bioRxiv · 10.1101/2024.07.01.599554
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 (NSF) 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 mNSFs efficacy in identifying true factors, shared anatomical regions, and region-specific biological functions. mNSFs 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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Wang, Y., Woyshner, K., Sriworarat, C., Stein-O'Brien, G., Goff, L. A., Hansen, K. D.. 2024-07-01. Multi-sample non-negative spatial factorization. https://doi.org/10.1101/2024.07.01.599554
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