加入树状图和热图
[英]Joining a dendrogram and a heatmap
问题描述
我有一个heatmap （来自一组样本的基因表达）：
set.seed(10)
mat <- matrix(rnorm(24*10,mean=1,sd=2),nrow=24,ncol=10,dimnames=list(paste("g",1:24,sep=""),paste("sample",1:10,sep="")))
dend <- as.dendrogram(hclust(dist(mat)))
row.ord <- order.dendrogram(dend)
mat <- matrix(mat[row.ord,],nrow=24,ncol=10,dimnames=list(rownames(mat)[row.ord],colnames(mat)))
mat.df <- reshape2::melt(mat,value.name="expr",varnames=c("gene","sample"))
require(ggplot2)
map1.plot <- ggplot(mat.df,aes(x=sample,y=gene))+geom_tile(aes(fill=expr))+scale_fill_gradient2("expr",high="darkred",low="darkblue")+scale_y_discrete(position="right")+
theme_bw()+theme(plot.margin=unit(c(1,1,1,-1),"cm"),legend.key=element_blank(),legend.position="right",axis.text.y=element_blank(),axis.ticks.y=element_blank(),panel.border=element_blank(),strip.background=element_blank(),axis.text.x=element_text(angle=45,hjust=1,vjust=1),legend.text=element_text(size=5),legend.title=element_text(size=8),legend.key.size=unit(0.4,"cm"))
（由于我正在使用的plot.margin参数，左侧被切断，但我需要这个，如下所示）。
然后我根据深度截止值prune行dendrogram以获得更少的聚类（即，只进行深度分割），并对得到的dendrogram进行一些编辑，以便按照我想要的方式绘制它：
depth.cutoff <- 11
dend <- cut(dend,h=depth.cutoff)$upper
require(dendextend)
gg.dend <- as.ggdend(dend)
leaf.heights <- dplyr::filter(gg.dend$nodes,!is.na(leaf))$height
leaf.seqments.idx <- which(gg.dend$segments$yend %in% leaf.heights)
gg.dend$segments$yend[leaf.seqments.idx] <- max(gg.dend$segments$yend[leaf.seqments.idx])
gg.dend$segments$col[leaf.seqments.idx] <- "black"
gg.dend$labels$label <- 1:nrow(gg.dend$labels)
gg.dend$labels$y <- max(gg.dend$segments$yend[leaf.seqments.idx])
gg.dend$labels$x <- gg.dend$segments$x[leaf.seqments.idx]
gg.dend$labels$col <- "black"
dend1.plot <- ggplot(gg.dend,labels=F)+scale_y_reverse()+coord_flip()+theme(plot.margin=unit(c(1,-3,1,1),"cm"))+annotate("text",size=5,hjust=0,x=gg.dend$label$x,y=gg.dend$label$y,label=gg.dend$label$label,colour=gg.dend$label$col)
我使用
cowplot的plot_grid它们绘制在一起：
require(cowplot)
plot_grid(dend1.plot,map1.plot,align='h',rel_widths=c(0.5,1))
虽然align='h'正在运行，但并不完美。
使用map1.plot用plot_grid绘制未切割的dendrogram ，说明了这一点：
dend0.plot <- ggplot(as.ggdend(dend))+scale_y_reverse()+coord_flip()+theme(plot.margin=unit(c(1,-1,1,1),"cm"))
plot_grid(dend0.plot,map1.plot,align='h',rel_widths=c(1,1))
dendrogram顶部和底部的分支似乎被压向中心。 使用scale似乎是一种调整它的方法，但是比例值似乎是特定于数字的，所以我想知道是否有任何方式以更有原则的方式做到这一点。
接下来，我对heatmap每个群集进行一些术语丰富度分析。 假设这个分析给了我这个data.frame ：
enrichment.df <- data.frame(term=rep(paste("t",1:10,sep=""),nrow(gg.dend$labels)),
cluster=c(sapply(1:nrow(gg.dend$labels),function(i) rep(i,5))),
score=rgamma(10*nrow(gg.dend$labels),0.2,0.7),
stringsAsFactors = F)
我想要做的是将这个data.frame为heatmap并将切割的dendrogram放在它下面（类似于它放置在表达式heatmap的左侧）。
所以我再次尝试使用plot_grid ，认为align='v'可以在这里工作：
首先重新生成树状图，使其面朝上：
dend2.plot <- ggplot(gg.dend,labels=F)+scale_y_reverse()+theme(plot.margin=unit(c(-3,1,1,1),"cm"))
现在尝试将它们一起绘制：
plot_grid(map2.plot,dend2.plot,align='v')
如图所示， plot_grid似乎无法对齐它们并且它会抛出警告消息：
In align_plots(plotlist = plots, align = align) :
Graphs cannot be vertically aligned. Placing graphs unaligned.
似乎接近的是：
plot_grid(map2.plot,dend2.plot,rel_heights=c(1,0.5),nrow=2,ncol=1,scale=c(1,0.675))
这是在使用scale参数后实现的，尽管情节太宽了。 所以，我想知道是否有办法绕过它或者以某种方式预先确定任何给定的dendrogram和heatmap列表的正确scale ，可能是它们的尺寸。
2 个解决方案
解决方案1
11 已采纳 2017-02-21 23:25:29
前段时间我遇到了同样的问题。 我使用的基本技巧是在给出树形图的结果的情况下直接指定基因的位置。 为简单起见，首先是绘制完整树状图的情况：
# For the full dendrogram
library(plyr)
library(reshape2)
library(dplyr)
library(ggplot2)
library(ggdendro)
library(gridExtra)
library(dendextend)
set.seed(10)
# The source data
mat <- matrix(rnorm(24 * 10, mean = 1, sd = 2),
nrow = 24, ncol = 10,
dimnames = list(paste("g", 1:24, sep = ""),
paste("sample", 1:10, sep = "")))
sample_names <- colnames(mat)
# Obtain the dendrogram
dend <- as.dendrogram(hclust(dist(mat)))
dend_data <- dendro_data(dend)
# Setup the data, so that the layout is inverted (this is more
# "clear" than simply using coord_flip())
segment_data <- with(
segment(dend_data),
data.frame(x = y, y = x, xend = yend, yend = xend))
# Use the dendrogram label data to position the gene labels
gene_pos_table <- with(
dend_data$labels,
data.frame(y_center = x, gene = as.character(label), height = 1))
# Table to position the samples
sample_pos_table <- data.frame(sample = sample_names) %>%
mutate(x_center = (1:n()),
width = 1)
# Neglecting the gap parameters
heatmap_data <- mat %>%
reshape2::melt(value.name = "expr", varnames = c("gene", "sample")) %>%
left_join(gene_pos_table) %>%
left_join(sample_pos_table)
# Limits for the vertical axes
gene_axis_limits <- with(
gene_pos_table,
c(min(y_center - 0.5 * height), max(y_center + 0.5 * height))
) +
0.1 * c(-1, 1) # extra spacing: 0.1
# Heatmap plot
plt_hmap <- ggplot(heatmap_data,
aes(x = x_center, y = y_center, fill = expr,
height = height, width = width)) +
geom_tile() +
scale_fill_gradient2("expr", high = "darkred", low = "darkblue") +
scale_x_continuous(breaks = sample_pos_table$x_center,
labels = sample_pos_table$sample,
expand = c(0, 0)) +
# For the y axis, alternatively set the labels as: gene_position_table$gene
scale_y_continuous(breaks = gene_pos_table[, "y_center"],
labels = rep("", nrow(gene_pos_table)),
limits = gene_axis_limits,
expand = c(0, 0)) +
labs(x = "Sample", y = "") +
theme_bw() +
theme(axis.text.x = element_text(size = rel(1), hjust = 1, angle = 45),
# margin: top, right, bottom, and left
plot.margin = unit(c(1, 0.2, 0.2, -0.7), "cm"),
panel.grid.minor = element_blank())
# Dendrogram plot
plt_dendr <- ggplot(segment_data) +
geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
scale_x_reverse(expand = c(0, 0.5)) +
scale_y_continuous(breaks = gene_pos_table$y_center,
labels = gene_pos_table$gene,
limits = gene_axis_limits,
expand = c(0, 0)) +
labs(x = "Distance", y = "", colour = "", size = "") +
theme_bw() +
theme(panel.grid.minor = element_blank())
library(cowplot)
plot_grid(plt_dendr, plt_hmap, align = 'h', rel_widths = c(1, 1))
请注意，我在热图图表的左侧保持y轴刻度，只是为了显示树状图和刻度线完全匹配。
现在，对于剪切树状图的情况，应该记住，树状图的叶子将不再以对应于给定聚类中的基因的确切位置结束。 为了获得基因和簇的位置，需要从切割完整的树形图中的两个树状图中提取数据。 总的来，为了澄清簇中的基因，我添加了划分簇的矩形。
# For the cut dendrogram
library(plyr)
library(reshape2)
library(dplyr)
library(ggplot2)
library(ggdendro)
library(gridExtra)
library(dendextend)
set.seed(10)
# The source data
mat <- matrix(rnorm(24 * 10, mean = 1, sd = 2),
nrow = 24, ncol = 10,
dimnames = list(paste("g", 1:24, sep = ""),
paste("sample", 1:10, sep = "")))
sample_names <- colnames(mat)
# Obtain the dendrogram
full_dend <- as.dendrogram(hclust(dist(mat)))
# Cut the dendrogram
depth_cutoff <- 11
h_c_cut <- cut(full_dend, h = depth_cutoff)
dend_cut <- as.dendrogram(h_c_cut$upper)
dend_cut <- hang.dendrogram(dend_cut)
# Format to extend the branches (optional)
dend_cut <- hang.dendrogram(dend_cut, hang = -1)
dend_data_cut <- dendro_data(dend_cut)
# Extract the names assigned to the clusters (e.g., "Branch 1", "Branch 2", ...)
cluster_names <- as.character(dend_data_cut$labels$label)
# Extract the names of the haplotypes that belong to each group (using
# the 'labels' function)
lst_genes_in_clusters <- h_c_cut$lower %>%
lapply(labels) %>%
setNames(cluster_names)
# Setup the data, so that the layout is inverted (this is more
# "clear" than simply using coord_flip())
segment_data <- with(
segment(dend_data_cut),
data.frame(x = y, y = x, xend = yend, yend = xend))
# Extract the positions of the clusters (by getting the positions of the
# leafs); data is already in the same order as in the cluster name
cluster_positions <- segment_data[segment_data$xend == 0, "y"]
cluster_pos_table <- data.frame(y_position = cluster_positions,
cluster = cluster_names)
# Specify the positions for the genes, accounting for the clusters
gene_pos_table <- lst_genes_in_clusters %>%
ldply(function(ss) data.frame(gene = ss), .id = "cluster") %>%
mutate(y_center = 1:nrow(.),
height = 1)
# > head(gene_pos_table, 3)
# cluster gene y_center height
# 1 Branch 1 g11 1 1
# 2 Branch 1 g20 2 1
# 3 Branch 1 g12 3 1
# Table to position the samples
sample_pos_table <- data.frame(sample = sample_names) %>%
mutate(x_center = 1:nrow(.),
width = 1)
# Coordinates for plotting rectangles delimiting the clusters: aggregate
# over the positions of the genes in each cluster
cluster_delim_table <- gene_pos_table %>%
group_by(cluster) %>%
summarize(y_min = min(y_center - 0.5 * height),
y_max = max(y_center + 0.5 * height)) %>%
as.data.frame() %>%
mutate(x_min = with(sample_pos_table, min(x_center - 0.5 * width)),
x_max = with(sample_pos_table, max(x_center + 0.5 * width)))
# Neglecting the gap parameters
heatmap_data <- mat %>%
reshape2::melt(value.name = "expr", varnames = c("gene", "sample")) %>%
left_join(gene_pos_table) %>%
left_join(sample_pos_table)
# Limits for the vertical axes (genes / clusters)
gene_axis_limits <- with(
gene_pos_table,
c(min(y_center - 0.5 * height), max(y_center + 0.5 * height))
) +
0.1 * c(-1, 1) # extra spacing: 0.1
# Heatmap plot
plt_hmap <- ggplot(heatmap_data,
aes(x = x_center, y = y_center, fill = expr,
height = height, width = width)) +
geom_tile() +
geom_rect(data = cluster_delim_table,
aes(xmin = x_min, xmax = x_max, ymin = y_min, ymax = y_max),
fill = NA, colour = "black", inherit.aes = FALSE) +
scale_fill_gradient2("expr", high = "darkred", low = "darkblue") +
scale_x_continuous(breaks = sample_pos_table$x_center,
labels = sample_pos_table$sample,
expand = c(0.01, 0.01)) +
scale_y_continuous(breaks = gene_pos_table$y_center,
labels = gene_pos_table$gene,
limits = gene_axis_limits,
expand = c(0, 0),
position = "right") +
labs(x = "Sample", y = "") +
theme_bw() +
theme(axis.text.x = element_text(size = rel(1), hjust = 1, angle = 45),
# margin: top, right, bottom, and left
plot.margin = unit(c(1, 0.2, 0.2, -0.1), "cm"),
panel.grid.minor = element_blank())
# Dendrogram plot
plt_dendr <- ggplot(segment_data) +
geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
scale_x_reverse(expand = c(0, 0.5)) +
scale_y_continuous(breaks = cluster_pos_table$y_position,
labels = cluster_pos_table$cluster,
limits = gene_axis_limits,
expand = c(0, 0)) +
labs(x = "Distance", y = "", colour = "", size = "") +
theme_bw() +
theme(panel.grid.minor = element_blank())
library(cowplot)
plot_grid(plt_dendr, plt_hmap, align = 'h', rel_widths = c(1, 1.8))
解决方案2
1 2017-03-04 14:07:27
这是一个基因和样本树状图的（暂定）解决方案。 这是一个相当缺乏的解决方案，因为我没有设法找到一个好的方法来使plot_grid正确对齐所有子图，同时自动调整图比例和子图之间的距离。 在这个版本中，产生整体图形的方法是添加“填充子图”（调用plot_grid的侧翼NULL条目）并且还手动微调子图的边缘（奇怪地看起来是耦合的）在各个子图中）。 再一次，这是一个相当缺乏的解决方案，希望我能尽快发布一个明确的版本。
library(plyr)
library(reshape2)
library(dplyr)
library(ggplot2)
library(ggdendro)
library(gridExtra)
library(dendextend)
set.seed(10)
# The source data
mat <- matrix(rnorm(24 * 10, mean = 1, sd = 2),
nrow = 24, ncol = 10,
dimnames = list(paste("g", 1:24, sep = ""),
paste("sample", 1:10, sep = "")))
getDendrogram <- function(data_mat, depth_cutoff) {
# Obtain the dendrogram
full_dend <- as.dendrogram(hclust(dist(data_mat)))
# Cut the dendrogram
h_c_cut <- cut(full_dend, h = depth_cutoff)
dend_cut <- as.dendrogram(h_c_cut$upper)
dend_cut <- hang.dendrogram(dend_cut)
# Format to extend the branches (optional)
dend_cut <- hang.dendrogram(dend_cut, hang = -1)
dend_data_cut <- dendro_data(dend_cut)
# Extract the names assigned to the clusters (e.g., "Branch 1", "Branch 2", ...)
cluster_names <- as.character(dend_data_cut$labels$label)
# Extract the entries that belong to each group (using the 'labels' function)
lst_entries_in_clusters <- h_c_cut$lower %>%
lapply(labels) %>%
setNames(cluster_names)
# The dendrogram data for plotting
segment_data <- segment(dend_data_cut)
# Extract the positions of the clusters (by getting the positions of the
# leafs); data is already in the same order as in the cluster name
cluster_positions <- segment_data[segment_data$yend == 0, "x"]
cluster_pos_table <- data.frame(position = cluster_positions,
cluster = cluster_names)
list(
full_dend = full_dend,
dend_data_cut = dend_data_cut,
lst_entries_in_clusters = lst_entries_in_clusters,
segment_data = segment_data,
cluster_pos_table = cluster_pos_table
)
}
# Cut the dendrograms
gene_depth_cutoff <- 11
sample_depth_cutof <- 12
# Obtain the dendrograms
gene_dend_data <- getDendrogram(mat, gene_depth_cutoff)
sample_dend_data <- getDendrogram(t(mat), sample_depth_cutof)
# Specify the positions for the genes and samples, accounting for the clusters
gene_pos_table <- gene_dend_data$lst_entries_in_clusters %>%
ldply(function(ss) data.frame(gene = ss), .id = "gene_cluster") %>%
mutate(y_center = 1:nrow(.),
height = 1)
# > head(gene_pos_table, 3)
# cluster gene y_center height
# 1 Branch 1 g11 1 1
# 2 Branch 1 g20 2 1
# 3 Branch 1 g12 3 1
# Specify the positions for the samples, accounting for the clusters
sample_pos_table <- sample_dend_data$lst_entries_in_clusters %>%
ldply(function(ss) data.frame(sample = ss), .id = "sample_cluster") %>%
mutate(x_center = 1:nrow(.),
width = 1)
# Neglecting the gap parameters
heatmap_data <- mat %>%
reshape2::melt(value.name = "expr", varnames = c("gene", "sample")) %>%
left_join(gene_pos_table) %>%
left_join(sample_pos_table)
# Limits for the vertical axes (genes / clusters)
axis_spacing <- 0.1 * c(-1, 1)
gene_axis_limits <- with(
gene_pos_table,
c(min(y_center - 0.5 * height), max(y_center + 0.5 * height))) + axis_spacing
sample_axis_limits <- with(
sample_pos_table,
c(min(x_center - 0.5 * width), max(x_center + 0.5 * width))) + axis_spacing
# For some reason, the margin of the various sub-plots end up being "coupled";
# therefore, for now this requires some manual fine-tuning,
# which is obviously not ideal...
# margin: top, right, bottom, and left
margin_specs_hmap <- 1 * c(-2, -1, -1, -2)
margin_specs_gene_dendr <- 1.7 * c(-1, -2, -1, -1)
margin_specs_sample_dendr <- 1.7 * c(-2, -1, -2, -1)
# Heatmap plot
plt_hmap <- ggplot(heatmap_data,
aes(x = x_center, y = y_center, fill = expr,
height = height, width = width)) +
geom_tile() +
scale_fill_gradient2("expr", high = "darkred", low = "darkblue") +
scale_x_continuous(breaks = sample_pos_table$x_center,
labels = sample_pos_table$sample,
expand = c(0.01, 0.01)) +
scale_y_continuous(breaks = gene_pos_table$y_center,
labels = gene_pos_table$gene,
limits = gene_axis_limits,
expand = c(0.01, 0.01),
position = "right") +
labs(x = "Sample", y = "Gene") +
theme_bw() +
theme(axis.text.x = element_text(size = rel(1), hjust = 1, angle = 45),
axis.text.y = element_text(size = rel(0.7)),
legend.position = "none",
plot.margin = unit(margin_specs_hmap, "cm"),
panel.grid.minor = element_blank())
# Dendrogram plots
plt_gene_dendr <- ggplot(gene_dend_data$segment_data) +
geom_segment(aes(x = y, y = x, xend = yend, yend = xend)) + # inverted coordinates
scale_x_reverse(expand = c(0, 0.5)) +
scale_y_continuous(breaks = gene_dend_data$cluster_pos_table$position,
labels = gene_dend_data$cluster_pos_table$cluster,
limits = gene_axis_limits,
expand = c(0, 0)) +
labs(x = "Distance", y = "", colour = "", size = "") +
theme_bw() +
theme(plot.margin = unit(margin_specs_gene_dendr, "cm"),
panel.grid.minor = element_blank())
plt_sample_dendr <- ggplot(sample_dend_data$segment_data) +
geom_segment(aes(x = x, y = y, xend = xend, yend = yend)) +
scale_y_continuous(expand = c(0, 0.5),
position = "right") +
scale_x_continuous(breaks = sample_dend_data$cluster_pos_table$position,
labels = sample_dend_data$cluster_pos_table$cluster,
limits = sample_axis_limits,
position = "top",
expand = c(0, 0)) +
labs(x = "", y = "Distance", colour = "", size = "") +
theme_bw() +
theme(plot.margin = unit(margin_specs_sample_dendr, "cm"),
panel.grid.minor = element_blank(),
axis.text.x = element_text(size = rel(0.8), angle = 45, hjust = 0))
library(cowplot)
final_plot <- plot_grid(
NULL, NULL, NULL, NULL,
NULL, NULL, plt_sample_dendr, NULL,
NULL, plt_gene_dendr, plt_hmap, NULL,
NULL, NULL, NULL, NULL,
nrow = 4, ncol = 4, align = "hv",
rel_heights = c(0.5, 1, 2, 0.5),
rel_widths = c(0.5, 1, 2, 0.5)
)
相似矩阵上的树状图和热图
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