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mNSF: multi-sample non-negative spatial factorization

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Publicado en:Genome Biology vol. 26 (2025), p. 1
Autor principal: Wang, Yi
Otros Autores: Woyshner, Kyla, Sriworarat, Chaichontat, Genevieve Stein-O’Brien, Goff, Loyal A, Hansen, Kasper D
Publicado:
Springer Nature B.V.
Materias:
Data processing
Software
Gene expression
Transcriptomics
Genomics
Spatial data
Discriminant analysis
Data analysis
Acceso en línea:Citation/Abstract
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Resumen:Analyzing multi-sample spatial transcriptomics data requires accounting for biological variation. We present multi-sample non-negative spatial factorization (mNSF), an alignment-free framework extending single-sample spatial factorization to multi-sample datasets. mNSF incorporates sample-specific spatial correlation modeling and extracts low-dimensional data representations. Through simulations and real data analysis, we demonstrate mNSF’s efficacy in identifying true factors, shared anatomical regions, and region-specific biological functions. mNSF’s performance is comparable to alignment-based methods when alignment is feasible, while enabling analysis in scenarios where spatial alignment is unfeasible. mNSF shows promise as a robust method for analyzing spatially resolved transcriptomics data across multiple samples.
ISSN:1474-7596
1474-760X
1465-6906
DOI:10.1186/s13059-025-03601-x
Fuente:Health & Medical Collection