VarNMF: non-negative probabilistic factorization with source variation Article Swipe
E. Fallik
,
Nir Friedman
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae758
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btae758
Motivation Non-negative matrix factorization (NMF) is a powerful tool often applied to genomic data to identify non-negative latent components that constitute linearly mixed samples. It is useful when the observed signal combines contributions from multiple sources, such as cell types in bulk measurements of heterogeneous tissue. NMF accounts for two types of variation between samples — disparities in the proportions of sources and observation noise. However, in many settings, there is also a non-trivial variation between samples in the contribution of each source to the mixed data. This variation cannot be accurately modeled using the NMF framework. Results We present VarNMF, a probabilistic extension of NMF that explicitly models this variation in source values. We show that by modeling sources as non-negative distributions, we can recover source variation directly from mixed samples without observing any of the sources directly. We apply VarNMF to a cell-free ChIP-seq dataset of two cancer cohorts and a healthy cohort, demonstrating that VarNMF provides a better estimation of the data distribution. Moreover, VarNMF extracts cancer-associated source distributions that decouple the tumor characteristics from the amount of tumor contribution, and identify patient-specific disease behaviors. This decomposition highlights the inter-tumor variability that is obscured in the mixed samples. Availability and implementation Code is available at https://github.com/Nir-Friedman-Lab/VarNMF.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btae758
- https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae758/61290454/btae758.pdf
- OA Status
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- Cited By
- 1
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4405862633Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btae758Digital Object Identifier
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VarNMF: non-negative probabilistic factorization with source variationWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-26Full publication date if available
- Authors
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E. Fallik, Nir FriedmanList of authors in order
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https://doi.org/10.1093/bioinformatics/btae758Publisher landing page
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goldOpen access status per OpenAlex
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10Other works algorithmically related by OpenAlex
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