Nonnegative spatial factorization applied to spatial genomics. Townes, F. W. & Engelhardt, B. E. Nature Methods, December, 2022. Publisher: Nature Publishing Group![Nonnegative spatial factorization applied to spatial genomics [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 2 downloads Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporate known structure between observations. Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model based on transformed Gaussian processes that naturally encourages sparsity and scales to tens of thousands of observations. NSF recovers ground truth factors more accurately than real-valued alternatives such as MEFISTO in simulations, and has lower out-of-sample prediction error than probabilistic NMF on three spatial transcriptomics datasets from mouse brain and liver. Since not all patterns of gene expression have spatial correlations, we also propose a hybrid extension of NSF that combines spatial and nonspatial components, enabling quantification of spatial importance for both observations and features. A TensorFlow implementation of NSF is available from https://github.com/willtownes/nsf-paper.
@article{townes_nonnegative_2022,
title = {Nonnegative spatial factorization applied to spatial genomics},
copyright = {2022 The Author(s)},
issn = {1548-7105},
url = {https://www.nature.com/articles/s41592-022-01687-w},
doi = {10.1038/s41592-022-01687-w},
abstract = {Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporate known structure between observations. Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model based on transformed Gaussian processes that naturally encourages sparsity and scales to tens of thousands of observations. NSF recovers ground truth factors more accurately than real-valued alternatives such as MEFISTO in simulations, and has lower out-of-sample prediction error than probabilistic NMF on three spatial transcriptomics datasets from mouse brain and liver. Since not all patterns of gene expression have spatial correlations, we also propose a hybrid extension of NSF that combines spatial and nonspatial components, enabling quantification of spatial importance for both observations and features. A TensorFlow implementation of NSF is available from https://github.com/willtownes/nsf-paper.},
language = {en},
urldate = {2022-12-31},
journal = {Nature Methods},
author = {Townes, F. William and Engelhardt, Barbara E.},
month = dec,
year = {2022},
note = {Publisher: Nature Publishing Group},
keywords = {Gene expression analysis, Machine learning, Software, Statistical methods, Transcriptomics},
pages = {1--10},
}
Downloads: 2
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