Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.).
Statistical Approaches for Gene Selection, Hub Gene Identification and Module Interaction in Gene Co-Expression Network Analysis: An Application to Aluminum Stress in Soybean (Glycine max L.).
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DOI：
10.1371/journal.pone.0169605
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Mandal BN
中科院分区：
文献类型：
--
作者：
Das S;Meher PK;Rai A;Bhar LM;Mandal BN
信息基因的筛选是基因表达研究中的一个重要问题。小样本量和基因表达数据中的大量基因使得选择过程复杂。此外，所选择的信息基因可以作为基因共表达网络分析的重要输入。此外，在基因共表达网络中的枢纽基因和模块相互作用的识别还有待充分探索。本文提出了一种基于支持向量机算法的统计可靠的基因选择技术，用于从高维基因表达数据中选择信息基因。此外，已尝试开发一种统计方法来识别基因共表达网络中的枢纽基因。此外，还开发了一种差异枢纽基因分析方法，根据病例与对照研究中的基因连接性将已鉴定的枢纽基因分为不同的组。基于所提出的方法，一个R包，即，dhga（https：//cran.r-project.org/web/packages/dhga）已经开发完成。在三个不同的作物微阵列数据集上评估了所提出的基因选择技术以及枢纽基因识别方法的比较性能。建议的基因选择技术优于大多数现有的技术选择强大的信息基因集。与现有方法相比，该方法识别出的枢纽基因数目较少，符合真实的网络无标度特性的原理.本研究报道了大豆铝毒响应基因工程中的关键基因沿着及其拟南芥同源基因。对筛选出的关键基因进行功能分析，揭示了大豆铝毒胁迫反应的分子机制。
Selection of informative genes is an important problem in gene expression studies. The small sample size and the large number of genes in gene expression data make the selection process complex. Further, the selected informative genes may act as a vital input for gene co-expression network analysis. Moreover, the identification of hub genes and module interactions in gene co-expression networks is yet to be fully explored. This paper presents a statistically sound gene selection technique based on support vector machine algorithm for selecting informative genes from high dimensional gene expression data. Also, an attempt has been made to develop a statistical approach for identification of hub genes in the gene co-expression network. Besides, a differential hub gene analysis approach has also been developed to group the identified hub genes into various groups based on their gene connectivity in a case vs. control study. Based on this proposed approach, an R package, i.e., dhga (https://cran.r-project.org/web/packages/dhga) has been developed. The comparative performance of the proposed gene selection technique as well as hub gene identification approach was evaluated on three different crop microarray datasets. The proposed gene selection technique outperformed most of the existing techniques for selecting robust set of informative genes. Based on the proposed hub gene identification approach, a few number of hub genes were identified as compared to the existing approach, which is in accordance with the principle of scale free property of real networks. In this study, some key genes along with their Arabidopsis orthologs has been reported, which can be used for Aluminum toxic stress response engineering in soybean. The functional analysis of various selected key genes revealed the underlying molecular mechanisms of Aluminum toxic stress response in soybean.
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