Advancement in next-generation sequencing, transcriptomics, proteomics and other high-throughput technologies has enabled simultaneous measurement of multiple types of genomic data for cancer samples. These data together may reveal new biological insights as compared to analyzing one single genome type data. This study proposes a novel use of supervised dimension reduction method, called sliced inverse regression, to multi-omics data analysis to improve prediction over a single data type analysis. The study further proposes an integrative sliced inverse regression method (integrative SIR) for simultaneous analysis of multiple omics data types of cancer samples, including MiRNA, MRNA and proteomics, to achieve integrative dimension reduction and to further improve prediction performance. Numerical results show that integrative analysis of multi-omics data is beneficial as compared to single data source analysis, and more importantly, that supervised dimension reduction methods possess advantages in integrative data analysis in terms of classification and prediction as compared to unsupervised dimension reduction methods.

中文翻译：

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切片逆回归，用于集成多组学数据分析。

下一代测序，转录组学，蛋白质组学和其他高通量技术的进步使得能够同时测量癌症样本的多种类型的基因组数据。与分析一个单一的基因组类型数据相比，这些数据可能会揭示出新的生物学见解。这项研究提出了一种新的有监督的降维方法，称为切片逆回归，用于多组学数据分析，以改善对单个数据类型分析的预测。这项研究进一步提出了一种集成切片反演回归方法（integrated SIR），用于同时分析癌症样本的多种组学数据类型，包括MiRNA，MRNA和蛋白质组学，以实现集成化尺寸缩减并进一步提高预测性能。