org/W4225278475","https://openalex.org/W4225598893","https://openalex.org/W4296211836","https://openalex.org/W4321452549","https://openalex.org/W4378838672","https://openalex.org/W4380869622","https://openalex.org/W4381094899","https://openalex.org/W4384283548","https://openalex.org/W4385421874","https://openalex.org/W4389046966","https://openalex.org/W4389504853","https://openalex.org/W4392168151","https://openalex.org/W4394881579","https://openalex.org/W4401911481","https://openalex.org/W4402590079","https://openalex.org/W4404778438","https://openalex.org/W4405795066","https://openalex.org/W4407356329","https://openalex.org/W4408242031","https://openalex.org/W4408461205","https://openalex.org/W4408537402","https://openalex.org/W4409212286","https://openalex.org/W4409246105","https://openalex.org/W4409801497","https://openalex.org/W4409820664","https://openalex.org/W4410596035","https://openalex.org/W4410997516","https://openalex.org/W4412540141","https://openalex.org/W4414946633","https://openalex.org/W4415698549","https://openalex.org/W4416666620","https://openalex.org/W4417284384","https://openalex.org/W4417508459","https://openalex.org/W6940340797","https://openalex.org/W7114928862","https://openalex.org/W7117353166","https://openalex.org/W7124448341"],"related_works":[],"abstract_inverted_index":{"Single-cell":[0],"omics":[1],"technologies":[2],"are":[3],"widely":[4],"used":[5],"to":[6,59,92,164,178,319],"dissect":[7],"cellular":[8],"heterogeneity,":[9],"yet":[10],"computational":[11],"analysis":[12],"across":[13,256,292],"modalities":[14,261],"remains":[15],"difficult":[16],"because":[17],"of":[18,26],"high":[19],"dimensionality,":[20],"technical":[21],"noise,":[22],"and":[23,83,113,149,169,195,210,216,228,253,259,299,328,351],"the":[24,51,56],"instability":[25],"stochastic":[27],"deep":[28,171,300],"generative":[29,172,301],"models.":[30,173],"We":[31],"present":[32],"CCVGAE,":[33],"a":[34,64,73,85],"Centroid-based":[35],"Coupled":[36],"Variational":[37],"Graph":[38],"Attention":[39],"Autoencoder":[40],"built":[41],"around":[42],"three":[43],"design":[44],"choices:":[45],"(i)":[46],"Centroid":[47,231],"Inference,":[48],"which":[49],"uses":[50,89],"deterministic":[52,241],"posterior":[53,242],"mean":[54],"as":[55,244],"cell":[57,245],"embedding":[58,136],"avoid":[60],"sampling":[61,234],"variance;":[62],"(ii)":[63],"Coupling":[65,271],"mechanism":[66],"that":[67,88,198],"regularizes":[68],"local":[69],"latent":[70,175,274],"geometry":[71,152,275],"via":[72],"dual-reconstruction":[74],"bottleneck":[75],"(":[76],"d":[77,80],"c":[78],"<":[79,131],"z":[81],");":[82],"(iii)":[84],"graph-attention":[86],"encoder":[87],"cell\u2013cell":[90,280],"similarity":[91],"preserve":[93],"neighborhood":[94],"structure.":[95],"Across":[96],"170":[97,293],"benchmark":[98],"datasets":[99],"(55":[100],"scRNA-seq,":[101],"115":[102],"scATAC-seq),":[103],"CCVGAE":[104,199,219],"improves":[105],"clustering":[106,251],"accuracy":[107,252],"(ARI":[108],"+":[109,115,124,141,155],"0":[110,116,125,132,142,156],".":[111,117,126,133,143,157],"104":[112,118],"NMI":[114],"over":[119,287],"CouVAE":[120],"on":[121,147,161],"scRNA-seq;":[122],"ASW":[123],"329":[127],"versus":[128,145,159],"scVI,":[129],"p":[130],"001":[134],"),":[135],"quality":[137,154,255],"(overall":[138],"UMAP":[139],"score":[140],"219":[144],"scVI":[146],"scRNA-seq),":[148],"intrinsic":[150,310],"manifold":[151,311],"(core":[153],"273":[158],"PeakVI":[160],"scATAC-seq)":[162],"relative":[163],"classical":[165,297],"dimensionality":[166],"reduction":[167],"methods":[168,298],"established":[170],"Individual":[174],"components":[176,201],"map":[177],"coherent":[179,320],"Gene":[180],"Ontology":[181],"Biological":[182,314],"Processes.":[183],"Case":[184],"studies":[185],"in":[186,250,309,335],"hematopoietic":[187,336],"systems":[188],"under":[189],"physiological":[190],"stress\u2014sleep":[191],"deprivation,":[192],"radiation":[193],"injury,":[194],"thrombopoietin-driven":[196],"megakaryopoiesis\u2014indicate":[197],"captures":[200,279],"associated":[202],"with":[203,264,306],"lineage":[204,324],"regulation,":[205,325],"inflammatory":[206],"responses,":[207],"cell-cycle":[208,329],"dynamics,":[209],"hemostasis-related":[211],"programs.":[212],"By":[213],"producing":[214],"stable":[215],"interpretable":[217],"embeddings,":[218,246],"supports":[220],"downstream":[221,345],"single-cell":[222],"analyses":[223,346],"including":[224,323,347],"clustering,":[225,348],"trajectory":[226,349],"inference,":[227,350],"visualization.":[229],"\u2022":[230,268,290,313,339],"Inference":[232],"bypasses":[233],"noise":[235],"at":[236],"inference":[237],"time":[238],"by":[239],"using":[240],"means":[243],"providing":[247],"consistent":[248,307],"improvements":[249,286],"geometric":[254],"both":[257],"scRNA-seq":[258],"scATAC-seq":[260],"when":[262],"combined":[263],"complementary":[265,285],"architectural":[266],"components.":[267],"Complementary":[269],"architecture:":[270],"regularization":[272],"refines":[273],"while":[276],"graph":[277],"attention":[278],"structure;":[281],"their":[282],"combination":[283],"yields":[284],"single-component":[288],"baselines.":[289],"Benchmarking":[291],"datasets:":[294],"Evaluation":[295],"against":[296],"models":[302],"shows":[303],"favorable":[304],"performance":[305],"gains":[308],"properties.":[312],"interpretability:":[315],"Latent":[316],"dimensions":[317],"correspond":[318],"biological":[321,355],"programs":[322],"immune":[326],"activation,":[327],"control,":[330],"validated":[331],"through":[332],"GO":[333],"enrichment":[334],"perturbation":[337],"studies.":[338],"Practical":[340],"utility:":[341],"Stable":[342],"embeddings":[343],"enhance":[344],"visualization,":[352],"supporting":[353],"reproducible":[354],"discovery.":[356]},"counts_by_year":[],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2026-04-17T00:00:00"}