GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA.
GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA.
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DOI：
10.3934/fods.2022013
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发表时间：
2022-02
影响因子：
2.3
通讯作者：
Duan Chen;Shaoyu Li;Xue Wang
中科院分区：
文献类型：
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作者：
Duan Chen;Shaoyu Li;Xue Wang
对大量RNA-seq数据进行完整的反褶积分析很重要，有助于区分患者和正常对照组织中与疾病相关的GEPs（基因表达谱）的差异是由于组织样本细胞组成的变化，还是由于特定细胞中GEPs的变化。非负矩阵分解（NMF）是实现完全反卷积的主要技术之一，在机器学习领域也有广泛的应用。然而，NMF是一个众所周知的强不适定问题，因此将NMF直接应用于RNA-seq数据将在解决方案的可解释性方面遇到严重困难。在本文中，我们开发了一个基于nmf的数学模型和相应的计算算法，以提高解卷积大量RNA-seq数据的解可识别性。在我们的方法中，我们将标记基因的生物学概念与NMF理论的可解性条件相结合，并建立了一个几何结构指导的优化模型。在该策略中，首先利用光谱聚类技术探索体组织数据的几何结构。然后，将标记基因的识别信息作为可解性约束进行整合，将总体相关图作为流形正则化。利用合成数据和生物数据验证了所提出的模型和算法，从而显著提高了解的可解释性和准确性。
Complete deconvolution analysis for bulk RNA-seq data is important and helpful to distinguish whether the differences of disease-associated GEPs (gene expression profiles) in tissues of patients and normal controls are due to changes in cellular composition of tissue samples, or due to GEPs changes in specific cells. One of the major techniques to perform complete deconvolution is nonnegative matrix factorization (NMF), which also has a wide-range of applications in the machine learning community. However, the NMF is a well-known strongly ill-posed problem, so a direct application of NMF to RNA-seq data will suffer severe difficulties in the interpretability of solutions. In this paper, we develop an NMF-based mathematical model and corresponding computational algorithms to improve the solution identifiability of deconvoluting bulk RNA-seq data. In our approach, we combine the biological concept of marker genes with the solvability conditions of the NMF theories, and develop a geometric structures guided optimization model. In this strategy, the geometric structure of bulk tissue data is first explored by the spectral clustering technique. Then, the identified information of marker genes is integrated as solvability constraints, while the overall correlation graph is used as manifold regularization. Both synthetic and biological data are used to validate the proposed model and algorithms, from which solution interpretability and accuracy are significantly improved.