001     300692
005     20250509113036.0
024 7 _ |a 10.1093/nargab/lqaf042
|2 doi
024 7 _ |a pmid:40276039
|2 pmid
024 7 _ |a pmc:PMC12019640
|2 pmc
037 _ _ |a DKFZ-2025-00872
041 _ _ |a English
082 _ _ |a 570
100 1 _ |a González-Velasco, Oscar
|0 0000-0002-5054-8635
|b 0
245 _ _ |a Identifying similar populations across independent single cell studies without data integration.
260 _ _ |a Oxford
|c 2025
|b Oxford University Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1745841239_33
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a #LA:B330#
520 _ _ |a Supervised and unsupervised methods have emerged to address the complexity of single cell data analysis in the context of large pools of independent studies. Here, we present ClusterFoldSimilarity (CFS), a novel statistical method design to quantify the similarity between cell groups across any number of independent datasets, without the need for data correction or integration. By bypassing these processes, CFS avoids the introduction of artifacts and loss of information, offering a simple, efficient, and scalable solution. This method match groups of cells that exhibit conserved phenotypes across datasets, including different tissues and species, and in a multimodal scenario, including single-cell RNA-Seq, ATAC-Seq, single-cell proteomics, or, more broadly, data exhibiting differential abundance effects among groups of cells. Additionally, CFS performs feature selection, obtaining cross-dataset markers of the similar phenotypes observed, providing an inherent interpretability of relationships between cell populations. To showcase the effectiveness of our methodology, we generated single-nuclei RNA-Seq data from the motor cortex and spinal cord of adult mice. By using CFS, we identified three distinct sub-populations of astrocytes conserved on both tissues. CFS includes various visualization methods for the interpretation of the similarity scores and similar cell populations.
536 _ _ |a 312 - Funktionelle und strukturelle Genomforschung (POF4-312)
|0 G:(DE-HGF)POF4-312
|c POF4-312
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 2 |a Single-Cell Analysis: methods
|2 MeSH
650 _ 2 |a Animals
|2 MeSH
650 _ 2 |a Mice
|2 MeSH
650 _ 2 |a Spinal Cord: cytology
|2 MeSH
650 _ 2 |a Spinal Cord: metabolism
|2 MeSH
650 _ 2 |a Motor Cortex: cytology
|2 MeSH
650 _ 2 |a Motor Cortex: metabolism
|2 MeSH
650 _ 2 |a Astrocytes: metabolism
|2 MeSH
650 _ 2 |a Astrocytes: cytology
|2 MeSH
650 _ 2 |a RNA-Seq
|2 MeSH
650 _ 2 |a Cluster Analysis
|2 MeSH
700 1 _ |a Simon, Malte
|b 1
700 1 _ |a Yilmaz, Rüstem
|b 2
700 1 _ |a Parlato, Rosanna
|b 3
700 1 _ |a Weishaupt, Jochen
|b 4
700 1 _ |a Imbusch, Charles D
|b 5
700 1 _ |a Brors, Benedikt
|0 P:(DE-He78)fc949170377b58098e46141d95c72661
|b 6
|e Last author
|u dkfz
773 _ _ |a 10.1093/nargab/lqaf042
|g Vol. 7, no. 2, p. lqaf042
|0 PERI:(DE-600)3009998-5
|n 2
|p lqaf042
|t NAR: genomics and bioinformatics
|v 7
|y 2025
|x 2631-9268
909 C O |o oai:inrepo02.dkfz.de:300692
|p VDB
|p OpenAPC
|p openCost
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 6
|6 P:(DE-He78)fc949170377b58098e46141d95c72661
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-312
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Funktionelle und strukturelle Genomforschung
|x 0
914 1 _ |y 2025
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2024-04-03T10:37:58Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2024-04-03T10:37:58Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2024-04-03T10:37:58Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2025-01-02
915 _ _ |a WoS
|0 StatID:(DE-HGF)0112
|2 StatID
|b Emerging Sources Citation Index
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2025-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2025-01-02
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2025-01-02
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2025-01-02
915 p c |a APC keys set
|2 APC
|0 PC:(DE-HGF)0000
915 p c |a Local Funding
|2 APC
|0 PC:(DE-HGF)0001
915 p c |a DFG OA Publikationskosten
|2 APC
|0 PC:(DE-HGF)0002
915 p c |a DOAJ Journal
|2 APC
|0 PC:(DE-HGF)0003
920 2 _ |0 I:(DE-He78)B330-20160331
|k B330
|l Angewandte Bioinformatik
|x 0
920 1 _ |0 I:(DE-He78)B330-20160331
|k B330
|l Angewandte Bioinformatik
|x 0
920 1 _ |0 I:(DE-He78)HD01-20160331
|k HD01
|l DKTK HD zentral
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)B330-20160331
980 _ _ |a I:(DE-He78)HD01-20160331
980 _ _ |a UNRESTRICTED
980 _ _ |a APC


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21