Ma Xin, Xiao Luo, Wong Wing Hung
Departments of Statistics and.
Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205.
Proc Natl Acad Sci U S A. 2014 Nov 4;111(44):15675-80. doi: 10.1073/pnas.1417808111. Epub 2014 Oct 20.
We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is used to analyze microRNA (miRNA) and long noncoding RNA (lncRNA) data from The Cancer Genome Atlas (TCGA) consortium. The analysis yields previously unidentified insights into the combinatorial regulation of gene expression by noncoding RNAs, as well as findings that are supported by evidence from the literature.
我们构建了一个用于多个调控程序对全局基因表达进行调控的统计模型，并提出了一种阈值奇异值分解（T-SVD）回归方法，用于从数据中学习这样的模型。大量模拟表明，与其他竞争方法相比，该方法具有更高的计算速度、灵敏度和特异性。该方法被用于分析来自癌症基因组图谱（TCGA）联盟的微小RNA（miRNA）和长链非编码RNA（lncRNA）数据。分析得出了关于非编码RNA对基因表达的组合调控的前所未有的见解，以及得到文献证据支持的研究结果。
