基于比例风险的低秩重组非负矩阵分解揭示生存相关基因簇
Low-Rank Reorganization via Proportional Hazards Non-negative Matrix Factorization Unveils Survival Associated Gene Clusters
摘要：
精准健康的核心目标之一是理解和解释高维生物学数据，以识别与疾病发生、发展和结局相关的基因和标志物。虽然已经致力于利用基因表达数据进行多重分析，同时通过包括生存时间来解释时间-事件建模，但许多传统分析分别关注基因表达数据矩阵的非负矩阵因子分解（NMF）和Cox比例风险模型的生存回归。在这项工作中，Cox比例风险回归与NMF通过施加生存约束。这是通过联合优化Frobenius范数和事件（如死亡或复发）的部分对数似然来实现的。仿真结果表明，该方法的优越性，与其他算法相比，在寻找生存相关的基因簇。此外，使用人类癌症基因表达数据，所提出的技术可以解开癌症基因的关键簇。发现的基因簇反映了丰富的生物学意义，可以帮助识别与生存相关的生物标志物。为了实现精确的健康和癌症治疗的目标，所提出的算法可以帮助理解和解释高维异质基因组数据，准确识别生存相关基因簇。
英文摘要：
One of the central goals in precision health is the understanding and interpretation of high-dimensional biological data to identify genes and markers associated with disease initiation, development, and outcomes. Though significant effort has been committed to harness gene expression data for multiple analyses while accounting for time-to-event modeling by including survival times, many traditional analyses have focused separately on non-negative matrix factorization (NMF) of the gene expression data matrix and survival regression with Cox proportional hazards model. In this work, Cox proportional hazards regression is integrated with NMF by imposing survival constraints. This is accomplished by jointly optimizing the Frobenius norm and partial log likelihood for events such as death or relapse. Simulation results on synthetic data demonstrated the superiority of the proposed method, when compared to other algorithms, in finding survival associated gene clusters. In addition, using human cancer gene expression data, the proposed technique can unravel critical clusters of cancer genes. The discovered gene clusters reflect rich biological implications and can help identify survival-related biomarkers. Towards the goal of precision health and cancer treatments, the proposed algorithm can help understand and interpret high-dimensional heterogeneous genomics data with accurate identification of survival-associated gene clusters.
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