HTRPCA: Hypergraph Regularized Tensor Robust Principal Component Analysis for Sample Clustering in Tumor Omics Data

In recent years, clustering analysis of cancer genomics data has gained widespread attention. However, limited by the dimensions of the matrix, the traditional methods cannot fully mine the underlying geometric structure information in the data. Besides, noise and outliers inevitably exist in the data. To solve the above two problems, we come up with a new method which uses tensor to represent cancer omics data and applies hypergraph to save the geometric structure information in original data. This model is called hypergraph regularized tensor robust principal component analysis (HTRPCA). The data processed by HTRPCA becomes two parts, one of which is a low-rank component that contains pure underlying structure information between samples, and the other is some sparse interference points. So we can use the low-rank component for clustering. This model can retain complex geometric information between more sample points due to the addition of the hypergraph regularization. Through clustering, we can demonstrate the effectiveness of HTRPCA, and the experimental results on TCGA datasets demonstrate that HTRPCA precedes other advanced methods. This paper proposes a new method of using tensors to represent cancer omics data and introduces hypergraph items to save the geometric structure information of the original data. At the same time, the model decomposes the original tensor into low-order tensors and sparse tensors. The low-rank tensor was used to cluster cancer samples to verify the effectiveness of the method.