放弃Venn-Upset-花瓣图，拥抱二分网络

写在前面

让点随机排布在一个区域，保证点之间不重叠,并且将点的图层放到最上层，保证节点最清晰，然后边可以进行透明化，更加突出节点的位置。这里我新构建了布局函数 PolyRdmNotdCirG 来做这个随机排布。调用的是packcircles包的算法。使用和其他相似函数一样，这里我们重点介绍一下使用这种算法构造的二分网络布局。

微生物网络

ggClusterNet 安装

ggClusterNet包依赖的R包均在cran或者biocductor中，所以未能成功安装，需要检查依赖是否都顺利安装。如果网路问题，无法下载R包，可以在github中手动下载安装

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#---ggClusterNet
devtools::install_github("taowenmicro/ggClusterNet")
#--如果无法安装请检查网络或者换个时间

导入R包和输入文件

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#--导入所需R包#-------
library(ggplot2)
library(ggrepel)
library(ggClusterNet)
library(phyloseq)
library(dplyr)

# 数据内置
#-----导入数据#-------
data(ps)

#--可选
#-----导入数据#-------
ps = readRDS("../ori_data/ps_liu.rds")

这里我们提取一部分OTU，节省出图时间。

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# ps
data(ps)

ps_sub = filter_taxa(ps, function(x) sum(x ) > 20 , TRUE)
ps_sub = filter_taxa(ps_sub, function(x) sum(x ) < 30 , TRUE)
ps_sub

div_network函数用于计算共有和特有关系

这个函数是之前我写的专门用于从OTU表格整理成Gephi的输入文件，所以大家直接用这个函数即可转到gephi进行操作。这次为了配合二分网络，我设置了参数flour = TRUE，代表是否仅仅提取共有部分和特有部分。

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# ?div_network
result = div_network(ps_sub,num = 6)

edge = result[[1]]
head(edge)

# levels(edge$target)
# node = result[[2]]
# head(node)
#
# tail(node)
data = result[[3]]
dim(data)

#----计算节点坐标
# flour参数，设置是否仅仅展示共有和特有的二分网络

div_culculate函数核心算法，用于计算二分网络的节点和边的表格

参数解释：

distance = 1.1：

中心一团点到样本点距离

distance2 = 1.5：

中心点模块到独有OTU点之间距离

distance3 = 1.3：

样本点和独有OTU之间的距离

order = FALSE ：

节点是否需要随机扰动效果

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result <- div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)

edge = result[[1]]
head(edge)

plotdata = result[[2]]
head(plotdata)
#--这部分数据是样本点数据
groupdata <- result[[3]]

对OTU进行注释，方便添加到图形上

为了让节点更加丰富，这里我对节点文件添加了注释信息。

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# table(plotdata$elements)
node =  plotdata[plotdata$elements == unique(plotdata$elements), ]

otu_table = as.data.frame(t(vegan_otu(ps_sub)))
tax_table = as.data.frame(vegan_tax(ps_sub))
res = merge(node,tax_table,by = "row.names",all = F)
dim(res)
head(res)
row.names(res) = res$Row.names
res$Row.names = NULL
plotcord = res

xx = data.frame(mean  =rowMeans(otu_table))
head(xx)
plotcord = merge(plotcord,xx,by = "row.names",all = FALSE)
head(plotcord)
# plotcord$Phylum
row.names(plotcord) = plotcord$Row.names
plotcord$Row.names = NULL
head(plotcord)
p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),
                            data = edge, size = 0.3,color = "yellow") +
  geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +
  geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +
  geom_text_repel(aes(X1, X2,label = elements ), data = groupdata) +
  theme_void()

p

ggsave("4.png",p,width = 12,height = 8)

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map = as.data.frame(sample_data(ps_sub))

map$Group2 <- rep(c("A1","A2","A3","A4","A5","A6"),3)

sample_data(ps_sub) <- map
# ?div_network
result = div_network(ps_sub,num = 3,group = "Group2",flour = TRUE)

edge = result[[1]]
head(edge)

# levels(edge$target)
# node = result[[2]]
# head(node)
#
# tail(node)

data = result[[3]]
dim(data)

#----计算节点坐标
# flour参数，设置是否仅仅展示共有和特有的二分网络

result <- div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)

edge = result[[1]]
head(edge)

plotdata = result[[2]]
head(plotdata)

groupdata <- result[[3]]

# table(plotdata$elements)
node =  plotdata[plotdata$elements == unique(plotdata$elements), ]

otu_table = as.data.frame(t(vegan_otu(ps_sub)))
tax_table = as.data.frame(vegan_tax(ps_sub))
res = merge(node,tax_table,by = "row.names",all = F)
dim(res)
head(res)
row.names(res) = res$Row.names
res$Row.names = NULL
plotcord = res

xx = data.frame(mean  =rowMeans(otu_table))
head(xx)
plotcord = merge(plotcord,xx,by = "row.names",all = FALSE)
head(plotcord)
# plotcord$Phylum
row.names(plotcord) = plotcord$Row.names
plotcord$Row.names = NULL
head(plotcord)

p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),
                            data = edge, size = 0.3,color = "yellow") +
  geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +
  geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +
  geom_text_repel(aes(X1, X2,label = elements ), data = groupdata) +
  theme_void()
p
ggsave("4.png",p,width = 12,height = 8)

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map = as.data.frame(sample_data(ps_sub))

map = map[1:12,]

# map$Group2 <- rep(c("A1","A2","A3","A4","A5","A6"),2)
sample_data(ps_sub) <- map

result = div_network(ps_sub,num = 3,group = "Group",flour = TRUE)

edge = result[[1]]
head(edge)

# levels(edge$target)
# node = result[[2]]
# head(node)
#
# tail(node)

data = result[[3]]
dim(data)

result <- div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)

edge = result[[1]]
head(edge)

plotdata = result[[2]]
head(plotdata)

groupdata <- result[[3]]

# table(plotdata$elements)
node =  plotdata[plotdata$elements == unique(plotdata$elements), ]

otu_table = as.data.frame(t(vegan_otu(ps_sub)))
tax_table = as.data.frame(vegan_tax(ps_sub))
res = merge(node,tax_table,by = "row.names",all = F)
dim(res)
head(res)
row.names(res) = res$Row.names
res$Row.names = NULL
plotcord = res

xx = data.frame(mean  =rowMeans(otu_table))
head(xx)
plotcord = merge(plotcord,xx,by = "row.names",all = FALSE)
head(plotcord)
# plotcord$Phylum
row.names(plotcord) = plotcord$Row.names
plotcord$Row.names = NULL
head(plotcord)

p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),
                            data = edge, size = 0.3,color = "yellow") +
  geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +
  geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +
  geom_text_repel(aes(X1, X2,label = elements ), data = groupdata) +
  theme_void()

p

# ggsave("4.png",p,width = 12,height = 22)

放弃Venn-Upset-花瓣图，拥抱二分网络

写在前面

微生物网络

导入R包和输入文件

div_network函数 用于计算共有和特有关系

div_culculate函数 核心算法，用于计算二分网络的节点和边的表格

对OTU进行注释，方便添加到图形上

div_network函数用于计算共有和特有关系

div_culculate函数核心算法，用于计算二分网络的节点和边的表格