微生物网络分析学习手稿

查找以下3个问题的文献，并尝试用R实现：

构建网络常用的方法
Cross-kingdom网络的构建
环境因子与网络拓扑参数的关系

一、构建网络常用的方法

二、Cross-kingdom网络的构建

三、环境因子与网络的关系

一、构建网络常用的方法及原理

MENAphttp://ieg4.rccc.ou.edu/mena

CoNethttps://apps.cytoscape.org/apps/conet

SparCChttps://github.com/scwatts/fastspar

SPIEC-EASIhttps://github.com/zdk123/SpiecEasi

二、Cross-kingdom网络的构建

参考自：Sparse and Compositionally Robust Inference of Microbial Ecological Networks

SPIEC-EASIhttps://github.com/zdk123/SpiecEasi

参考自：Integrative Transkingdom Analysis of the Gut Microbiome in Antibiotic Perturbation and Critical Illness

MOFAhttps://biofam.github.io/MOFA2/tutorials.html

三、环境因子与网络的关系

1. 探究环境因子与网络拓扑参数的关系

参考自：Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China

We generated sub-networks for each soil sample from meta-community networks by preserving OTUs presented in each site using subgraph functions in igraph packages. Network-level topological features provided in igraph packages were calculated for each sub-network. We grouped each sub-network by sampling location and used Wilcoxon rank-sum test to determine the different network-level topological features between climatic regions. We then predicted the spatial distribution of these topological features based on Krige interpolation using the function autoKrige in automap packages (Hiemstra et al., 2009). The correlation coefficients between network level topological features and environmental factors were calculated. The importance of environmental factors (geographic factors, climatic factors and soil properties) for network-level topological features was estimated with multiple regression on distance matrices (MRM) in ecodist packages. The Euclidean distance matrices for environmental factors and network-level topological features standardized with decostand of vegan package were used in MRM models.To test the relationship between network level topological features and environmental factors, we further compared the first component of principal component analysis for network-level topological features with soil pH or the first principal component analysis components of soil carbon, iron and nitrogen parameters, respectively.

参考自：Molecular ecological network analyses

The relationships between microbial network topology and environmental characteristics can be examined in both direct and indirect ways. Indirectly, as a first step, the OTU significance (GS) is calculated and defined as the square of Pearson correlation coefficient (r2) of OTU abundance profile with environmental traits. Then the correlation between GS and nodes’ topological indices (e.g., connectivity) was used to measure the relationship of network topology with traits. For instance, in warming pMEN, the GS of average soil temperature was significantly correlated with the nodes’ connectivity (r = 0.30, p = 4.7 × 10-5), indicating that the nodes with higher connectivity were inclined to have closer relationships with temperature. If multiple GS was involved, Mantel and partial Mantel tests could be implemented to calculate correlations between the connectivity and multiple GS of environmental traits to reveal the internal associations between network topology and environmental changes. In warming pMEN, the nodes’ connectivity was significantly associated with the GS of pH values, soil NO3-nitrogen and soil carbon contents when the effect of temperature was controlled (rM = 0.104, P = 0.018). Meanwhile, the GS of temperature was also significantly associated with the connectivity when aforementioned soil geochemistry factors were controlled (rM = 0.159, P = 0.003) (Table 4). Moreover, the OTUs of β Proteobacteria and Verrucomicrobia were highly associated with the changes of soil geochemistry (rM = 0.59 and 0.926 respectively, both P = 0.013). These results suggested that the OTUs topology in warming pMEN was significantly associated with both temperature and the selected soil variables. In addition, OTUs from β-Proteobacteria and Verrucomicrobia were most sensitive to the changes of soil variables.

2.探究环境因子对网络模块的影响

参考自：Molecular ecological network analyses

The correlations between module-based eigengenes and environmental factors can be used to detect the modules’ response to environmental changes. In warming pMEN, the coefficients (r values) and significances (p values) were shown in a heatmap (Figure 5). Submodules #1 and #9 were positively correlated with the average soil temperature significantly (p < 0.01) but negatively (p < 0.01) with soil pH values and soil carbon contents, indicating that the members in these two submodules might be stimulated by temperature but inhibited by soil pH and carbon. Also, submodules #6 and #8 were positively correlated with soil pH (p < 0.01), #4 was positively correlated with NO3- concentration (p = 0.001) and soil carbon content (p = 0.013). While #3 was positively correlated with carbon content (p = 0.016), #7 was negatively correlated with soil carbon content (p = 0.025). In addition, #2 and #6 were negatively correlated with temperature (p < 0.05). All above results demonstrated that different submodules in warming pMEN responded to the environmental changes differently and the changes of temperature could have significant impacts on members of some submodules (e.g., #1, #2, #6 and #9).

参考自：Co-occurrence patterns of soybean rhizosphere microbiome at a continental scale

To examine the relationships between modules and environmental factors, we calculated the module eigengene E (the first principal component of modules) (de Menezes et al., 2015) of the top modules for both bulk soil and rhizosphere networks, and then tested their relationships with edaphic and climate variables using Spearman's rank correlation test.

参考自：Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters

The WCNA procedure also allows the calculation of module and trait (e.g.soil variable) associations; first, the first principal componentof each module (the module eigengene E) is calculated. The eigengene (E) summarizes the ribotype relative abundance profiles within a module. For each module, the eigengenesare then correlated with the external traits to provide ameasure of trait and module relationship.

参考自：Biogeographic patterns of microbial association networks in paddy soil within Eastern Chin

The importance of environmental variables for network 226 topological features were estimated using multiple regression model (MRM) with the R ‘ecodist’ 227 package. All environmental variables were standardized with function ‘decostand’ in R vegan 228 package, and Euclidean distance matrices were used in the MRM model. Pearson’s correlations 229 were used to determine how environmental variables influence network topological features with 230 the R ‘cor.test’ function. We further investigated the importance of environmental variables on 231 microbial community composition using MRM model, in which Euclidean distance matrices for 232 environmental variables datasets and Bray-Curtis distance matrices for community datasets were 233 used. We also examined the relationships between microbial networks and environmental 234 variables in an indirect way using OTU significance, which was defined as the square of Pearson correlation coefficient (r2 235 ) of OTU abundance profile with environmental variables (Deng et al., 236 2012). After calculating OTU significance, Mantel tests were used to examine the relationships 237 between OTU significance and network features for exploring relationships between potential 238 interactions and environmental variables.