mNSF: multi-sample non-negative spatial factorization
Abstract 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-s...
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| Format: | Article |
| Language: | English |
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BMC
2025-06-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-025-03601-x |
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| author | Yi Wang Kyla Woyshner Chaichontat Sriworarat Genevieve Stein-O’Brien Loyal A. Goff Kasper D. Hansen |
| author_facet | Yi Wang Kyla Woyshner Chaichontat Sriworarat Genevieve Stein-O’Brien Loyal A. Goff Kasper D. Hansen |
| author_sort | Yi Wang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-33e21c8feecc4440b73e77c44a532fba |
| institution | OA Journals |
| issn | 1474-760X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-33e21c8feecc4440b73e77c44a532fba2025-08-20T02:30:41ZengBMCGenome Biology1474-760X2025-06-0126112810.1186/s13059-025-03601-xmNSF: multi-sample non-negative spatial factorizationYi Wang0Kyla Woyshner1Chaichontat Sriworarat2Genevieve Stein-O’Brien3Loyal A. Goff4Kasper D. Hansen5Department of Biostatistics, Johns Hopkins Bloomberg School of Public HealthDepartment of Genetic Medicine, Johns Hopkins School of MedicineDepartment of Neuroscience, Johns Hopkins School of MedicineDepartment of Genetic Medicine, Johns Hopkins School of MedicineDepartment of Genetic Medicine, Johns Hopkins School of MedicineDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthAbstract 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.https://doi.org/10.1186/s13059-025-03601-xSpatial transcriptomicsMatrix factorizationMulti-sample analysisDimensionality reductionSpatial gene expression |
| spellingShingle | Yi Wang Kyla Woyshner Chaichontat Sriworarat Genevieve Stein-O’Brien Loyal A. Goff Kasper D. Hansen mNSF: multi-sample non-negative spatial factorization Genome Biology Spatial transcriptomics Matrix factorization Multi-sample analysis Dimensionality reduction Spatial gene expression |
| title | mNSF: multi-sample non-negative spatial factorization |
| title_full | mNSF: multi-sample non-negative spatial factorization |
| title_fullStr | mNSF: multi-sample non-negative spatial factorization |
| title_full_unstemmed | mNSF: multi-sample non-negative spatial factorization |
| title_short | mNSF: multi-sample non-negative spatial factorization |
| title_sort | mnsf multi sample non negative spatial factorization |
| topic | Spatial transcriptomics Matrix factorization Multi-sample analysis Dimensionality reduction Spatial gene expression |
| url | https://doi.org/10.1186/s13059-025-03601-x |
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