Background: Non-negative Matrix Factorization (NMF) has been extensively used in gene expression data. However, most NMF-based methods have single-layer structures, which may achieve poor performance for complex data. Deep learning, with its carefully designed hierarchical structure, has shown significant advantages in learning data features.

Objective: In bioinformatics, on the one hand, to discover differentially expressed genes in gene expression data; on the other hand, to obtain higher sample clustering results. It can provide the reference value for the prevention and treatment of cancer.

Method: In this paper, we apply a deep NMF method called Deep Semi-NMF on the integrated gene expression data. In each layer, the coefficient matrix is directly decomposed into the basic and coefficient matrix of the next layer. We apply this factorization model on The Cancer Genome Atlas (TCGA) genomic data.

Results: The experimental results demonstrate the superiority of Deep Semi-NMF method in identifying differentially expressed genes and clustering samples.

Conclusion: The Deep Semi-NMF model decomposes a matrix into multiple matrices and multiplies them to form a matrix. It can also improve the clustering performance of samples while digging out more accurate key genes for disease treatment.

背景：非负矩阵分解（NMF）已广泛用于基因表达数据中。但是，大多数基于NMF的方法都具有单层结构，这对于复杂数据可能会实现较差的性能。深度学习及其精心设计的层次结构在学习数据功能方面显示出显着的优势。

目的：一方面在生物信息学中发现基因表达数据中差异表达的基因。另一方面，以获得更高的样本聚类结果。它可以为癌症的预防和治疗提供参考价值。

方法：在本文中，我们对整合的基因表达数据应用了一种称为Deep Deep-NMF的深度NMF方法。在每一层中，系数矩阵直接分解为下一层的基本矩阵和系数矩阵。我们将这种分解模型应用于癌症基因组图谱（TCGA）基因组数据。

结果：实验结果证明了Deep Semi-NMF方法在鉴定差异表达基因和聚类样品中的优越性。

结论：Deep Semi-NMF模型将矩阵分解为多个矩阵，并将它们相乘以形成矩阵。它还可以改善样本的聚类性能，同时挖掘出更准确的疾病治疗关键基因。