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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-06-01
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| Series: | Genome Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13059-025-03601-x |
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| Summary: | 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. |
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| ISSN: | 1474-760X |