{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gao C"],"funding":["Howard Hughes Medical Institute","NIAID NIH HHS","NIMH NIH HHS","NHGRI NIH HHS","NCI NIH HHS","U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute","U.S. Department of Health & Human Services | NIH | National Institute of Mental Health","Division of Intramural Research, National Institute of Allergy and Infectious Diseases"],"pagination":["1000-1007"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8355612"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["39(8)"],"pubmed_abstract":["Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation p"],"journal":["Nature biotechnology"],"pubmed_title":["Iterative single-cell multi-omic integration using online learning."],"pmcid":["PMC8355612"],"funding_grant_id":["RF1 MH123199","U19MH114831","R01 AI149669","R01 HG010883","P30 CA014195","T32 HG000040","U19 MH114831"],"pubmed_authors":["Kriebel AR","Liu J","Sandoval J","Behrens MM","Preissl S","Luo C","Rivkin A","Gao C","Ren B","Castanon R","Nery JR","Ecker JR","Welch JD"],"additional_accession":[]},"is_claimable":false,"name":"Iterative single-cell multi-omic integration using online learning.","description":"Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for integrating large, diverse and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation p","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Aug","modification":"2025-04-04T21:30:39.751Z","creation":"2025-04-04T21:30:39.751Z"},"accession":"S-EPMC8355612","cross_references":{"pubmed":["33875866"],"doi":["10.1038/s41587-021-00867-x"]}}