R - heatmap.2：重新排序行和列

Question

我使用gplots包的heatmap.2生成了热图：

library(gplots)
abc <-read.csv(file="abc.txt", header=T, sep="\t", dec=".")
abcm<-as.matrix(abc)
def <-read.csv(file="def.txt", header=T, sep="\t", dec=".")
defm<-as.matrix(def)
mean <-read.csv(file="mean.txt", header=T, sep="\t", dec=".")
meanm<-as.matrix(mean)
distance.row = dist(as.matrix(def), method = "euclidean")
cluster.row = hclust(distance.row, method = "average")
distance.col = dist(t(as.matrix(abc)), method = "euclidean")
cluster.col = hclust(distance.col, method = "average")
my_palette <- colorRampPalette(c("red", "yellow", "green"))(n = 299)

heatmap.2(meanm, trace="none", dendrogram="both", Rowv=as.dendrogram(cluster.row), Colv=as.dendrogram(cluster.col), margins = c(7,7), col=my_palette)

为每个树形图提供两种不同的聚类方法。

现在我想重新排序对象，以便绿色方块形成对角线。我该怎么做？

编辑：我的示例输入

structure(c(1, 0.6798, 0.6604, 0.7101, 0.6771, 0.6725, 0.6696, 
0.6548, 0.676, 0.6811, 0.6798, 1, 0.656, 0.6763, 0.8163, 0.781, 
0.7811, 0.6503, 0.7811, 0.855, 0.6604, 0.656, 1, 0.6532, 0.6498, 
0.6459, 0.6455, 0.7532, 0.6521, 0.6536, 0.7101, 0.6763, 0.6532, 
1, 0.672, 0.669, 0.6669, 0.6517, 0.6748, 0.6786, 0.6771, 0.8163, 
0.6498, 0.672, 1, 0.7828, 0.7838, 0.6441, 0.7736, 0.8227, 0.6725, 
0.781, 0.6459, 0.669, 0.7828, 1, 0.8361, 0.6447, 0.7574, 0.7796, 
0.6696, 0.7811, 0.6455, 0.6669, 0.7838, 0.8361, 1, 0.638, 0.7566, 
0.7772, 0.6548, 0.6503, 0.7532, 0.6517, 0.6441, 0.6447, 0.638, 
1, 0.6563, 0.6459, 0.676, 0.7811, 0.6521, 0.6748, 0.7736, 0.7574, 
0.7566, 0.6563, 1, 0.7778, 0.6811, 0.855, 0.6536, 0.6786, 0.8227, 
0.7796, 0.7772, 0.6459, 0.7778, 1), .Dim = c(10L, 10L), .Dimnames = list(
    c("sp1", "sp2", "sp3", "sp4", "sp5", "sp6", "sp7", "sp8", 
    "sp9", "sp10"), c("sp1", "sp2", "sp3", "sp4", "sp5", "sp6", 
    "sp7", "sp8", "sp9", "sp10")))

Answer 1

函数reorder.dendrogram翻转分支。

reorder(as.dendrogram(cluster.col), 10:1)
reorder(as.dendrogram(cluster.row), 10:1)

用于

heatmap.2(meanm, trace="none", dendrogram="both",
Rowv=reorder(as.dendrogram(cluster.row), 10:1),
Colv=reorder(as.dendrogram(cluster.col), 10:1),
margins = c(7,7), breaks=breaks,col=hm.colors, na.color="white",
main="mean(AAI+ANI)", xlab="AAI clustering", ylab="ANI clustering", srtCol=45)