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SpaDiff denoises sequence-based spatial transcriptomics via diffusion process.

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Τίτλος: SpaDiff denoises sequence-based spatial transcriptomics via diffusion process.
Συγγραφείς: Cai J; Department of Statistics, University of Georgia, Athens, GA, USA., Chen Y; Department of Statistics, Harvard University, Cambridge, MA, USA., Fang L; Department of Statistics, University of Georgia, Athens, GA, USA., Liu X; Department of Statistics, University of Georgia, Athens, GA, USA., Zhong W; Department of Statistics, University of Georgia, Athens, GA, USA. Electronic address: wenxuan@uga.edu., Yuan GC; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: guo-cheng.yuan@mssm.edu., Ma P; Department of Statistics, University of Georgia, Athens, GA, USA. Electronic address: pingma@uga.edu.
Πηγή: Cell reports methods [Cell Rep Methods] 2026 Sep 21; Vol. 6 (9), pp. 101530. Date of Electronic Publication: 2026 Jul 17.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Inc Country of Publication: United States NLM ID: 9918227360606676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2667-2375 (Electronic) Linking ISSN: 26672375 NLM ISO Abbreviation: Cell Rep Methods Subsets: MEDLINE
Imprint Name(s): Original Publication: [New York] : Elsevier Inc., [2021]-
Ιατρικοί όροι (MeSH): Spatial Transcriptomics*/methods , Gene Expression Profiling*/methods , Transcriptome*/genetics, Colorectal Neoplasms/genetics ; Animals ; Humans ; Mice ; Algorithms
Περίληψη: Spot-swapping in sequence-based spatial transcriptomics redistributes RNA molecules across neighboring spots, distorting spatial gene expression and biasing downstream analysis. We introduce SpaDiff, a mass-conserving denoising framework that corrects this artifact through score-guided reverse diffusion. Across simulated and real spatial transcriptomics datasets, SpaDiff consistently improves the recovery of spatial gene-expression distributions. In particle-level simulations with known ground truth, SpaDiff robustly restores gene-level spatial patterns across diverse contamination regimes. In real tissues, SpaDiff relocalizes misplaced transcripts in human-mouse chimeric samples, improves spatial-domain recovery in human dorsolateral prefrontal cortex, and preserves local tumor heterogeneity while sharpening regional structure in colorectal cancer. These results show that SpaDiff improves the spatial accuracy of gene expression and supports more accurate downstream analyses such as clustering and spatial domain identification.
(Copyright © 2026 The Author(s). Published by Elsevier Inc. All rights reserved.)
Competing Interests: Declaration of interests The authors declare no competing interests.
Σχόλια: Update of: bioRxiv. 2025 Oct 08:2025.10.07.681011. doi: 10.1101/2025.10.07.681011.. (PMID: 41280001)
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Grant Information: R01 GM152814 United States GM NIGMS NIH HHS; RF1 MH133703 United States MH NIMH NIH HHS
Contributed Indexing: Keywords: CP: computational biology; CP: systems biology; data denoising; diffusion process; score-based diffusion; sequence-based spatial transcriptomics; spatial transcriptomics; spot swapping
Entry Date(s): Date Created: 20260717 Date Completed: 20260921 Latest Revision: 20260929
Update Code: 20260929
PubMed Central ID: PMC13615491
DOI: 10.1016/j.crmeth.2026.101530
PMID: 42468536
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:2667-2375
DOI:10.1016/j.crmeth.2026.101530