The development of this manuscript was supported by funds from National Natural Science Foundation of China-Guizhou Provincial People&#39;s Government Karst Science Research Center Project (U1812401), Doctoral Research Project of Guizhou Normal University (GZNUD[2017]1), Science and Technology Support Project of Guizhou Province (QKHZC[2021]YB459) and the Science and Technology Project of the Guiyang([2019]2-8).
The authors would like to thank Edwards J.A et al for providing rice microbiome data in public databases and support from TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript.

1. Data Download
<ol>
	<li>Download the data of the accession PRJNA386367 form the NCBI database. From the data of the accession PRJNA386367, select the rhizosphere, rhizoplane, and endosphere microbiome data from rice plants grown for 14 weeks in a submerged rice field in Arbuckle, California in 2014. 
	&#8203;NOTE: The rhizosphere, rhizoplane, and endosphere microbiome data were presented by the OTUs table in accession PRJNA386367.</li>
</ol>
2. Optimal power value determination
<p clas...

<ol>
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The authors have nothing to disclose.

Correlation networks have been increasingly used in bioinformatics applications. WGCNA is a systems biology method for descriptive analysis of the relationships between various elements of a biological system12. R software package was used in earlier work on WGCNA13,14,15. The package includes functions for network construction, module detection, calculations of topological properties, data simulation, vi...

Microbiome research has important implications for understanding and manipulating ecosystem processes1,2. Microbial populations are interconnected by interacting ecological networks, whose characteristics can affect the response of microorganisms to environmental changes3,4. Furthermore, the properties of these networks affect the stability of microbial communities, and are closely associated with soil function5. Weighted gene correlation network analysis has now been widely applied for research on the relationship between genes and microbial communities6. Previous studies have focused mainly on the associations between networks of different genes or populations and the outside world7. However, the differences in correlation networks formed by microbial populations under different environmental conditions have been scarcely investigated. The purpose of the research presented in this paper is to provide insights and details on the rapid implementation of the WGCNA algorithm to construct a co-occurrence network of microbiome samples collected under different environmental conditions. Based on the analysis results, we assessed the composition and differences of the population and further discussed the relationship between different microbial populations. The following basic flow of weighted correlation network algorithm8 was applied. First, a similarity matrix needed to be constructed by calculating the Pearson correlation coefficient between the Operational Taxonomic Units (OTU) expression profiles. Then, the parameters of the adjacency functions (the power or the sigmoid adjacency functions) were adopted with a scale-free topology criterion, the similarity matrix was transformed into an adjacency matrix, and each co-occurrence network corresponded to an adjacency matrix. We used average linkage hierarchical clustering coupled with the TOM-based dissimilarity to group OTUs with coherent expression profiles into modules. Further, we calculated the relationship between conservative statistics and the related parameter analysis modules, finally identifying the hub OTU in the module. These methods are particularly suitable for analysis of the differences in network structures among various microbial populations under divergent environmental conditions. In this manuscript, we have described in detail the method of co-expression network development, the analysis of the dissimilarities between the modules, and have provided a brief overview of the steps in the procedure applied to obtain the core species in different module networks.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>R</td>
			<td>The University of Auckland</td>
			<td>version 4.0.2</td>
			<td>R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.</td>
		</tr>
		<tr>
			<td>RStdio</td>
			<td>JJ Allaire</td>
			<td>version 1.4.1103</td>
			<td>The RStudio IDE is a set of integrated tools designed to help you be more productive with R and Python.</td>
		</tr>
		<tr>
			<td>Cytoscape</td>
			<td></td>
			<td>version 3.7.1</td>
			<td>Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data.</td>
		</tr>
		<tr>
			<td>NCBI database</td>
			<td></td>
			<td></td>
			<td>The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information.</td>
		</tr>
	</tbody>
</table>

divergence of root microbiota in different habitats based on weighted correlation networks

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, which can effectively explore the interaction relationship or co-occurrence model of different microbial species in different environments. The purpose of this manuscript is to provide details on how to use the weighted correlation network algorithm to analyze different co-occurrence networks that may occur in microbial communities due to different ecological environments. All analysis of the experiment is performed in the WGCNA package. WGCNA is an R package for weighted correlation network analysis. The experimental data used to demonstrate these methods were microbial community data from the NCBI (National Center for Biotechnology Information) database for three niches of the rice (Oryza sativa) root system. We used the weighted correlation network algorithm to construct co-abundance networks of microbial community in each of the three niches. Then, differential co-abundance networks among endosphere, rhizoplane and rhizosphere soil were identified. In addition, the core genera in network were obtained by the "WGCNA" package, which plays an important regulated role in network functions. These methods enable researchers to analyze the response of microbial network to environmental disturbance and verify different microbial ecological response theories. The results of these methods show that the significant differential microbial networks identified in the endosphere, rhizoplane and rhizosphere soil of rice.

The representative results in this article were downloaded from the 2014 California Abaker rice root microbiome data in the NCBI database (PRJNA386367)9. The data includes the rhizosphere, rhizoplane, and endosphere microbiome samples from rice plants grown for 14 weeks in a submerged rice field. We used the WGCNA algorithm to select the power value that satisfied the three networks that were close to the scale-free network (Figure 1) and developed three co-expression...

Watch this Scientific Journal Video about Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks at JoVE.com

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Network analysis was applied to evaluate the association of various ecological microbial communities, such as soil, water and rhizosphere. Presented here is a protocol on how to use the WGCNA algorithm to analyze different co-occurrence networks that may occur in the microbial communities due to different ecological environments.

The root microbiome plays an important role in plant growth and environmental adaptation. Network analysis is an important tool for studying communities, ...

This method can effectively explore the interactive relationship or co-occurrence network of different microbial species in different environments. It provides detail on how to use the WGCNA algorithm to construct a co-occurrence network of microbiota. In addition, based on the results, this method assesses the differentiation of microbial relationships and composition between microbial consortia.Here is the basic flow of the method. The composition and abundance data of microbiota are downloaded from the NCBI database or sequence data for your samples. First, open the RStudio software and install the WGCNA package.Then, load the data and use the good samples genes function to check the correctness of the data. Check for outliers and store samples that meet our requirements. When the check result is true, continue to the next step.Save the result. Use the picks off threshold function to calculate the scale free index, R squared, of the data under different power values. Visualize the results.When the scale free index is closer to one, the network structure is closer to the scale free network. When the scale free index, R squared, is greater than 0.9, select the power value. Finally, use the same method to analyze the rest of the microbiome data.First, use the adjacency function to construct a symbolic co-occurrence network. In addition, use the TOM similarity function to develop a topological, overlapping network. Second, use the Hclust function to perform hierarchical clustering and draw the resulting cluster tree.Third, use the cutree dynamic function to perform dynamic branch cutting and use the min cluster size parameter to set the smallest module size. The smallest module size is usually set over 30. Fourth, calculate the microbial characteristic of each module.Hierarchical clustering is performed according to the correlation coefficient, and modules with a height of less than 0.25 were merged to acquire the distribution of each module. Fifth, use the plot dendro and colors function to visualize the results. The assignment display diagram of the co-occurrence network module is obtained.Then, rename the module colors and construct digital labels corresponding to the colors. Save them for use in subsequent parts. Finally, repeat the above process for other sets of data.In this part, compare and analyze the two sets of data and conduct the preservation test. First, load the parameters and results of the two datasets saved in the previous steps. Then, set the module results of one data set as the reference group, another as the test group and perform the module comparison.Next, calculate the values of the conservativeness, statistical parameters, Z summary and median rank to quantify the conservativeness between modules. Finally, visualize the results. Determine the network module that satisfies both the Z summary value, less than two, and the median rank value at the top.This module is the most non-preserved module in the two microbiota data. Perform correlation analysis of the module membership. Set the module assignment results of two networks as the reference and the test group respectively.The settings need to be the same as the preservation test. First, the KME value of each OTU was calculated in several candidate modules based on the results of the preservation test. Take the yellow module as an example.Calculate the correlation coefficient of the KME value in the two yellow modules, then draw the correlation analysis diagram of the KME value in the module. Finally, according to the correlation coefficient in the figure, judge the conservativeness of the module in the two data set. Select the module with the smallest correlation coefficient.First, use the export network to Cytoscape function to export the network to an edge and node list file that Cytoscape can read. Then, import the file into Cytoscape. Set the threshold to 0.5 and adjust other parameters as needed.Finally, obtain a co-occurrence network of different microorganisms. In this article, the WGCNA algorithm is used to analyze the differences in three niches of the rice root system. Select the power value that satisfied the three networks that were close to the scale free network.In the endosphere, rhizoplane, and rhizosphere microbial occurrence network, identify 23, 22 and 21 models respectively. Use the preservation test and correlation analysis to find the extremely non-conservative modules between each two niches of the rice root system. Construct a co-occurrence network for these three modules using different colors to represent different microbial phylum.Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes and Verrucomicrobia dominated the three different microbial networks. Moreover, 17 core genera mainly regulate these networks. After watching this video, you should have a good understanding of how to perform a series of steps to use the WGCNA algorithm to analyze different co-occurrence networks that may occur in the microbial communities due to different ecological environments.

divergence-root-microbiota-different-habitats-based-on-weighted

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4.2K 조회수.  Guizhou Normal University. 이 방법은 다른 환경에서 다른 미생물 종의 대화형 관계 또는 공존 네트워크를 효과적으로 탐색할 수 있습니다. WGCNA 알고리즘을 사용하여 미생물의 공존 네트워크를 구성하는 방법에 대한 세부 정보를 제공합니다. 또한, 결과에 기초하여, 이 방법은 미생물 동과간의 미생물 관계 및 조성물의 분화를 평가한다.다음은 메서드의 기본 흐름입니다. 미생물의 구성 및 풍부한...