Differentially expressed genes selection becomes a hotspot and difficulty in recent molecular biology. Low-rank representation (LRR) uniting graph Laplacian regularization has gained good achievement in the above field. However, the co-expression information of data cannot be captured well by graph regularization. Therefore, a novel low-rank representation method regularized by dual-hypergraph Laplacian is proposed to reveal the intrinsic geometrical structures hidden in the samples and genes direction simultaneously, which is called dual-hypergraph Laplacian regularized LRR (DHLRR). Finally, a low-rank matrix and a sparse perturbation matrix can be recovered from genomic data by DHLRR. Based on the sparsity of differentially expressed genes, the sparse disturbance matrix can be applied to extracting differentially expressed genes. In our experiments, two gene analysis tools are used to discuss the experimental results. The results on two real genomic data and an integrated dataset prove that DHLRR is efficient and effective in finding differentially expressed genes.

中文翻译：

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通过双Hypergraph Laplacian进行正则化的低秩表示方法，用于选择差异表达的基因。

差异表达基因的选择成为近来分子生物学研究的热点和难点。在上述领域中，低秩表示（LRR）联合图Laplacian正则化取得了良好的成就。但是，通过图正则化不能很好地捕获数据的共表达信息。因此，提出了一种通过双超图拉普拉斯正则化正则化的新低秩表示方法来揭示样本和基因方向同时隐藏的内在几何结构，称为双超拉普拉斯正则化LRR（DHLRR）。最后，DHLRR可以从基因组数据中恢复低秩矩阵和稀疏扰动矩阵。基于差异表达基因的稀疏性，稀疏扰动矩阵可用于提取差异表达基因。在我们的实验中，使用了两种基因分析工具来讨论实验结果。在两个真实的基因组数据和一个集成的数据集上的结果证明DHLRR在寻找差异表达的基因方面非常有效。