{"id":"https://openalex.org/W7161706060","doi":"https://doi.org/10.48550/arxiv.2605.18587","title":"PACE: Geometry-Aware Bridge Transport for Single-Cell Trajectory Inference","display_name":"PACE: Geometry-Aware Bridge Transport for Single-Cell Trajectory Inference","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161706060","doi":"https://doi.org/10.48550/arxiv.2605.18587"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18587","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18587","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.18587","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136465558","display_name":"Chenglei Yu*","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Chenglei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136485126","display_name":"Chuanrui Wang*","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chuanrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125513315","display_name":"Bangyan Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Bangyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136464768","display_name":"Tailin Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Tailin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.8841000199317932,"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"}},"topics":[{"id":"https://openalex.org/T11289","display_name":"Single-cell and spatial transcriptomics","score":0.8841000199317932,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.017799999564886093,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10604","display_name":"RNA Research and Splicing","score":0.008100000210106373,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5712000131607056},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5684000253677368},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.44530001282691956},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.43799999356269836},{"id":"https://openalex.org/keywords/tangent","display_name":"Tangent","score":0.39309999346733093},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.3783000111579895},{"id":"https://openalex.org/keywords/synchronism","display_name":"Synchronism","score":0.3564999997615814}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5712000131607056},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5684000253677368},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5109000205993652},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4984000027179718},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.44530001282691956},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.43799999356269836},{"id":"https://openalex.org/C138187205","wikidata":"https://www.wikidata.org/wiki/Q131251","display_name":"Tangent","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.362199991941452},{"id":"https://openalex.org/C2779853468","wikidata":"https://www.wikidata.org/wiki/Q7662139","display_name":"Synchronism","level":3,"score":0.3564999997615814},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C13393347","wikidata":"https://www.wikidata.org/wiki/Q8424","display_name":"Aerodynamics","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C122044880","wikidata":"https://www.wikidata.org/wiki/Q5498822","display_name":"Ergodic theory","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2906000018119812},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C47890412","wikidata":"https://www.wikidata.org/wiki/Q1179296","display_name":"Tangent vector","level":3,"score":0.27570000290870667},{"id":"https://openalex.org/C91188154","wikidata":"https://www.wikidata.org/wiki/Q186247","display_name":"Vector field","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26420000195503235}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18587","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18587","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.18587","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18587","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Single-cell":[0],"trajectory":[1,72],"inference":[2,73],"from":[3,81],"destructive":[4,82],"time-course":[5,83],"snapshots":[6,84],"is":[7,53,211],"fundamentally":[8],"ill-posed:":[9],"neither":[10],"cross-time":[11,119],"cell":[12,201],"correspondences":[13],"nor":[14],"continuous":[15,78],"trajectories":[16,50],"are":[17],"observed,":[18],"so":[19],"the":[20,28,59,122,137,163,181],"snapshot":[21],"distributions":[22],"alone":[23],"do":[24],"not":[25],"uniquely":[26],"determine":[27],"underlying":[29],"dynamics.":[30],"Existing":[31],"optimal":[32],"transport":[33,79,102],"and":[34,55,94,126,153,172],"flow-based":[35],"methods":[36],"typically":[37],"couple":[38],"cells":[39,56],"by":[40,175,190],"Euclidean":[41],"proximity":[42],"at":[43,58,213],"observed":[44],"clock":[45],"times,":[46],"which":[47],"can":[48],"misalign":[49],"when":[51],"development":[52],"asynchronous":[54],"sampled":[57],"same":[60],"experimental":[61],"time":[62],"occupy":[63],"different":[64],"latent":[65],"pseudotime":[66],"stages.":[67],"We":[68],"propose":[69],"PACE,":[70],"a":[71,92,142],"framework":[74],"that":[75,99],"recovers":[76],"geometry-consistent":[77],"dynamics":[80,140],"through":[85],"three":[86],"coupled":[87],"components.":[88,113],"First,":[89],"PACE":[90,161,185],"constructs":[91],"state-":[93],"time-dependent":[95],"anisotropic":[96],"Riemannian":[97],"metric":[98],"assigns":[100],"low":[101],"cost":[103,125],"along":[104],"locally":[105],"supported":[106],"tangent":[107],"directions":[108],"while":[109],"penalizing":[110],"normal":[111],"velocity":[112,145],"Second,":[114],"it":[115,135],"alternates":[116],"between":[117,131],"refining":[118],"couplings":[120],"under":[121],"induced":[123],"path-action":[124],"fitting":[127],"endpoint-preserving":[128],"neural":[129],"bridges":[130],"adjacent":[132],"snapshots.":[133],"Third,":[134],"distills":[136],"learned":[138],"bridge":[139],"into":[141],"global":[143],"continuous-time":[144],"field":[146],"over":[147],"cellular":[148],"states.":[149],"Across":[150],"seven":[151],"controlled":[152],"biological":[154],"datasets":[155],"covering":[156],"nine":[157],"held-out":[158],"reconstruction":[159,166],"experiments,":[160],"achieves":[162],"strongest":[164,182],"overall":[165],"performance,":[167],"reducing":[168],"MMD,":[169],"Wasserstein-1":[170],"distance,":[171],"Wasserstein-2":[173],"distance":[174],"23.7%":[176],"on":[177,192],"average":[178],"relative":[179],"to":[180],"competing":[183],"baseline.":[184],"also":[186],"improves":[187],"RNA-velocity":[188,206],"alignment":[189],"15.4%":[191],"an":[193],"embryoid":[194],"body":[195],"differentiation":[196],"benchmark,":[197],"without":[198],"requiring":[199],"explicit":[200],"pairing,":[202],"lineage":[203],"tracing,":[204],"or":[205],"supervision":[207],"during":[208],"training.":[209],"Code":[210],"available":[212],"https://github.com/AI4Science-WestlakeU/PACE.":[214]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}