主要的信息：

1、肿瘤相对于正常组织发生显著差异表达的基因被称为特征基因（gene signatures），这些基因的差异变化反映的是肿瘤异常调控网络的混合作用，因此需要将差异表达基因聚类后再寻找每个聚团对应的调控因子，如本文研究的miRNA；

2、一般的聚类方法直接取统一阈值来识别基因共表达聚团，但不同的调控因子可能在不同的调控尺度（共表达程度）上对靶基因进行调控，因此我们提出在层级聚类产生的多尺度共表达特征基因集中分析调控因子靶基因富集度分析的方法Gene Set Analysis in Hierarchical Gene Co-Expression Signatures

讨论：

复杂系统通常存在层级化的结构，如何在分析模型中有效的挖掘并利用层级化的思想是非常有意思的问题。本文中提出的方法仍比较粗糙，有很大的改进空间。

Jin Gu#, Zhenyu Xuan. Inferring the perturbed microRNA regulatory networks in cancer using hierarchical gene co-expression signatures. PLoS ONE 2013, 8(11):e81032

MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks. However, due to the complexity of cancer transcriptome, current methods often encounter low sensitivity and report few onco-miR candidates. Here, we developed a new method, named miRHiC (enrichment analysis of miRNA targets in Hierarchical gene Co-expression signatures), to infer the perturbed miRNA regulatory networks by using the hierarchical co-expression signatures in large-scale cancer gene expression datasets. The method can infer onco-miR candidates and their target networks which are only linked to sub-clusters of the differentially expressed genes at fine scales of the co-expression hierarchy. On two real datasets of lung cancer and hepatocellular cancer, miRHiC uncovered several known onco-miRs and their target genes (such as miR-26, miR-29, miR-124, miR-125 and miR-200) and also identified many new candidates (such as miR-149, which is inferred in both types of cancers). Using hierarchical gene co-expression signatures, miRHiC can greatly increase the sensitivity for inferring the perturbed miRNA regulatory networks in cancer. All Perl scripts of miRHiC and the detailed documents are freely available on the web at http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/.