ResolVI: addressing noise and bias in spatial transcriptomics.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	ResolVI: addressing noise and bias in spatial transcriptomics.
Συγγραφείς:	Ergen C; Center for Computational Biology, University of California, Berkeley, CA, USA. ergenbehr@gmail.com.; Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, USA. ergenbehr@gmail.com.; Würzburg Institute of Systems Immunology, Julius-Maximilians-Universität Würzburg, Würzburg, Germany. ergenbehr@gmail.com., Yosef N; System Immunology, Weizmann Institute of Science, Rehovot, Israel. nir.yosef@weizmann.ac.il.
Πηγή:	Nature methods [Nat Methods] 2026 Oct; Vol. 23 (10), pp. 2003-2014. Date of Electronic Publication: 2026 Sep 24.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: Nature Pub. Group Country of Publication: United States NLM ID: 101215604 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1548-7105 (Electronic) Linking ISSN: 15487091 NLM ISO Abbreviation: Nat Methods Subsets: MEDLINE
Imprint Name(s):	Original Publication: New York, NY : Nature Pub. Group, c2004-
Ιατρικοί όροι (MeSH):	Spatial Transcriptomics/methods , Gene Expression Profiling/methods , Software* , Transcriptome*, Algorithms ; Animals ; Humans
Περίληψη:	Technologies for estimating RNA expression at high throughput, in intact tissue slices and with high spatial resolution (spatial transcriptomics) shed new light on how cells communicate and tissues function. A fundamental step in analyzing data generated by subcellular resolution spatial transcriptomics technologies is quantification, namely, segmenting the plane into regions, each approximating a cell, and then collating the molecules inside each region to estimate the cellular expression profile. Despite many advances in this area, a persistent problem is that of the incorrect assignment of molecules to cells, which limits many current applications to the level of a priori-defined cell subsets and complicates the discovery of novel cell states. Here we develop resolVI, a model that operates downstream of any segmentation algorithm to generate a probabilistic representation, correcting for the misassignment of molecules, as well as for batch effects and other nuisance factors. We demonstrate that resolVI improves our ability to distinguish between cell states, identify subtle expression changes in space and perform integrated analysis across datasets. ResolVI is available as open source software within scvi-tools. (© 2026. The Author(s).)
Competing Interests:	Competing interests: The authors declare no competing interests.
References:	Nat Methods. 2022 Jan;19(1):41-50. (PMID: 34949812) Nat Biotechnol. 2022 Feb;40(2):163-166. (PMID: 35132262) BMC Genomics. 2025 Oct 30;26(1):974. (PMID: 41168710) Nat Biotechnol. 2026 Feb 10;:. (PMID: 41667711) Nat Biotechnol. 2022 Apr;40(4):555-565. (PMID: 34795433) Cell. 2024 Apr 11;187(8):2010-2028.e30. (PMID: 38569542) Nat Methods. 2021 Jan;18(1):100-106. (PMID: 33318659) Nat Biotechnol. 2022 Mar;40(3):345-354. (PMID: 34650268) Nat Biotechnol. 2022 Jan;40(1):121-130. (PMID: 34462589) Nat Methods. 2025 Jun;22(6):1331-1342. (PMID: 40404994) Cell. 2018 Aug 9;174(4):999-1014.e22. (PMID: 30096314) Proc Natl Acad Sci U S A. 2023 May 23;120(21):e2209124120. (PMID: 37192164) Nat Biotechnol. 2022 Feb;40(2):245-253. (PMID: 34594043) Genome Biol. 2018 Feb 6;19(1):15. (PMID: 29409532) Cell Stem Cell. 2023 Apr 6;30(4):433-449.e8. (PMID: 37028407) Nat Methods. 2019 Dec;16(12):1289-1296. (PMID: 31740819) Nat Methods. 2024 Jun;21(6):1103-1113. (PMID: 38532015) Nat Methods. 2018 Dec;15(12):1053-1058. (PMID: 30504886) Science. 2023 Aug 4;381(6657):eabq4964. (PMID: 37535749) J Hepatol. 2024 May;80(5):730-743. (PMID: 38199298) Oncogene. 2011 Dec 8;30(49):4901-9. (PMID: 21666710) Genome Biol. 2024 Jun 12;25(1):153. (PMID: 38867267) Gastroenterology. 2023 Feb;164(2):241-255. (PMID: 36279923) Trends Immunol. 2010 Jun;31(6):220-7. (PMID: 20538542) Nat Metab. 2022 Nov;4(11):1591-1610. (PMID: 36400935) PLoS Genet. 2024 Apr 22;20(4):e1011235. (PMID: 38648200) Cell Syst. 2021 May 19;12(5):446-456.e9. (PMID: 33951459)
Grant Information:	448802458 Deutsche Forschungsgemeinschaft (German Research Foundation); EOSS4-0000000121 Silicon Valley Community Foundation (SVCF); 101089213 EC \| EU Framework Programme for Research and Innovation H2020 \| H2020 Priority Excellent Science \| H2020 European Research Council (H2020 Excellent Science - European Research Council)
Entry Date(s):	Date Created: 20260924 Date Completed: 20261008 Latest Revision: 20261010
Update Code:	20261010
PubMed Central ID:	PMC13645626
DOI:	10.1038/s41592-026-03212-9
PMID:	42786290
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1548-7105
DOI:	10.1038/s41592-026-03212-9