如何根据定义的组为树状图的标签着色？ （在R）
How to color a dendrogram's labels according to defined groups? (in R)
我在 R 中有一个包含 24 行和 10,000 列的数字矩阵。这个矩阵的行名基本上是文件名，我从中读取了与 24 行中每一行对应的数据。除此之外，我还有一个包含 24 个整体的单独因子列表，指定了这 24 个文件所属的组。有 3 组 - 醇类、碳氢化合物和酯类。他们所属的名称和相应的组如下所示：
> MS.mz
[1] "int-354.19" "int-361.35" "int-368.35" "int-396.38" "int-408.41" "int-410.43" "int-422.43"
[8] "int-424.42" "int-436.44" "int-438.46" "int-452.00" "int-480.48" "int-648.64" "int-312.14"
[15] "int-676.68" "int-690.62" "int-704.75" "int-312.29" "int-326.09" "int-326.18" "int-326.31"
[22] "int-340.21" "int-340.32" "int-352.35"
> MS.groups
[1] Alcohol Alcohol Alcohol Alcohol Hydrocarbon Alcohol Hydrocarbon Alcohol
[9] Hydrocarbon Alcohol Alcohol Alcohol Ester Alcohol Ester Ester
[17] Ester Alcohol Alcohol Alcohol Alcohol Alcohol Alcohol Hydrocarbon
Levels: Alcohol Ester Hydrocarbon
我想生成一个树状图来查看矩阵中的数据如何聚类。所以，我使用了以下命令：
require(vegan)
dist.mat<-vegdist(MS.data.scaled.transposed,method="euclidean")
clust.res<-hclust(dist.mat)
plot(clust.res)
我得到了树状图。现在我想根据它们所属的组（即酒精、碳氢化合物或酯）为树状图中的文件名着色。我查看了论坛上发布的不同示例，例如
Label and color leaf dendrogram in r
Label and color leaf dendrogram in R using ape package
，但无法为我的数据实现它。我不确定如何将 row.names 与 MS.groups 相关联以获得树状图中的彩色名称。
在使用 dendextend 生成树时（如 https://nycdatascience.com/wp-content/uploads/2013/09/dendextend-tutorial.pdf 中所述），我得到以下树
这是用于生成它的代码：
require(colorspace)
d_SIMS <- dist(firstpointsample5[,-1])
hc_SIMS <- hclust(d_SIMS)
labels(hc_SIMS)
dend_SIMS <- as.dendrogram(hc_SIMS)
SIMS_groups <- rev(levels(firstpointsample5[, 1]))
dend_SIMS <- color_branches(dend_SIMS, k = 3, groupLabels = SIMS_groups)
is.character(labels(dend_SIMS))
plot(dend_SIMS)
labels_colors(dend_SIMS) <- rainbow_hcl(3)[sort_levels_values(as.numeric(firstpointsample5[,1])[order.dendrogram(dend_SIMS)])]
labels(dend_SIMS) <- paste(as.character(firstpointsample5[, 1])[order.dendrogram(dend_SIMS)],"(", labels(dend_SIMS), ")", sep = "")
dend_SIMS <- hang.dendrogram(dend_SIMS, hang_height = 0.1)
dend_SIMS <- assign_values_to_leaves_nodePar(dend_SIMS, 0.5,"lab.cex")
par(mar = c(3, 3, 3, 7))
plot(dend_SIMS, main = "Clustered SIMS dataset\n (the labels give the true m/z groups)",horiz = TRUE, nodePar = list(cex = 0.007))
legend("topleft", legend = SIMS_groups, fill = rainbow_hcl(3))
你可以看看这个教程，它显示了在 R 中按组可视化树状图的几种解决方案
https://rstudio-pubs-static.s3.amazonaws.com/1876_df0bf890dd54461f98719b461d987c3d.html
但是，我认为最适合您的数据的解决方案是由包 'dendextend' 提供的。请参阅教程（关于 'iris' 数据集的示例，与您的问题类似）：https://nycdatascience.com/wp-content/uploads/2013/09/dendextend-tutorial.pdf
另见小插图：http://cran.r-project.org/web/packages/dendextend/vignettes/Cluster_Analysis.html
您可以尝试此解决方案，只需将 'labs' 更改为 'MS.groups' 并将 'var' 更改为 'MS.groups' 转换为数字（也许，使用 as.numeric ).
来自How to colour the labels of a dendrogram by an additional factor variable in R
## The data
df <- structure(list(labs = c("a1", "a2", "a3", "a4", "a5", "a6", "a7",
"a8", "b1", "b2", "b3", "b4", "b5", "b6", "b7"), var = c(1L, 1L, 2L,
1L,2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L), td = c(13.1, 14.5, 16.7,
12.9, 14.9, 15.6, 13.4, 15.3, 12.8, 14.5, 14.7, 13.1, 14.9, 15.6, 14.6),
fd = c(2L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 4L, 2L, 1L, 4L, 3L, 3L)),
.Names = c("labs", "var", "td", "fd"), class = "data.frame", row.names =
c(NA, -15L))
## Subset for clustering
df.nw = df[,3:4]
# Assign the labs column to a vector
labs = df$labs
d = dist(as.matrix(df.nw)) # find distance matrix
hc = hclust(d, method="complete") # apply hierarchical clustering
## plot the dendrogram
plot(hc, hang=-0.01, cex=0.6, labels=labs, xlab="")
## convert hclust to dendrogram
hcd = as.dendrogram(hc)
## plot using dendrogram object
plot(hcd, cex=0.6)
Var = df$var # factor variable for colours
varCol = gsub("1","red",Var) # convert numbers to colours
varCol = gsub("2","blue",varCol)
# colour-code dendrogram branches by a factor
# ... your code
colLab <- function(n) {
if(is.leaf(n)) {
a <- attributes(n)
attr(n, "label") <- labs[a$label]
attr(n, "nodePar") <- c(a$nodePar, lab.col = varCol[a$label])
}
n
}
## Coloured plot
plot(dendrapply(hcd, colLab))
我怀疑您要查找的函数是 color_labels 或 get_leaves_branches_col。第一个基于 cutree 给标签上色（就像 color_branches 做的那样），第二个让你得到每片叶子的分支的颜色，然后用它来给树的标签上色（如果您使用不寻常的方法为分支着色（就像使用 branches_attr_by_labels 时发生的那样）。例如：
# define dendrogram object to play with:
hc <- hclust(dist(USArrests[1:5,]), "ave")
dend <- as.dendrogram(hc)
library(dendextend)
par(mfrow = c(1,2), mar = c(5,2,1,0))
dend <- dend %>%
color_branches(k = 3) %>%
set("branches_lwd", c(2,1,2)) %>%
set("branches_lty", c(1,2,1))
plot(dend)
dend <- color_labels(dend, k = 3)
# The same as:
# labels_colors(dend) <- get_leaves_branches_col(dend)
plot(dend)
无论哪种方式，您都应该经常查看 set 函数，了解可以对您的树状图进行哪些操作（这样可以省去记住所有不同函数名称的麻烦）。
我在 R 中有一个包含 24 行和 10,000 列的数字矩阵。这个矩阵的行名基本上是文件名，我从中读取了与 24 行中每一行对应的数据。除此之外，我还有一个包含 24 个整体的单独因子列表，指定了这 24 个文件所属的组。有 3 组 - 醇类、碳氢化合物和酯类。他们所属的名称和相应的组如下所示：
> MS.mz
[1] "int-354.19" "int-361.35" "int-368.35" "int-396.38" "int-408.41" "int-410.43" "int-422.43"
[8] "int-424.42" "int-436.44" "int-438.46" "int-452.00" "int-480.48" "int-648.64" "int-312.14"
[15] "int-676.68" "int-690.62" "int-704.75" "int-312.29" "int-326.09" "int-326.18" "int-326.31"
[22] "int-340.21" "int-340.32" "int-352.35"
> MS.groups
[1] Alcohol Alcohol Alcohol Alcohol Hydrocarbon Alcohol Hydrocarbon Alcohol
[9] Hydrocarbon Alcohol Alcohol Alcohol Ester Alcohol Ester Ester
[17] Ester Alcohol Alcohol Alcohol Alcohol Alcohol Alcohol Hydrocarbon
Levels: Alcohol Ester Hydrocarbon
我想生成一个树状图来查看矩阵中的数据如何聚类。所以，我使用了以下命令：
require(vegan)
dist.mat<-vegdist(MS.data.scaled.transposed,method="euclidean")
clust.res<-hclust(dist.mat)
plot(clust.res)
我得到了树状图。现在我想根据它们所属的组（即酒精、碳氢化合物或酯）为树状图中的文件名着色。我查看了论坛上发布的不同示例，例如
Label and color leaf dendrogram in r
Label and color leaf dendrogram in R using ape package
，但无法为我的数据实现它。我不确定如何将 row.names 与 MS.groups 相关联以获得树状图中的彩色名称。
在使用 dendextend 生成树时（如 https://nycdatascience.com/wp-content/uploads/2013/09/dendextend-tutorial.pdf 中所述），我得到以下树
这是用于生成它的代码：
require(colorspace)
d_SIMS <- dist(firstpointsample5[,-1])
hc_SIMS <- hclust(d_SIMS)
labels(hc_SIMS)
dend_SIMS <- as.dendrogram(hc_SIMS)
SIMS_groups <- rev(levels(firstpointsample5[, 1]))
dend_SIMS <- color_branches(dend_SIMS, k = 3, groupLabels = SIMS_groups)
is.character(labels(dend_SIMS))
plot(dend_SIMS)
labels_colors(dend_SIMS) <- rainbow_hcl(3)[sort_levels_values(as.numeric(firstpointsample5[,1])[order.dendrogram(dend_SIMS)])]
labels(dend_SIMS) <- paste(as.character(firstpointsample5[, 1])[order.dendrogram(dend_SIMS)],"(", labels(dend_SIMS), ")", sep = "")
dend_SIMS <- hang.dendrogram(dend_SIMS, hang_height = 0.1)
dend_SIMS <- assign_values_to_leaves_nodePar(dend_SIMS, 0.5,"lab.cex")
par(mar = c(3, 3, 3, 7))
plot(dend_SIMS, main = "Clustered SIMS dataset\n (the labels give the true m/z groups)",horiz = TRUE, nodePar = list(cex = 0.007))
legend("topleft", legend = SIMS_groups, fill = rainbow_hcl(3))
你可以看看这个教程，它显示了在 R 中按组可视化树状图的几种解决方案
https://rstudio-pubs-static.s3.amazonaws.com/1876_df0bf890dd54461f98719b461d987c3d.html
但是，我认为最适合您的数据的解决方案是由包 'dendextend' 提供的。请参阅教程（关于 'iris' 数据集的示例，与您的问题类似）：https://nycdatascience.com/wp-content/uploads/2013/09/dendextend-tutorial.pdf
另见小插图：http://cran.r-project.org/web/packages/dendextend/vignettes/Cluster_Analysis.html
您可以尝试此解决方案，只需将 'labs' 更改为 'MS.groups' 并将 'var' 更改为 'MS.groups' 转换为数字（也许，使用 as.numeric ). 来自How to colour the labels of a dendrogram by an additional factor variable in R
## The data
df <- structure(list(labs = c("a1", "a2", "a3", "a4", "a5", "a6", "a7",
"a8", "b1", "b2", "b3", "b4", "b5", "b6", "b7"), var = c(1L, 1L, 2L,
1L,2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L), td = c(13.1, 14.5, 16.7,
12.9, 14.9, 15.6, 13.4, 15.3, 12.8, 14.5, 14.7, 13.1, 14.9, 15.6, 14.6),
fd = c(2L, 3L, 3L, 1L, 2L, 3L, 2L, 3L, 2L, 4L, 2L, 1L, 4L, 3L, 3L)),
.Names = c("labs", "var", "td", "fd"), class = "data.frame", row.names =
c(NA, -15L))
## Subset for clustering
df.nw = df[,3:4]
# Assign the labs column to a vector
labs = df$labs
d = dist(as.matrix(df.nw)) # find distance matrix
hc = hclust(d, method="complete") # apply hierarchical clustering
## plot the dendrogram
plot(hc, hang=-0.01, cex=0.6, labels=labs, xlab="")
## convert hclust to dendrogram
hcd = as.dendrogram(hc)
## plot using dendrogram object
plot(hcd, cex=0.6)
Var = df$var # factor variable for colours
varCol = gsub("1","red",Var) # convert numbers to colours
varCol = gsub("2","blue",varCol)
# colour-code dendrogram branches by a factor
# ... your code
colLab <- function(n) {
if(is.leaf(n)) {
a <- attributes(n)
attr(n, "label") <- labs[a$label]
attr(n, "nodePar") <- c(a$nodePar, lab.col = varCol[a$label])
}
n
}
## Coloured plot
plot(dendrapply(hcd, colLab))
我怀疑您要查找的函数是 color_labels 或 get_leaves_branches_col。第一个基于 cutree 给标签上色（就像 color_branches 做的那样），第二个让你得到每片叶子的分支的颜色，然后用它来给树的标签上色（如果您使用不寻常的方法为分支着色（就像使用 branches_attr_by_labels 时发生的那样）。例如：
# define dendrogram object to play with:
hc <- hclust(dist(USArrests[1:5,]), "ave")
dend <- as.dendrogram(hc)
library(dendextend)
par(mfrow = c(1,2), mar = c(5,2,1,0))
dend <- dend %>%
color_branches(k = 3) %>%
set("branches_lwd", c(2,1,2)) %>%
set("branches_lty", c(1,2,1))
plot(dend)
dend <- color_labels(dend, k = 3)
# The same as:
# labels_colors(dend) <- get_leaves_branches_col(dend)
plot(dend)
无论哪种方式，您都应该经常查看 set 函数，了解可以对您的树状图进行哪些操作（这样可以省去记住所有不同函数名称的麻烦）。