A global [Formula: see text] gene co-expression network constructed from hundreds of experimental conditions with missing values.
A global [Formula: see text] gene co-expression network constructed from hundreds of experimental conditions with missing values.
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DOI：
10.1186/s12859-022-04697-9
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发表时间：
2022-05-09
影响因子：
3
通讯作者：
Scoglio, Caterina
中科院分区：
文献类型：
--
作者：
Kuang, Junyao;Buchon, Nicolas;Michel, Kristin;Scoglio, Caterina
基因共表达网络（GCNs）可用于确定基因调控，并将基因功能归因于生物过程。不同的高通量技术，包括单通道和双通道微阵列以及RNA测序，能够同时评估数千个基因表达数据，但这些方法所提供的结果无法直接比较。因此，分析基因之间的共表达关系是复杂的，尤其是当由于实验原因存在缺失值时。网络是研究基因共表达的有用工具，其中节点代表基因，边代表基因对的共表达。
在本文中，我们建立了一种方法，用于从257项采用不同方法和实验设计的独特研究中构建冈比亚按蚊转录组的基因共表达网络。我们引入滑动阈值方法来选择具有高皮尔逊相关系数的节点对。所得到的网络，我们将其命名为AgGCN1.0，对随机去除条件具有稳健性，并且具有与小世界和无标度网络相似的特征。对网络子图的分析表明，核心主要由编码线粒体呼吸链和核糖体成分的基因组成，而不同的群落则富含参与不同生物过程的基因。
对网络的分析表明，核心子网络的结构和网络群落均基于基因功能，这支持了所提出的GCN构建方法的有效性。网络科学方法的应用表明，整体网络结构旨在最大程度地整合基本细胞功能，可能允许灵活添加新功能。
Gene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze co-expression relations between genes, especially when there are missing values arising for experimental reasons. Networks are a helpful tool for studying gene co-expression, where nodes represent genes and edges represent co-expression of pairs of genes. In this paper, we establish a method for constructing a gene co-expression network for the Anopheles gambiae transcriptome from 257 unique studies obtained with different methodologies and experimental designs. We introduce the sliding threshold approach to select node pairs with high Pearson correlation coefficients. The resulting network, which we name AgGCN1.0, is robust to random removal of conditions and has similar characteristics to small-world and scale-free networks. Analysis of network sub-graphs revealed that the core is largely comprised of genes that encode components of the mitochondrial respiratory chain and the ribosome, while different communities are enriched for genes involved in distinct biological processes. Analysis of the network reveals that both the architecture of the core sub-network and the network communities are based on gene function, supporting the power of the proposed method for GCN construction. Application of network science methodology reveals that the overall network structure is driven to maximize the integration of essential cellular functions, possibly allowing the flexibility to add novel functions.
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作者：
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