乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T13:47:54Z","timestamp":1733320074361,"version":"3.30.1"},"update-to":[{"updated":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T00:00:00Z","timestamp":1694476800000},"DOI":"10.1371\/journal.pcbi.1011324","type":"new_version","label":"New version"}],"reference-count":78,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T00:00:00Z","timestamp":1692921600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["2U24CA180996"]},{"DOI":"10.13039\/100014989","name":"Chan Zuckerberg Initiative","doi-asserted-by":"publisher","award":["CZF2019-002443"],"id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Belgian National Fund for Scientific Research"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"\nBackground<\/jats:title>\nThe majority of high-throughput single-cell molecular profiling methods quantify RNA expression; however, recent multimodal profiling methods add simultaneous measurement of genomic, proteomic, epigenetic, and\/or spatial information on the same cells. The development of new statistical and computational methods in Bioconductor for such data will be facilitated by easy availability of landmark datasets using standard data classes.<\/jats:p>\n<\/jats:sec>\n\nResults<\/jats:title>\nWe collected, processed, and packaged publicly available landmark datasets from important single-cell multimodal protocols, including CITE-Seq, ECCITE-Seq, SCoPE2, scNMT, 10X Multiome, seqFISH, and G&T. We integrate data modalities via the MultiAssayExperiment<\/jats:italic> Bioconductor class, document and re-distribute datasets as the SingleCellMultiModal<\/jats:italic> package in Bioconductor\u2019s Cloud-based ExperimentHub<\/jats:italic>. The result is single-command actualization of landmark datasets from seven single-cell multimodal data generation technologies, without need for further data processing or wrangling in order to analyze and develop methods within Bioconductor\u2019s ecosystem of hundreds of packages for single-cell and multimodal data.<\/jats:p>\n<\/jats:sec>\n\nConclusions<\/jats:title>\nWe provide two examples of integrative analyses that are greatly simplified by SingleCellMultiModal<\/jats:italic>. The package will facilitate development of bioinformatic and statistical methods in Bioconductor to meet the challenges of integrating molecular layers and analyzing phenotypic outputs including cell differentiation, activity, and disease.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pcbi.1011324","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T17:35:42Z","timestamp":1692984942000},"page":"e1011324","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Curated single cell multimodal landmark datasets for R\/Bioconductor"],"prefix":"10.1371","volume":"19","author":[{"given":"Kelly 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