偶然发现一本书:
里面刚好有微生物网络分析的内容，我就当个搬运工把代码整理了一下（注：书籍和示例数据在公众号PLANTOMIX后台回复“微生物组分析”即可获取下载链接噢）：
library(ggraph)
library(vegan)
library(MCL)
library(tidyverse)
library(qgraph)
library(SpiecEasi)
library(ggnet)
# load OTU table
load('../data/data.OTU.RData')
data.otu = data.otu[,seq(1,90,3)]
colnames(data.otu) = paste('S',1:ncol(data.otu), sep = '')
# 基于相异度的网络（Dissimilarity-Based Network）
diss.mat = vegan::vegdist(t(data.otu),
method = 'bray') %>%
as.matrix()
diss.cutoff = 0.6 # 设置阈值
diss.adj = ifelse(diss.mat <= diss.cutoff, 1, 0)
# 从邻接矩阵中构建网络
diss.net = graph.adjacency(diss.adj,
mode = 'undirected',
diag = FALSE)
# 基于相关性的网络
cor.matrix = cor(diss.mat,method = 'pearson')
cor.cutoff = 0.3
cor.adj = ifelse(abs(cor.matrix) >= cor.cutoff,1,0) # 相关性矩阵转换成二元邻接矩阵
cor.net = graph.adjacency(cor.adj,
mode = 'undirected',
diag = FALSE)
build.cor.net =function(MB, method, num_perms, sig_level){
taxa =dim(MB)[2]
MB.mat =array(0, dim = c(taxa, taxa, num_perms + 1))
# Perform permutation Constructing and Analyzing Microbiome Networks in R 251
MBperm =permatswap(MB, "quasiswap", times = num_perms)
# Convert to relative abundance
MB.relative =MB / rowSums(MB)
MB.mat[,,1]<-as.matrix(cor(MB.relative,method=method))
for(p in 2:num_perms) {
MBperm.relative=MBperm$perm[[p-1]] / rowSums(MBperm$perm[[p-1]])
MB.mat[, , p] =as.matrix(cor(MBperm.relative,
method=method))
}
# Get p-values
pvals=sapply(1:taxa,
function(i) sapply(1:taxa, function(j)
sum(MB.mat[i, j, 1]> MB.mat[i, j, 2:num_perms])))
pvals=pvals / num_perms
# p-value correction
pvals_BH=array(p.adjust(pvals, method = "BH"),
dim=c(nrow(pvals), ncol(pvals)))
# Build adjacency matrix
adj.mat=ifelse(pvals_BH>= (1 - sig_level), 1, 0)
# Add names to rows & cols
rownames(adj.mat)=colnames(MB)
colnames(adj.mat)=colnames(MB)
# Build and return the network
graph<-graph.adjacency(adj.mat,
mode="undirected",
diag=FALSE)
}
cor.net.2 = build.cor.net(as.matrix(data.otu), # 此处矩阵要求输入的是整数，小数会报错
method = 'pearson',
num_perms = 999,
sig_level = 0.01)
# Graphical Model Networks
ebic.cor = cor_auto(data.otu) # extended Bayesian information criterion
ebic.graph = EBICglasso(ebic.cor,
ncol(data.otu),
0.5)
ebic.qgnet = qgraph(ebic.graph,
DoNotPlot = FALSE) # build network
ebic.net = as.igraph(ebic.qgnet,
attributes=T) # convert to igraph
# local false discovery rate network
fdr.cor = cor_auto(data.otu)
fdr.graph = FDRnetwork(fdr.cor,
cutoff = 0.01,
method = 'pval')
fdr.qgnet = qgraph(fdr.graph, DoNotPlot = T)
fdr.net = as.igraph(fdr.qgnet)
# SparCC and SPIEC-EASI Networks
sparcc.matrix = sparcc(data.otu)
sparcc.cutoff = 0.3
sparcc.adj = ifelse(abs(sparcc.matrix$Cor) >= sparcc.cutoff,
1,0)
rownames(sparcc.adj) = colnames(data.otu)
colnames(sparcc.adj) = colnames(data.otu)
sparcc.net = graph.adjacency(sparcc.adj,
mode = 'undirected',
diag = FALSE)
# SPIEC-EASI network
spieceasi.matrix = spiec.easi(as.matrix(data.otu),
method = 'glasso',
lambda.min.ratio = 1e-2,
nlambda = 20,
icov.select.params = list(rep.num = 50))
rownames(spieceasi.matrix[["refit"]][["stars"]]) = colnames(data.otu)
spieceasi.net = graph.adjacency(spieceasi.matrix[["refit"]][["stars"]],
mode = 'undirected',
diag = FALSE)
# hub decetion
net = sparcc.net
net.cn = closeness(net)
net.bn = betweenness(net)
net.pr = page_rank(net)$vector
net.hs = hub_score(net)$vector
net.hs.sort = sort(net.hs, decreasing = T)
net.hs.top5 = head(net.hs.sort, 5)
# cluster decetion
wt = walktrap.community(net)
ml = multilevel.community(net)
membership(wt)
adj = as_adjacency_matrix(net)
mc = mcl(adj, addLoops = TRUE)
compare(membership(wt), membership(ml))
compare(membership(wt), mc$Cluster)
expected.cls =sample(1:5, vcount(net), replace = T) %>%
as_membership
compare(expected.cls, membership(wt))
plot_dendrogram(wt)
modularity(net, membership(wt))
# Network Simulation
num.nodes = 50
regular.net = k.regular.game(num.nodes, k = 4)
random.net = erdos.renyi.game(num.nodes, p = 0.037)
smallworld.net = sample_smallworld(dim = 1, num.nodes, nei =
2, p = 0.2)
scalefree.net = barabasi.game(num.nodes)
# 网络可视化
net = diss.net
wt = walktrap.community(net)
layout = c(layout.fruchterman.reingold,
layout_as_tree,
layout_nicely,
layout_on_sphere)
plot.igraph(net,
vertex.size = 10,
vertex.label = NA,
edge.curved = T,
edge.size = 1,
layout = layout.fruchterman.reingold)
# 带聚类
ceb = cluster_edge_betweenness(net)
dendPlot(ceb, mode="hclust")
plot(ceb, net)
# 导出网络
#将网络图导出为"graphml"、"gml"格式，方便导入Gephi等软件中使用
#支持的格式有"edgelist", "pajek", "ncol", "lgl"
#"graphml", "dimacs", "gml", "dot", "leda"
write_graph(net, "../figures/g1.graphml", format="graphml")
write_graph(net, "../figures/g1.gephi", format="gml")
write_graph(net, '../figures/net.txt', format = 'edgelist')
大概图形是这样的：
我的 Cytoscape 和 Gephi 都罢工了，无法进行网络图绘制，大家自行尝试一下噢！