Dergi makalesi Açık Erişim
Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan
{ "conceptrecid": "263585", "created": "2024-02-09T06:52:40.355414+00:00", "doi": "10.1002/pmic.202300282", "files": [ { "bucket": "a9b11077-3b99-4f0e-9bc0-d1fd3610351f", "checksum": "md5:771ae7d5e242edee80a66b8f4ed279f5", "key": "SCPRO-HI.zip", "links": { "self": "https://aperta.ulakbim.gov.tr/api/files/a9b11077-3b99-4f0e-9bc0-d1fd3610351f/SCPRO-HI.zip" }, "size": 154807681, "type": "zip" } ], "id": 263586, "links": { "badge": "https://aperta.ulakbim.gov.tr/badge/doi/10.1002/pmic.202300282.svg", "bucket": "https://aperta.ulakbim.gov.tr/api/files/a9b11077-3b99-4f0e-9bc0-d1fd3610351f", "doi": "https://doi.org/10.1002/pmic.202300282", "html": "https://aperta.ulakbim.gov.tr/record/263586", "latest": "https://aperta.ulakbim.gov.tr/api/records/263586", "latest_html": "https://aperta.ulakbim.gov.tr/record/263586" }, "metadata": { "access_right": "open", "access_right_category": "success", "creators": [ { "affiliation": "Gebze Teknik \u00dcniversitesi", "name": "Koca, Mehmet Burak" }, { "affiliation": "Bo\u011fazi\u00e7i \u00dcniversitesi", "name": "Sevilgen, Fatih Erdo\u011fan" } ], "description": "<p>The use of mass spectrometry and antibody-based sequencing technologies at the single-cell level has led to an increase in single-cell proteomic datasets. Integrating these datasets is crucial to eliminate the batch effect that often arises due to their limited sequencing molecules. Although methods for horizontally integrating high-dimensional single-cell transcriptomic datasets can also be applied to single-cell proteomic datasets, a specialized approach explicitly tailored for low-dimensional proteomic datasets may enhance the integration process. Here, we introduce SCPRO-HI, an algorithm for the horizontal integration of antibody-based single-cell proteomic datasets. It utilizes a hierarchical cell anchoring technique to match cells based on the similarity of distinctive proteins for constituting cell clusters. A novel variational auto-encoder model is employed for correcting batch effects on the protein abundances, eliminating the need for mapping them into a new domain. Moreover, we propose a technique for extending the algorithm to high-dimensional datasets. The performance of the SCPRO-HI algorithm is evaluated using simulated and real-world single-cell proteomic datasets. The findings demonstrate our algorithm outperforms state-of-the-art methods, achieving a 75% higher silhouette score while preserving HVPs 13% better. Furthermore, the algorithm shows competitive performance in transcriptomic datasets, suggesting potential for integrating high-dimensional mass-spectrometry-based proteomic datasets.</p>", "doi": "10.1002/pmic.202300282", "has_grant": true, "keywords": [ "single-cell", "data integration", "batch effect", "variational autoencoder", "cell matching" ], "language": "eng", "license": { "id": "cc-by-sa" }, "publication_date": "2023-12-22", "relations": { "version": [ { "count": 1, "index": 0, "is_last": true, "last_child": { "pid_type": "recid", "pid_value": "263586" }, "parent": { "pid_type": "recid", "pid_value": "263585" } } ] }, "resource_type": { "subtype": "article", "title": "Dergi makalesi", "type": "publication" }, "science_branches": [ "Temel Bilimler > Ya\u015fam Bilimleri > Biyoinformatik", "Teknik Bilimler > Bilgisayar Bilimleri" ], "title": "Integration of single-cell proteomic datasets through distinctive proteins in cell clusters", "tubitak_grants": [ { "program": "1002", "project_number": "122E587", "workgroup": "EEEAG" } ] }, "owners": [ 1803 ], "revision": 1, "stats": { "downloads": 7.0, "unique_downloads": 7.0, "unique_views": 74.0, "version_downloads": 7.0, "version_unique_downloads": 7.0, "version_unique_views": 74.0, "version_views": 81.0, "version_volume": 1083653767.0, "views": 81.0, "volume": 1083653767.0 }, "updated": "2024-02-09T06:52:40.399236+00:00" }
Görüntülenme | 81 |
İndirme | 7 |
Veri hacmi | 1.1 GB |
Tekil görüntülenme | 74 |
Tekil indirme | 7 |