School of Computer Science and Technology, Xidian University, Xi'an, China.
School of Computer Science and Technology, Xidian University, Xi'an, China.
Methods. 2021 Aug;192:67-76. doi: 10.1016/j.ymeth.2020.08.001. Epub 2020 Aug 14.
Integrative analysis of multiple omics offers the opportunity to uncover coordinated cellular processes acting across different omics layers. The ever-increasing of multi-omics data provides us a comprehensive insight into cancer subtyping. Many multi-omics integrative methods have been developed, but few of them can deal with partial datasets in which some samples only have data for a subset of the omics. In this study, we propose a partial multi-omics integrative method, MSNE (Multiple Similarity Network Embedding), for cancer subtyping. MSNE integrates the multi-omics information by embedding the neighbor relations of samples defined by the random walk on multiple similarity networks. We compared MSNE with five existing multi-omics integrative methods on twelve datasets in both full and partial scenarios. MSNE achieved the best result on pan-cancer and image datasets. Furthermore, on ten cancer subtyping datasets, MSNE got the most enriched clinical parameters and comparable log-rank test P-values in survival analysis. In conclusion, MSNE is an effective and efficient integrative method for multi-omics data and, especially, has a strong power on partial datasets.
多组学综合分析提供了揭示跨不同组学层面协同细胞过程的机会。越来越多的多组学数据为我们深入了解癌症亚型提供了全面的视角。已经开发了许多多组学综合方法，但其中很少有方法能够处理部分数据集，即一些样本仅具有部分组学数据。在这项研究中，我们提出了一种部分多组学综合方法 MSNE（多相似网络嵌入），用于癌症亚型分析。MSNE 通过在多个相似网络上的随机游走定义的样本邻接关系来整合多组学信息。我们在完整和部分场景下，将 MSNE 与五种现有的多组学综合方法在 12 个数据集上进行了比较。MSNE 在泛癌和图像数据集上取得了最好的结果。此外，在十个癌症亚型数据集上，MSNE 在生存分析中获得了最丰富的临床参数和可比的对数秩检验 P 值。总之，MSNE 是一种有效的多组学数据综合方法，特别是在部分数据集上具有强大的功能。
