{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Reisman BJ"],"funding":["National Center for Advancing Translational Sciences","NCATS NIH HHS","NIDDK NIH HHS","National Cancer Institute","NCI NIH HHS","National Institute of General Medical Sciences","NIGMS NIH HHS"],"pagination":["946-953"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8410645"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["99(9)"],"pubmed_abstract":["Fluorescent cell barcoding (FCB) enables efficient collection of tens to hundreds of flow cytometry samples by covalently marking cells with varying concentration of spectrally distinct dyes. A key consideration in FCB is to balance the density of dye barcodes, the complexity of cells in the sample, and the desired accuracy of the debarcoding. Unfortunately, barcoding bench and computational methods have not benefited from the high dimensional revolution in cytometry due to a lack of automated computational tools that effectively balance these common cytometry needs. DebarcodeR addresses these unmet needs by providing a framework for computational debarcoding augmented by improvements to experimental methods. Adaptive regression modeling accounted for differential dye uptake between differ"],"journal":["Cytometry. Part A : the journal of the International Society for Analytical Cytology"],"pubmed_title":["DebarcodeR increases fluorescent cell barcoding capacity and accuracy."],"pmcid":["PMC8410645"],"funding_grant_id":["U54 CA217450","P30 CA068485","F30 CA236131","R01 CA226833","R01 GM092218","P30 DK058404","T32 GM007347","P30 CA68485","U01 TR002625","T32 GM065086"],"pubmed_authors":["Irish JM","Barone SM","Reisman BJ","Bachmann BO"],"additional_accession":[]},"is_claimable":false,"name":"DebarcodeR increases fluorescent cell barcoding capacity and accuracy.","description":"Fluorescent cell barcoding (FCB) enables efficient collection of tens to hundreds of flow cytometry samples by covalently marking cells with varying concentration of spectrally distinct dyes. A key consideration in FCB is to balance the density of dye barcodes, the complexity of cells in the sample, and the desired accuracy of the debarcoding. Unfortunately, barcoding bench and computational methods have not benefited from the high dimensional revolution in cytometry due to a lack of automated computational tools that effectively balance these common cytometry needs. DebarcodeR addresses these unmet needs by providing a framework for computational debarcoding augmented by improvements to experimental methods. Adaptive regression modeling accounted for differential dye uptake between differ","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Sep","modification":"2025-04-22T04:45:39.501Z","creation":"2024-11-21T10:29:59.873Z"},"accession":"S-EPMC8410645","cross_references":{"pubmed":["33960644"],"doi":["10.1002/cyto.a.24363"]}}