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.
