数据准备
library('statnet')
library('circlize')
df<-read.table("taxa_L2.txt", header=T,row.names = 1) #核磁数据，行为有机碳成分，列为样品
df= t(df)
map = read.table('map.txt', row.names = 1, header = T) #样点分组信息，行为样品，列为分组属性
df= df[rownames(map),] #数据匹配，同时对样品进行排序
library('dplyr')
metadata = cbind(df,map[,2:3]) %>% #将两文件合并
aggregate(.~ Week*Site,data = ., mean) #分组求均值
1.1 选取一组样点进行分析
CS = subset(metadata,Site == 'CS')
rownames(CS) = CS$Week
CS = CS[,-(1:2)]
mat1<-as.matrix(CS) # 矩阵化
# 手动设置行列名（可选）
# rownames(my.data) <-c("CCK", "CNPK", "GCCK", "GCNPK")
# colnames(my.data) <-c("Alphaproteobacteria","Betaproteobacteria","Gammaproteobacteria",
# "Deltaproteobacteria","Acidobacteria","Actinobacteria",
# "Bacteroidetes","Chloroflexi","Firmicutes",
# "Gemmatimonadetes","Planctomycetes","Thaumarchaeota" ,
# "Verrucomicrobia","Ascomycota", "Basidiomycota",
# "Zygomycota")
1.2 颜色设定
grid.col = NULL
# 定义处理的颜色，这里随便选取了4个颜色，大家可以根据自己的喜好制定好看的配色
grid.col[c("W1", "W2", "W4", "W8","W16")] = c("blue", "black", "orange", "chocolate","pink")
# 定义微生物各个门的颜色，
grid.col[colnames(mat1)] = c("lavender", "khaki","mistyrose",
"sienna1", "skyblue", "brown1","maroon")
1.3 初步成图
# jpeg(file="circlize.jpg", width=4000, height=3000, pointsize=8,units = 'px',res = 600)
#jpeg()与dev.off()合用，可将图直接导出而不在绘图区展示
# 参数设置
circos.par(gap.degree = c(rep(2, nrow(mat1)-1), 10, rep(2, ncol(mat1)-1), 10), #设置各个扇叶的间距
start.degree = -95) #设置起始位置，0处于时钟3点处
# 出图，本人这里只用了少部分参数，所有参数见此包的help文档，或者看下文
chordDiagram(mat1,
order = c('W1','W2','W4','W8','W16',
'Alkyl_C','O-CH3/NCH','O-alkyl_C','O-C-O_anomeric_C',
'Aromatic_C','Aromatic_C-O','Carbonyl'),
annotationTrack = c('grid','name','axis'), #依次为外环的阴影，名称和外围坐标轴，可选择显示或者不显示
directional = TRUE,
diffHeight = 0,
grid.col = grid.col,
transparency = 0.5)
# 图例制作
legend("topright",pch=20,legend=colnames(mat1),
col=grid.col[colnames(mat1)],bty="n",
cex=1,pt.cex=3,border="black",title = 'NMR',title.col = 'red') # 设定核磁成分的图例
legend("topleft",pch=20,legend= c('1','2','4','8','16'),
col=grid.col[c('W1','W2','W4','W8','W16')],bty="n",
cex=1,pt.cex=3,border="black",title = 'Week',title.col = 'blue') # 设定样品分组图例
abline(v = 0, lty = 2, lwd = 2, col = "#00000080") #添加横线，将样品和核磁成分区分开
circos.clear() #清空设置
# 绘图结束后写入文件
# dev.off()
我们目前预览的图看，图例和图区严重重叠。是因为绘图区空间的限制。我们只需在图片输出时调置图片宽大于高即可解决。
2.1 针对所有样点
rownames(metadata) = paste0(rep(c('CQ','CS','YT'),each = 5),c(1,16,2,4,8))
mat = as.matrix(metadata[,-(1:2)])
####颜色设定
grid.col = NULL
# 定义处理的颜色，这里随便选取了5个颜色，大家可以根据自己的喜好制定好看的配色
grid.col[rownames(metadata)] = rep(c("blue", "black", "orange", "chocolate","pink"),3)
# 定义微生物各个门的颜色，
grid.col[colnames(mat)] = c("lavender", "khaki","mistyrose",
"sienna1", "skyblue", "brown1","maroon")
# 定义各组件的间隔，同样点的week间距离较小，不同样点距离较大
circos.par(gap.after = c(rep(c(rep(1, nrow(mat)/3-1), 8), 3),rep(1,ncol(mat)-1),8),
start.degree = 0) #定义起始点的位置，90为时钟12点的位置，逆时针排序
2.2 成图
# 若需输出图片，则取消jpeg() 和 dev.off()的注释符
#jpeg(file="circlize-2.jpg", width=4000, height=3000, pointsize=8,units = 'px',res = 600)
chordDiagram(mat,
annotationTrack = c('grid','axis'),
directional = TRUE,
diffHeight = 0,
grid.col = grid.col,
transparency = 0.5,
# 定义出图顺序，从时钟的3点出开始
order = c(paste0(rep(c('CQ','CS','YT'),each = 5),c(1,2,4,8,16)),
'Alkyl_C','O-CH3/NCH','O-alkyl_C','O-C-O_anomeric_C',
'Aromatic_C','Aromatic_C-O','Carbonyl'),
preAllocateTracks = list(
track.height = uh(4, "mm"),
track.margin = c(uh(4, "mm"), 0))
)
# 图例制作
legend(x = 1, y = 1,pch=20,legend=colnames(mat),
col=grid.col[colnames(mat)],bty="n",text.font = 2,
cex=1,pt.cex=3,title = 'NMR',title.col = 'red',title.adj = 0) # 设定图例
# 注意这里的颜色顺序和grid.col[rownames(mat)]中相对应
legend(x = 1,y = -0.3,pch=15,
legend= c('1','2','4','8','16'),
col=c("blue", "orange", "chocolate","pink","black"),
bty="n",text.font = 2,cex=1,
pt.cex=3,pt.bg = 'white',title = 'Week',title.col = 'blue',title.adj = 0) # 设定图例
abline(h = 0.05, lty = 2, lwd = 2, col = "#00000080")
2.3 在圆环外围添加扇形进行分组
#定义后续图层叠加在圆环的第二层进行
circos.track(track.index = 2, panel.fun = function(x, y) {
sector.index = get.cell.meta.data("sector.index")
xlim = get.cell.meta.data("xlim")
ylim = get.cell.meta.data("ylim")
# circos.text(mean(xlim), mean(ylim), sector.index, cex = 0.6, niceFacing = TRUE)
}, bg.border = NA)
# 在外围添加扇形进行分组
highlight.sector(paste0('CS',c(1,2,4,8,16)), track.index = 1, col = "red",
text = "CS", cex = 0.8, text.col = "black",font= 2,niceFacing = TRUE)
highlight.sector(paste0('CQ',c(1,2,4,8,16)), track.index = 1, col = "green",
text = "CQ", cex = 0.8, text.col = "black",font= 2,niceFacing = TRUE)
highlight.sector(paste0('YT',c(1,2,4,8,16)), track.index = 1, col = "blue",
text = "YT", cex = 0.8, text.col = "black", font= 2,niceFacing = TRUE)
highlight.sector(colnames(mat), track.index = 1, col = "purple",
text = "Carbon Composition", cex = 1, font= 2,text.col = "black",niceFacing = TRUE)
circos.clear() #回复出厂设置
#dev.off()
除了颜值高点，看着都费劲啊，可读性不如堆积图。
这不是科学应有之道，不可盲目追求花里胡哨的莫须有，而应求真求实。
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