Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number of experimental samples is small and the number of genes is huge. We focus on the prediction of the epithelial-mesenchymal transition (EMT), which is an underlying mechanism of tumor metastasis, here, rather than tumor metastasis itself, to avoid confounding effects of uncertainties derived from various factors. In this paper, we propose a novel model in predicting EMT based on multidimensional scaling (MDS) strategies and integrating entropy and random matrix detection strategies to determine the optimal reduced number of dimension in low dimensional space. We verified our proposed model with the gene expression data for EMT samples of breast cancer and the experimental results demonstrated the superiority over state-of-the-art clustering methods. Furthermore, we developed a novel feature extraction method for selecting the significant genes and predicting the tumor metastasis. The source code is available at “ https://github.com/yushanqiu/yushan.qiu-szu.edu.cn ”.

中文翻译：

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预测上皮-间质转化的多维尺度无监督学习框架

从全基因组水平的基因表达数据中对肿瘤转移样本进行聚类仍然是一项艰巨的挑战，尤其是在实验样本数量少而基因数量巨大的情况下。我们专注于预测上皮 - 间质转化（EMT），这是肿瘤转移的潜在机制，而不是肿瘤转移本身，以避免来自各种因素的不确定性的混杂影响。在本文中，我们提出了一种基于多维缩放（MDS）策略的 EMT 预测模型，并结合熵和随机矩阵检测策略来确定低维空间中的最佳降维数。我们用乳腺癌 EMT 样本的基因表达数据验证了我们提出的模型，实验结果证明了其优于最先进的聚类方法。此外，我们开发了一种新的特征提取方法，用于选择重要基因和预测肿瘤转移。源代码可在“ https://github.com/yushanqiu/yushan.qiu-szu.edu.cn ”。