{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T02:29:51Z","timestamp":1775874591212,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T00:00:00Z","timestamp":1734307200000},"content-version":"vor","delay-in-days":24,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32000459"],"award-info":[{"award-number":["32000459"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81673036"],"award-info":[{"award-number":["81673036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Despite significant advancements in single-cell sequencing analysis for characterizing tissue sample heterogeneity, identifying the associations between cell subpopulations and disease phenotypes remains a challenging task. Here, we introduce scPAS, a new bioinformatics tool designed to integrate bulk data to identify phenotype-associated cell subpopulations within single-cell data. scPAS employs a network-regularized sparse regression model to quantify the association between each cell in single-cell data and a phenotype. Additionally, it estimates the significance of these associations through a permutation test, thereby identifying phenotype-associated cell subpopulations. Utilizing simulated data and various single-cell datasets from breast carcinoma, ovarian cancer, and atherosclerosis, as well as spatial transcriptomics data from multiple cancers, we demonstrated the accuracy, flexibility, and broad applicability of scPAS. Evaluations on large datasets revealed that scPAS exhibits superior operational efficiency compared to other methods. The open-source scPAS R package is available at GitHub website: https:\/\/github.com\/aiminXie\/scPAS.<\/jats:p>","DOI":"10.1093\/bib\/bbae655","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T22:59:56Z","timestamp":1734389996000},"source":"Crossref","is-referenced-by-count":6,"title":["scPAS: single-cell phenotype-associated subpopulation identifier"],"prefix":"10.1093","volume":"26","author":[{"given":"Aimin","family":"Xie","sequence":"first","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin, 157 Baojian Road, Heilongjiang 150081 ,","place":["China"]}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin, 157 Baojian Road, Heilongjiang 150081 ,","place":["China"]}]},{"given":"Jiaxu","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin, 157 Baojian Road, Heilongjiang 150081 ,","place":["China"]}]},{"given":"Zhaoyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Genetron Health (Beijing) Co. Ltd , 1-2\/F, Building 11, Zone 1, 8 Life Science Parkway, Changping District, Beijing 102208 ,","place":["China"]}]},{"given":"Jinyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin, 157 Baojian Road, Heilongjiang 150081 ,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1387-8459","authenticated-orcid":false,"given":"Yan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Bioinformatics Science and Technology, Harbin Medical University , Harbin, 157 Baojian Road, Heilongjiang 150081 ,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2024,12,16]]},"reference":[{"key":"2024121622593989500_ref1","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1038\/s41581-018-0021-7","article-title":"Sequencing for the study of development, physiology and disease","volume":"14","author":"Potter","year":"2018","journal-title":"Nat Rev Nephrol"},{"key":"2024121622593989500_ref2","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1016\/j.cell.2019.10.003","article-title":"Landscape and dynamics of Single immune cells in hepatocellular carcinoma","volume":"179","author":"Zhang","year":"2019","journal-title":"Cell"},{"key":"2024121622593989500_ref3","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1016\/j.cell.2019.03.005","article-title":"A Single-cell atlas of the tumor and immune ecosystem of human breast cancer","volume":"177","author":"Wagner","year":"2019","journal-title":"Cell"},{"key":"2024121622593989500_ref4","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1038\/s41588-022-01187-9","article-title":"Identifying disease-critical cell types and cellular processes by integrating single-cell RNA-sequencing and human genetics","volume":"54","author":"Jagadeesh","year":"2022","journal-title":"Nat Genet"},{"key":"2024121622593989500_ref5","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1016\/j.cell.2019.01.031","article-title":"Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity","volume":"176","author":"Galen","year":"2019","journal-title":"Cell"},{"key":"2024121622593989500_ref6","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.1038\/s41467-022-29366-6","article-title":"Single-cell and spatial analysis reveal interaction of FAP + fibroblasts and SPP1 + macrophages in colorectal cancer","volume":"13","author":"Qi","year":"2022","journal-title":"Nat Commun"},{"key":"2024121622593989500_ref7","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s13059-020-1926-6","article-title":"Eleven grand challenges in single-cell data science","volume":"21","author":"L\u00e4hnemann","year":"2020","journal-title":"Genome Biol"},{"key":"2024121622593989500_ref8","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.molcel.2019.05.003","article-title":"Single-cell RNA sequencing in cancer: lessons learned and emerging challenges","volume":"75","author":"Suv\u00e0","year":"2019","journal-title":"Mol Cell"},{"key":"2024121622593989500_ref9","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome atlas pan-cancer analysis project","volume":"45","author":"Weinstein","year":"2013","journal-title":"Nat Genet"},{"key":"2024121622593989500_ref10","doi-asserted-by":"publisher","first-page":"bar026","DOI":"10.1093\/database\/bar026","article-title":"International cancer genome consortium data portal-a one-stop shop for cancer genomics data","volume":"2011","author":"Zhang","year":"2011","journal-title":"Database"},{"key":"2024121622593989500_ref11","doi-asserted-by":"publisher","first-page":"1479","DOI":"10.1016\/j.ccell.2021.09.008","article-title":"Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer","volume":"39","author":"Chan","year":"2021","journal-title":"Cancer Cell"},{"key":"2024121622593989500_ref12","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.3390\/biom11081161","article-title":"Clinical perspectives of single-cell RNA sequencing","volume":"11","author":"Kim","year":"2021","journal-title":"Biomolecules"},{"key":"2024121622593989500_ref13","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1038\/s41587-021-01091-3","article-title":"Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data","volume":"40","author":"Sun","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024121622593989500_ref14","doi-asserted-by":"publisher","first-page":"12112","DOI":"10.1093\/nar\/gkac1109","article-title":"scAB detects multiresolution cell states with clinical significance by integrating single-cell genomics and bulk sequencing data","volume":"50","author":"Zhang","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2024121622593989500_ref15","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s13073-022-01012-2","article-title":"Diagnostic evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease","volume":"14","author":"Johnson","year":"2022","journal-title":"Genome Med"},{"key":"2024121622593989500_ref16","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2024121622593989500_ref17","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1002\/sim.7526","article-title":"Efficient \u21130 -norm feature selection based on augmented and penalized minimization","volume":"37","author":"Li","year":"2018","journal-title":"Stat Med"},{"key":"2024121622593989500_ref18","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1186\/s13059-017-1305-0","article-title":"Splatter: simulation of single-cell RNA sequencing data","volume":"18","author":"Zappia","year":"2017","journal-title":"Genome Biol"},{"key":"2024121622593989500_ref19","doi-asserted-by":"publisher","DOI":"10.1101\/217737","article-title":"K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data","author":"Wagner","year":"2018","journal-title":"bioRxiv"},{"key":"2024121622593989500_ref20","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1186\/s13059-019-1812-2","article-title":"MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions","volume":"20","author":"Baran","year":"2019","journal-title":"Genome Biol"},{"key":"2024121622593989500_ref21","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1016\/j.cell.2018.05.061","article-title":"Recovering gene interactions from single-cell data using data diffusion","volume":"174","author":"Dijk","year":"2018","journal-title":"Cell"},{"key":"2024121622593989500_ref22","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/s41592-018-0033-z","article-title":"SAVER: gene expression recovery for single-cell RNA sequencing","volume":"15","author":"Huang","year":"2018","journal-title":"Nat Methods"},{"key":"2024121622593989500_ref23","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/s41467-018-03405-7","article-title":"An accurate and robust imputation method scImpute for single-cell RNA-seq data","volume":"9","author":"Li","year":"2018","journal-title":"Nat Commun"},{"key":"2024121622593989500_ref24","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1186\/1471-2105-14-7","article-title":"GSVA: gene set variation analysis for microarray and RNA-seq data","volume":"14","author":"H\u00e4nzelmann","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2024121622593989500_ref25","doi-asserted-by":"publisher","first-page":"e107333","DOI":"10.15252\/embj.2020107333","article-title":"A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast","volume":"40","author":"Pal","year":"2021","journal-title":"EMBO J"},{"key":"2024121622593989500_ref26","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1038\/s42003-022-04056-7","article-title":"Decoding the transcriptome of calcified atherosclerotic plaque at single-cell resolution","volume":"5","author":"Alsaigh","year":"2022","journal-title":"Commun Biol"},{"key":"2024121622593989500_ref27","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.cell.2011.04.005","article-title":"Macrophages in the pathogenesis of atherosclerosis","volume":"145","author":"Moore","year":"2011","journal-title":"Cell"},{"key":"2024121622593989500_ref28","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1038\/s41569-020-0352-5","article-title":"T cell subsets and functions in atherosclerosis","volume":"17","author":"Saigusa","year":"2020","journal-title":"Nat Rev Cardiol"},{"key":"2024121622593989500_ref29","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1016\/j.immuni.2017.09.008","article-title":"Monocyte-macrophages and T cells in atherosclerosis","volume":"47","author":"Tabas","year":"2017","journal-title":"Immunity"},{"key":"2024121622593989500_ref30","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1161\/CIRCULATIONAHA.111.046755","article-title":"Auto-antigenic protein-DNA complexes stimulate plasmacytoid dendritic cells to promote atherosclerosis","volume":"125","author":"D\u00f6ring","year":"2012","journal-title":"Circulation"},{"key":"2024121622593989500_ref31","doi-asserted-by":"publisher","first-page":"e458","DOI":"10.1002\/ctm2.458","article-title":"Integrative multiomics analysis of human atherosclerosis reveals a serum response factor-driven network associated with intraplaque hemorrhage","volume":"11","author":"Jin","year":"2021","journal-title":"Clin Transl Med"},{"key":"2024121622593989500_ref32","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s12038-013-9310-2","article-title":"Identification of two genes potentially associated in iron-heme homeostasis in human carotid plaque using microarray analysis","volume":"38","author":"Ayari","year":"2013","journal-title":"J Biosci"},{"key":"2024121622593989500_ref33","doi-asserted-by":"publisher","first-page":"3590","DOI":"10.1158\/1078-0432.CCR-22-0296","article-title":"Single-cell RNA sequencing reveals the tissue architecture in human high-grade serous ovarian cancer","volume":"28","author":"Xu","year":"2022","journal-title":"Clin Cancer Res"},{"key":"2024121622593989500_ref34","doi-asserted-by":"publisher","first-page":"1374","DOI":"10.1158\/1078-0432.CCR-11-2725","article-title":"High-risk ovarian cancer based on 126-gene expression signature is uniquely characterized by downregulation of antigen presentation pathway","volume":"18","author":"Yoshihara","year":"2012","journal-title":"Clin Cancer Res"},{"key":"2024121622593989500_ref35","doi-asserted-by":"publisher","first-page":"3794","DOI":"10.1158\/1078-0432.CCR-16-2196","article-title":"Bevacizumab may differentially improve ovarian cancer outcome in patients with proliferative and mesenchymal molecular subtypes","volume":"23","author":"Kommoss","year":"2017","journal-title":"Clin Cancer Res"},{"key":"2024121622593989500_ref36","doi-asserted-by":"publisher","first-page":"5198","DOI":"10.1158\/1078-0432.CCR-08-0196","article-title":"Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome","volume":"14","author":"Tothill","year":"2008","journal-title":"Clin Cancer Res"},{"key":"2024121622593989500_ref37","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1146\/annurev-pathol-020117-043854","article-title":"Epithelial mesenchymal transition in tumor metastasis","volume":"13","author":"Mittal","year":"2018","journal-title":"Annu Rev Pathol"},{"key":"2024121622593989500_ref38","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1038\/s41587-021-00935-2","article-title":"Spatial transcriptomics at subspot resolution with BayesSpace","volume":"39","author":"Zhao","year":"2021","journal-title":"Nat Biotechnol"},{"key":"2024121622593989500_ref39","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1038\/s41592-019-0548-y","article-title":"High-definition spatial transcriptomics for in situ tissue profiling","volume":"16","author":"Vickovic","year":"2019","journal-title":"Nat Methods"},{"key":"2024121622593989500_ref40","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1038\/s41576-018-0088-9","article-title":"Challenges in unsupervised clustering of single-cell RNA-seq data","volume":"20","author":"Kiselev","year":"2019","journal-title":"Nat Rev Genet"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae655\/61200415\/bbae655.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/1\/bbae655\/61200415\/bbae655.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T22:59:58Z","timestamp":1734389998000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae655\/7925487"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,22]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,11,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae655","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,1]]},"published":{"date-parts":[[2024,11,22]]},"article-number":"bbae655"}}