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之前我们微生物生态网络基于igraph进行出图，这是基础包写的出图函数，近年来，随着ggplot的普及，使用ggplot出图似乎成了部分人的基本功，plot函数为代表的基本包已经很少有人去学习了。
为了减少学习成本，同时制作多样化程度和个性化程度更高的网络图，使用ggplot出图成了必然的选择，近年来很多人基于ggplot的版本的网络图进行了许多尝试，但是就微生物网络而言，这一方面使用基于ggplot为基础网络的人很少。
一方面因为转移成本问题，一方面因为展示样式问题。
慢慢的工作量积累够了：
16年gplot.layout的出现，使得ggplot网络可以扩展19中可视化方式。
ggplot出图数据为矩阵，依托于强大的数据框处理函数aplyr包，我们可以对网络进行个性化程度极高的设置，包括标签，图例，颜色，形状，大小。
这是google上的一个尝试
通过ggplot做出基本网络图形
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(network))
suppressPackageStartupMessages(library(sna))
suppressPackageStartupMessages(library(ergm))
library(network)
library(ggplot2)
library(sna)
library(ergm)
plotg <- function(net, value = NULL) {
m <- as.matrix.network.adjacency(net) # get sociomatrix
# get coordinates from Fruchterman and Reingold's force-directed placement
# algorithm.
plotcord <- data.frame(gplot.layout.fruchtermanreingold(m, NULL))
# or get it them from Kamada-Kawai's algorithm: plotcord <-
# data.frame(gplot.layout.kamadakawai(m, NULL))
colnames(plotcord) = c("X1", "X2")
edglist <- as.matrix.network.edgelist(net)
edges <- data.frame(plotcord[edglist[, 1], ], plotcord[edglist[, 2], ])
plotcord$elements <- as.factor(get.vertex.attribute(net, "elements"))
colnames(edges) <- c("X1", "Y1", "X2", "Y2")
edges$midX <- (edges$X1 + edges$X2)/2
edges$midY <- (edges$Y1 + edges$Y2)/2
pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),
data = edges, size = 0.5, colour = "grey") + geom_point(aes(X1, X2,
colour = elements), data = plotcord) + scale_colour_brewer(palette = "Set1") +
scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
# discard default grid + titles in ggplot2
theme(panel.background = element_blank()) + theme(legend.position = "none") +
theme(axis.title.x = element_blank(), axis.title.y = element_blank()) +
theme(legend.background = element_rect(colour = NA)) + theme(panel.background = element_rect(fill = "white",
colour = NA)) + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank())
return(print(pnet))
}
g <- network(50, directed = FALSE, density = 0.03)
classes <- rbinom(50, 1, 0.5) + rbinom(50, 1, 0.5) + rbinom(50, 1, 0.5)
set.vertex.attribute(g, "elements", classes)
g
plotg(g)
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尝试：基于ggplot出图储存于list后批量拼图
ggplot拼图函数有很多，但是这里我们批量出图储存于list中，这里使用下面这个函数做拼图。
google上有人写的：
multiplot <- function(..., plotlist=NULL, cols) {
require(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# Make the panel
plotCols = cols # Number of columns of plots
plotRows = ceiling(numPlots/plotCols) # Number of rows needed, calculated from # of cols
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(plotRows, plotCols)))
vplayout <- function(x, y)
viewport(layout.pos.row = x, layout.pos.col = y)
# Make each plot, in the correct location
for (i in 1:numPlots) {
curRow = ceiling(i/plotCols)
curCol = (i-1) %% plotCols + 1
print(plots[[i]], vp = vplayout(curRow, curCol ))
}
}
基于微生物大量的OTU，我尝试了18中layout
大家使用cor.test计算得到的相关矩阵即可作为输入
# 确定物种间存在相互作用关系的阈值，将相关性R矩阵内不符合的数据转换为0
occor.r[occor.p>p.threshold|abs(occor.r) 0) {
aaa[i] = "+"
}
if (edges$weight[i]< 0) {
aaa[i] = "-"
}
}
#添加到edges中
edges$wei_label = aaa
colnames(edges) <- c("X1", "Y1","OTU_1", "X2", "Y2","OTU_2","weight","wei_label")
edges$midX <- (edges$X1 + edges$X2)/2
edges$midY <- (edges$Y1 + edges$Y2)/2
head(edges)
# library(ggrepel)
pnet <- ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2,colour = edges$wei_label),
data = edges, size = 0.5) +
geom_point(aes(X1, X2), size=3, pch = 21, data = plotcor, fill = "#8DD3C7") + scale_colour_brewer(palette = "Set1") +
scale_x_continuous(breaks = NULL) + scale_y_continuous(breaks = NULL) +
labs( title = names(plotcord[[ii]])[1])+
# geom_text(aes(X1, X2,label=elements),size=4, data = plotcor)+
# 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
plots[[ii]] = pnet
}
pdf(file = "cs.pdf",width = 12,height = 18)
multiplot(plotlist=plots,cols=3)
dev.off()
image
到这里我们就可以将微生物生态网络移植到ggolot中
这里我选择合适和layout布局方式，使用google网上构造的拼图工具，结合ggolot的微生物生态网络图
这里我做了细菌和真菌的五个处理的网络，隐去标签。
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备注：代码略长，此处略去；
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