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Bayesian multi-study non-negative matrix factorization for mutational signatures
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DOI：10.1186/s13059-025-03563-0.png)
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Abstract
En 中文
Mutational signatures are typically identified from tumor genome sequencing data using non-negative matrix factorization (NMF). However, existing NMF techniques only decompose a single dataset, limiting rigorous comparisons of signatures across conditions. We propose a Bayesian NMF method that jointly decomposes multiple datasets to identify signatures and their sharing pattern across conditions. We propose a fully unsupervised discovery-only model and a semi-supervised recovery-discovery model that simultaneously estimates known and novel signatures, and extend both to estimate covariate effects. We demonstrate our approach on extensive simulations, and apply our method to answer questions related to colorectal cancer and early-onset breast cancer.
Keywords:
Mutational signatures
Non-negative matrix factorization
Dimension reduction
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