A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules.
A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules.
复制标题
DOI：
10.1093/bioinformatics/btr206
复制
发表时间：
2011-07-01
期刊：
影响因子：
--
通讯作者：
Zhou XJ
中科院分区：
文献类型：
--
作者：
Zhang S;Li Q;Liu J;Zhou XJ
研究动机：众所周知，microRNAs （miRNAs）与基因协同工作，构成基因调控网络的关键部分。然而，大多数mirna的具体功能作用及其在细胞过程中的组合作用仍不清楚。多种功能基因组数据的可用性为研究mirna基因调控提供了前所未有的机会。一个主要的挑战是如何整合不同的基因组数据来识别mirna和基因的调控模块。结果：本文提出了一个有效的数据整合框架来识别mirna基因调控模块。miRNA和基因表达谱在多个非负矩阵分解框架中进行联合分析，并以正则化方式同时集成额外的网络数据。同时，我们对变量采用稀疏性惩罚来实现模块化解决方案。该数学公式可通过迭代乘法更新算法有效求解。我们应用该方法整合了一组异构数据源，包括385例人类卵巢癌样本中mirna和基因的表达谱，计算预测了mirna -基因相互作用和基因-基因相互作用。我们证明69%的调控模块中的mirna和基因显著相关。此外，这些组件分别在miRNA簇、GO生物过程和KEGG途径等已知功能集中显著富集。此外，这些小模块中的许多mirna和基因与包括卵巢癌在内的各种癌症有关。最后，我们表明模块可以将患者（样本）分层为具有显著临床特征的组。可获得性：课程和补充材料可在http://zhoulab.usc.edu/SNMNMF/上获得。联系:xjzhou@usc.edu;zsh@amss.ac.cn补充信息：补充数据可在Bioinformatics网站在线获得。
Motivation: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA–gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes. Results: Here we propose an effective data integration framework to identify the miRNA–gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA–gene interactions, and gene–gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics. Availability: The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/. Contact: xjzhou@usc.edu; zsh@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.
登录
查看更多内容
影响因子：
5.8
作者：
Joung, Je-Gun;Hwang, Kyu-Baek;Zhang, Byoung-Tak
通讯作者：
Zhang, Byoung-Tak
影响因子：
5.8
作者：
Kim, Hyunsoo;Park, Haesun
通讯作者：
Park, Haesun
影响因子：
30.8
作者：
Bentwich, I;Avniel, A;Bentwich, Z
通讯作者：
Bentwich, Z
影响因子：
64.8
作者：
Hartwell, LH;Hopfield, JJ;Murray, AW
通讯作者：
Murray, AW
影响因子：
1.8
作者：
Berry, Michael W.;Browne, Murray;Plemmons, Robert J.
通讯作者：
Plemmons, Robert J.