非负矩阵分解的深度展开及其在突变特征分析中的应用
deep unfolding for non-negative matrix factorization with application to mutational signature analysis
摘要：
非负矩阵分解（NMF）是一种基本的矩阵分解技术，主要用于降维，在生物领域越来越受欢迎。虽然找到唯一的NMF通常是不可能的，但存在收敛到局部最优解的用于NMF优化的各种迭代算法。这样的技术也可以作为深度学习方法的起点，将算法迭代展开到深度网络的层中。在这里，我们为NMF开发了展开的深度网络，并在监督和无监督环境下开发了几个正则化变体。我们将我们的方法应用于各种突变数据集，以重建其潜在的突变签名及其暴露。我们证明了我们的方法在分析模拟和真实突变数据中比标准制剂的准确性提高。
英文摘要：
Non-negative matrix factorization (NMF) is a fundamental matrix decomposition technique that is used primarily for dimensionality reduction and is increasing in popularity in the biological domain. Although finding a unique NMF is generally not possible, there are various iterative algorithms for NMF optimization that converge to locally optimal solutions. Such techniques can also serve as a starting point for deep learning methods that unroll the algorithmic iterations into layers of a deep network. Here we develop unfolded deep networks for NMF and several regularized variants in both a supervised and an unsupervised setting. We apply our method to various mutation data sets to reconstruct their underlying mutational signatures and their exposures. We demonstrate the increased accuracy of our approach over standard formulations in analyzing simulated and real mutation data.
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