骚操作 将sna包中全部layout都要用ggplot模块化展示出来 - taowenmicro/ggClusterNet GitHub Wiki

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写在前面

为了让cluster更加灵活，我写了一个randomClusterG布局，这个布局根据模块生成坐标，但是坐标是随意生成的，并且不同模块之间不会重叠。每次出图都是不一样的，下面带领大家学习学习。

第一部分：随机计算模块位置，可视化网络

输入文件

#--导入所需R包#-------
library(igraph)
library(ggplot2)
library(ggClusterNet)
library(sna)
#-----导入数据#-------
ps = readRDS("../ori_data/ps_liu.rds")
ps

corMicro函数用于计算相关

按照丰度过滤微生物表格，并却计算相关矩阵，按照指定的阈值挑选矩阵中展示的数值。

#-----微生物网络构建参数设置#----
#-提取丰度前百分之多少的otu进行构建网络
# N = 0.02
# r.threshold=0.6
# p.threshold=0.05


#----------计算相关#----
result = corMicro (ps = ps,N = 0.02,r.threshold=0.8,p.threshold=0.05,method = "pearson")

#--提取相关矩阵
cor = result[1](/taowenmicro/ggClusterNet/wiki/1)
# head(cor)

制作分组

这是网络布局的基础，无论是什么聚类布局，都需要制作一个分组文件，这个文件有两列，一列是节点，一列是分组信息，这个分组信息名称为：group。

#-提取tax注释文件，用于分组#-----


#-提取tax表格--目的是按照分类等级进行分组聚类绘制网络#----

ps_net = result[3](/taowenmicro/ggClusterNet/wiki/3)


vegan_tax <-  function(physeq){
  tax <-  tax_table(physeq)

  return(as(tax,"matrix"))
}
tax_table = as.data.frame(vegan_tax(ps_net))
group = as.data.frame(tax_table)
head(group)

group$ID = row.names(group)


#--指定分组，也就是otu一列，分组一列
netClu = data.frame(ID = row.names(group),group = group$Phylum)

randomClusterG 根据分组，随机分布计算布局

#--------计算布局#---------
#-------计算网络布局-得到节点坐标=node#---------
result2 = randomClusterG (cor = cor,nodeGroup =netClu )

node = result2[1](/taowenmicro/ggClusterNet/wiki/1)
head(node)

### nodeadd 节点注释的简单封装，便捷实用otu表格和分组文件进行注释

vegan_otu <-  function(physeq){
  OTU <-  otu_table(physeq)
  if(taxa_are_rows(OTU)){
    OTU <-  t(OTU)
  }
  return(as(OTU,"matrix"))
}
otu_table = as.data.frame(t(vegan_otu(ps_net)))

#---node节点注释#-----------
nodes = nodeadd(plotcord =node,otu_table = otu_table ,tax_table = tax_table)
head(nodes)


#-----计算边#--------
edge = edgeBuild(cor = cor,plotcord = node)

head(edge)

出图

pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = as.factor(wei_label)),
                                data = edge, size = 0.5) +
  geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) +
  scale_colour_brewer(palette = "Set1") +
  scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
  theme(panel.background = element_blank()) +
  # theme(legend.position = "none") +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
  theme(legend.background = element_rect(colour = NA)) +
  theme(panel.background = element_rect(fill = "white",  colour = NA)) +
  theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet

ggsave("0.png",pnet)

# pnet <- pnet +  geom_text_repel(aes(X1, X2,label=elements),size=4, data = plotcord)

#--------计算布局#---------
#-------计算网络布局-得到节点坐标=node#---------
result2 = randomClusterG (cor = cor,nodeGroup =netClu )

node = result2[1](/taowenmicro/ggClusterNet/wiki/1)
head(node)

### nodeadd 节点注释的简单封装，便捷实用otu表格和分组文件进行注释

vegan_otu <-  function(physeq){
  OTU <-  otu_table(physeq)
  if(taxa_are_rows(OTU)){
    OTU <-  t(OTU)
  }
  return(as(OTU,"matrix"))
}
otu_table = as.data.frame(t(vegan_otu(ps_net)))

#---node节点注释#-----------
nodes = nodeadd(plotcord =node,otu_table = otu_table ,tax_table = tax_table)
head(nodes)


#-----计算边#--------
edge = edgeBuild(cor = cor,plotcord = node)

head(edge)

出图

pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = as.factor(wei_label)),
                                data = edge, size = 0.5) +
  geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) +
  scale_colour_brewer(palette = "Set1") +
  scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
  theme(panel.background = element_blank()) +
  # theme(legend.position = "none") +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
  theme(legend.background = element_rect(colour = NA)) +
  theme(panel.background = element_rect(fill = "white",  colour = NA)) +
  theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet

ggsave("1.png",pnet)

# pnet <- pnet +  geom_text_repel(aes(X1, X2,label=elements),size=4, data = plotcord)

第二部分：sna包中全部的layout都要模块化展示

写在前面

sna包中有好多layout，但是正如大家所知道的，使用起来很少，因为还是太难看了，今天我将这些layout都进行一个模块化输出，希望可以改善一些layout的可视化效果。

微生物网络

输入文件

#--导入所需R包#-------
library(igraph)
library(network)
library(sna)
library(ggplot2)
library(ggrepel)
library(ggClusterNet)
#-----导入数据#-------
ps = readRDS("../ori_data/ps_liu.rds")
ps

corMicro函数用于计算相关

按照丰度过滤微生物表格，并却计算相关矩阵，按照指定的阈值挑选矩阵中展示的数值。

#-----微生物网络构建参数设置#----
#-提取丰度前百分之多少的otu进行构建网络
# N = 0.02
# r.threshold=0.6
# p.threshold=0.05


#----------计算相关#----
result = corMicro (ps = ps,N = 0.05,r.threshold=0.6,p.threshold=0.05,method = "pearson")

#--提取相关矩阵
cor = result[1](/taowenmicro/ggClusterNet/wiki/1)
# head(cor)

制作分组

这是网络布局的基础，无论是什么聚类布局，都需要制作一个分组文件，这个文件有两列，一列是节点，一列是分组信息，这个分组信息名称为：group。

#-提取tax注释文件，用于分组#-----


#-提取tax表格--目的是按照分类等级进行分组聚类绘制网络#----

ps_net = result[3](/taowenmicro/ggClusterNet/wiki/3)


vegan_tax <-  function(physeq){
  tax <-  tax_table(physeq)

  return(as(tax,"matrix"))
}
tax_table = as.data.frame(vegan_tax(ps_net))
group = as.data.frame(tax_table)
head(group)

group$ID = row.names(group)


#--指定分组，也就是otu一列，分组一列
netClu = data.frame(ID = row.names(group),group = group$Phylum)

### 计算布局: "circle" ：布局

# layouts = c("circle","adj","circrand","eigen","random")
#-------计算网络布局-得到节点坐标=node#---------
result2 = ranSNEClusterG (cor=  cor,layout ="circle")

node = result2[1](/taowenmicro/ggClusterNet/wiki/1)
head(node)
# row.names(node) = node$elements

dim(node)

vegan_otu <-  function(physeq){
  OTU <-  otu_table(physeq)
  if(taxa_are_rows(OTU)){
    OTU <-  t(OTU)
  }
  return(as(OTU,"matrix"))
}
otu_table = as.data.frame(t(vegan_otu(ps_net)))

#---node节点注释#-----------
nodes = nodeadd(plotcord =node,otu_table = otu_table ,tax_table = tax_table)
head(nodes)
#-----计算边#--------
edge = edgeBuild(cor = cor,plotcord = node)

head(edge)


### 出图

pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = as.factor(wei_label)),
                                data = edge, size = 0.5) +
  geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) +
  scale_colour_brewer(palette = "Set1") +
  scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
  # labs( title = paste(layout,"network",sep = "_"))+
  # geom_text_repel(aes(X1, X2,label=Phylum),size=4, data = plotcord)+
  # discard default grid + titles in ggplot2
  theme(panel.background = element_blank()) +
  # theme(legend.position = "none") +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
  theme(legend.background = element_rect(colour = NA)) +
  theme(panel.background = element_rect(fill = "white",  colour = NA)) +
  theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet

ggsave("10.png",pnet)
# pnet <- pnet +  geom_text_repel(aes(X1, X2,label=elements),size=4, data = plotcord)

"circrand" 布局

# layouts = c("circle","adj","circrand","eigen","random")
#-------计算网络布局-得到节点坐标=node#---------
result2 = ranSNEClusterG (cor=  cor,layout ="circrand")

node = result2[1](/taowenmicro/ggClusterNet/wiki/1)
head(node)


vegan_otu <-  function(physeq){
  OTU <-  otu_table(physeq)
  if(taxa_are_rows(OTU)){
    OTU <-  t(OTU)
  }
  return(as(OTU,"matrix"))
}
otu_table = as.data.frame(t(vegan_otu(ps_net)))

#---node节点注释#-----------
nodes = nodeadd(plotcord =node,otu_table = otu_table ,tax_table = tax_table)
head(nodes)
#-----计算边#--------
edge = edgeBuild(cor = cor,plotcord = node)

head(edge)


### 出图

pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = as.factor(wei_label)),
                                data = edge, size = 0.5) +
  geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) +
  scale_colour_brewer(palette = "Set1") +
  scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
  # labs( title = paste(layout,"network",sep = "_"))+
  # geom_text_repel(aes(X1, X2,label=Phylum),size=4, data = plotcord)+
  # discard default grid + titles in ggplot2
  theme(panel.background = element_blank()) +
  # theme(legend.position = "none") +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
  theme(legend.background = element_rect(colour = NA)) +
  theme(panel.background = element_rect(fill = "white",  colour = NA)) +
  theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet

ggsave("11.png",pnet)

"eigen"布局

# layouts = c("circle","adj","circrand","eigen","random")
#-------计算网络布局-得到节点坐标=node#---------
result2 = ranSNEClusterG (cor=  cor,layout ="eigen")

node = result2[1](/taowenmicro/ggClusterNet/wiki/1)
head(node)

vegan_otu <-  function(physeq){
  OTU <-  otu_table(physeq)
  if(taxa_are_rows(OTU)){
    OTU <-  t(OTU)
  }
  return(as(OTU,"matrix"))
}
otu_table = as.data.frame(t(vegan_otu(ps_net)))

#---node节点注释#-----------
nodes = nodeadd(plotcord =node,otu_table = otu_table ,tax_table = tax_table)
head(nodes)

#-----计算边#--------
edge = edgeBuild(cor = cor,plotcord = node)

head(edge)


### 出图

pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = as.factor(wei_label)),
                                data = edge, size = 0.5) +
  geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) +
  scale_colour_brewer(palette = "Set1") +
  scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
  # labs( title = paste(layout,"network",sep = "_"))+
  # geom_text_repel(aes(X1, X2,label=Phylum),size=4, data = plotcord)+
  # discard default grid + titles in ggplot2
  theme(panel.background = element_blank()) +
  # theme(legend.position = "none") +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
  theme(legend.background = element_rect(colour = NA)) +
  theme(panel.background = element_rect(fill = "white",  colour = NA)) +
  theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet
ggsave("12.png",pnet)

"random"布局

# layouts = c("circle","adj","circrand","eigen","random")
#-------计算网络布局-得到节点坐标=node#---------
result2 = ranSNEClusterG (cor=  cor,layout ="random")

node = result2[1](/taowenmicro/ggClusterNet/wiki/1)
head(node)


#---node节点注释#-----------
nodes = nodeadd(plotcord =node,otu_table = otu_table ,tax_table = tax_table)
head(nodes)
#-----计算边#--------
edge = edgeBuild(cor = cor,plotcord = node)

head(edge)


### 出图

pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,color = as.factor(wei_label)),
                                data = edge, size = 0.5) +
  geom_point(aes(X1, X2,fill = Phylum,size = mean),pch = 21, data = nodes) +
  scale_colour_brewer(palette = "Set1") +
  scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
  # labs( title = paste(layout,"network",sep = "_"))+
  # geom_text_repel(aes(X1, X2,label=Phylum),size=4, data = plotcord)+
  # discard default grid + titles in ggplot2
  theme(panel.background = element_blank()) +
  # theme(legend.position = "none") +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
  theme(legend.background = element_rect(colour = NA)) +
  theme(panel.background = element_rect(fill = "white",  colour = NA)) +
  theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
pnet


ggsave("13.png",pnet)

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