UMAP visualizations of extracted CD8 + T and CD4 + T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.

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Bibliografische gegevens
Hoofdauteur:	Fatemeh Keikha (20917848) (author)
Andere auteurs:	Chuanyuan Wang (22676721) (author), Zhixia Yang (3180567) (author), Zhi-Ping Liu (149304) (author)
Gepubliceerd in:	2025
Onderwerpen:	Medicine Genetics Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wasserstein optimal transport real world dataset quantified via gromov enhancing diagnostic precision dynamic network index deep neural network ablation studies confirm regulatory role transitions gene regulatory networks combining regulatory rewiring analyzing disease evolution temporal expression dynamics reflect dynamic changes neglecting structural rewiring multilayer network models modeling disease progression state alignment provides understanding cancer progression state graph alignment disease state classification state specific biomarkers disease progression state alignment specific expression multilayer graph cancer progression disease state regulatory variability regulatory roles structural shifts significant changes prioritized biomarkers classification accuracy state single xlink "> topological features synthetic simulated ranked using powerful strategy overall performance meaningful shifts gats ), gastric adenocarcinoma gac ), framework designed findings suggest dni ), distinct layer contextualized embeddings cell data
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author	Fatemeh Keikha (20917848)
author2	Chuanyuan Wang (22676721) Zhixia Yang (3180567) Zhi-Ping Liu (149304)
author2_role	author author author
author_facet	Fatemeh Keikha (20917848) Chuanyuan Wang (22676721) Zhixia Yang (3180567) Zhi-Ping Liu (149304)
author_role	author
dc.creator.none.fl_str_mv	Fatemeh Keikha (20917848) Chuanyuan Wang (22676721) Zhixia Yang (3180567) Zhi-Ping Liu (149304)
dc.date.none.fl_str_mv	2025-11-24T18:36:29Z
dc.identifier.none.fl_str_mv	10.1371/journal.pcbi.1013743.s002
dc.relation.none.fl_str_mv	https://figshare.com/articles/figure/UMAP_visualizations_of_extracted_CD8_sup_sup_T_and_CD4_sup_sup_T_cell_barcodes_employed_for_downstream_subsequent_single-cell_trajectory_and_classification_analyses_/30697929
dc.rights.none.fl_str_mv	CC BY 4.0 info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv	Medicine Genetics Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wasserstein optimal transport real world dataset quantified via gromov enhancing diagnostic precision dynamic network index deep neural network ablation studies confirm regulatory role transitions gene regulatory networks combining regulatory rewiring analyzing disease evolution temporal expression dynamics reflect dynamic changes neglecting structural rewiring multilayer network models modeling disease progression state alignment provides understanding cancer progression state graph alignment disease state classification state specific biomarkers disease progression state alignment specific expression multilayer graph cancer progression disease state regulatory variability regulatory roles structural shifts significant changes prioritized biomarkers classification accuracy state single xlink "> topological features synthetic simulated ranked using powerful strategy overall performance meaningful shifts gats ), gastric adenocarcinoma gac ), framework designed findings suggest dni ), distinct layer contextualized embeddings cell data
dc.title.none.fl_str_mv	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.
dc.type.none.fl_str_mv	Image Figure info:eu-repo/semantics/publishedVersion image
description	<p>UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.</p>
eu_rights_str_mv	openAccess
id	Manara_910e5bc16371bb33b8337429ed9c2b70
identifier_str_mv	10.1371/journal.pcbi.1013743.s002
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/30697929
publishDate	2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv	CC BY 4.0
spelling	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.Fatemeh Keikha (20917848)Chuanyuan Wang (22676721)Zhixia Yang (3180567)Zhi-Ping Liu (149304)MedicineGeneticsBiotechnologyCancerInfectious DiseasesBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedwasserstein optimal transportreal world datasetquantified via gromovenhancing diagnostic precisiondynamic network indexdeep neural networkablation studies confirmregulatory role transitionsgene regulatory networkscombining regulatory rewiringanalyzing disease evolutiontemporal expression dynamicsreflect dynamic changesneglecting structural rewiringmultilayer network modelsmodeling disease progressionstate alignment providesunderstanding cancer progressionstate graph alignmentdisease state classificationstate specific biomarkersdisease progressionstate alignmentspecific expressionmultilayer graphcancer progressiondisease stateregulatory variabilityregulatory rolesstructural shiftssignificant changesprioritized biomarkersclassification accuracystate singlexlink ">topological featuressynthetic simulatedranked usingpowerful strategyoverall performancemeaningful shiftsgats ),gastric adenocarcinomagac ),framework designedfindings suggestdni ),distinct layercontextualized embeddingscell data<p>UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.</p>2025-11-24T18:36:29ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pcbi.1013743.s002https://figshare.com/articles/figure/UMAP_visualizations_of_extracted_CD8_sup_sup_T_and_CD4_sup_sup_T_cell_barcodes_employed_for_downstream_subsequent_single-cell_trajectory_and_classification_analyses_/30697929CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306979292025-11-24T18:36:29Z
spellingShingle	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses. Fatemeh Keikha (20917848) Medicine Genetics Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wasserstein optimal transport real world dataset quantified via gromov enhancing diagnostic precision dynamic network index deep neural network ablation studies confirm regulatory role transitions gene regulatory networks combining regulatory rewiring analyzing disease evolution temporal expression dynamics reflect dynamic changes neglecting structural rewiring multilayer network models modeling disease progression state alignment provides understanding cancer progression state graph alignment disease state classification state specific biomarkers disease progression state alignment specific expression multilayer graph cancer progression disease state regulatory variability regulatory roles structural shifts significant changes prioritized biomarkers classification accuracy state single xlink "> topological features synthetic simulated ranked using powerful strategy overall performance meaningful shifts gats ), gastric adenocarcinoma gac ), framework designed findings suggest dni ), distinct layer contextualized embeddings cell data
status_str	publishedVersion
title	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.
title_full	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.
title_fullStr	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.
title_full_unstemmed	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.
title_short	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.
title_sort	UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.
topic	Medicine Genetics Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wasserstein optimal transport real world dataset quantified via gromov enhancing diagnostic precision dynamic network index deep neural network ablation studies confirm regulatory role transitions gene regulatory networks combining regulatory rewiring analyzing disease evolution temporal expression dynamics reflect dynamic changes neglecting structural rewiring multilayer network models modeling disease progression state alignment provides understanding cancer progression state graph alignment disease state classification state specific biomarkers disease progression state alignment specific expression multilayer graph cancer progression disease state regulatory variability regulatory roles structural shifts significant changes prioritized biomarkers classification accuracy state single xlink "> topological features synthetic simulated ranked using powerful strategy overall performance meaningful shifts gats ), gastric adenocarcinoma gac ), framework designed findings suggest dni ), distinct layer contextualized embeddings cell data

UMAP visualizations of extracted CD8<sup> + </sup> T and CD4<sup> + </sup> T cell barcodes, employed for downstream subsequent single-cell trajectory and classification analyses.

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