开发笔记:难以定位heatmap.2组件

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我不知道你是否对非基于heatmap.2的解决方案持开放态度。在我看来，ggplot提供了更大的灵活性，通过一些调整，您可以重现类似于您所展示的热像图，同时最大限度地绘制“不动产”并避免过多的空白。

如果你只是在寻找heatmap.2解决方案，我很高兴删除这篇文章。

除此之外，ggplot2解决方案可能如下所示：

首先，让我们生成一些样本数据

set.seed(2018)
df <- as_tibble(matrix(runif(7*10), ncol = 10), .name_repair = ~seq(1:10))


在绘图之前，我们需要将df从宽到长重塑

library(tidyverse)
df <- df %>%
rowid_to_column("row") %>%
gather(col, Correlation, -row) %>%
mutate(col = as.integer(col))


然后去绘图

ggplot(df, aes(row, col, fill = Correlation)) +
geom_tile() +
scale_fill_gradientn(colours = my_palette) + # Use your custom colour palette
theme_void() + # Minimal theme
labs(title = "Main title") +
geom_text(aes(label = sprintf("%2.1f", Correlation)), size = 2) +
theme(
plot.title = element_text(hjust = 1), # Right-aligned text
legend.position="bottom") + # Legend at the bottom
guides(fill = guide_colourbar(
title.position = "bottom", # Legend title below bar
barwidth = 25, # Extend bar length
title.hjust = 0.5))


通过facet_wrap在网格布局中使用多个热图的示例

首先，让我们生成更复杂的数据。

set.seed(2018)
df <- replicate(
4,
as_tibble(matrix(runif(7*10), ncol = 10), .name_repair = ~seq(1:10)), simplify = F) %>%
setNames(., paste("data", 1:4, sep = "")) %>%
map(~ .x %>% rowid_to_column("row") %>%
gather(col, Correlation, -row) %>%
mutate(col = as.integer(col))) %>%
bind_rows(.id = "data")


然后，绘图与我们之前所做的相同，另外还有一个facet_wrap(~data, ncol = 2)声明

ggplot(df, aes(row, col, fill = Correlation)) +
geom_tile() +
scale_fill_gradientn(colours = my_palette) + # Use your custom colour palette
theme_void() + # Minimal theme
labs(title = "Main title") +
geom_text(aes(label = sprintf("%2.1f", Correlation)), size = 2) +
facet_wrap(~ data, ncol = 2) +
theme(
plot.title = element_text(hjust = 1), # Right-aligned text
legend.position="bottom") + # Legend at the bottom
guides(fill = guide_colourbar(
title.position = "bottom", # Legend title below bar
barwidth = 25, # Extend bar length
title.hjust = 0.5))


最后一次更新

我认为看到我们可以走多远的复杂热图，与你的link to from the paper相似，这将是有趣/有趣的。

最后包含样本数据，因为这会占用一些空间。

我们首先构造三个不同的ggplot2绘图对象，显示主热图（gg3），另一个较小的热图（缺少值）（gg2），以及一个条形表示每行的组标签（gg1）。

gg3 <- ggplot(df.cor, aes(col, row, fill = Correlation)) +
geom_tile() +
scale_fill_distiller(palette = "RdYlBu") +
theme_void() +
labs(title = "Main title") +
geom_text(aes(label = sprintf("%2.1f", Correlation)), size = 2) +
scale_y_discrete(position = "right") +
theme(
plot.title = element_text(hjust = 1),
legend.position="bottom",
axis.text.y = element_text(color = "black", size = 10)) +
guides(fill = guide_colourbar(
title.position = "bottom",
barwidth = 10,
title.hjust = 0.5))
gg2 <- ggplot(df.flag, aes(col, row, fill = Correlation)) +
geom_tile(colour = "grey") +
scale_fill_distiller(palette = "RdYlBu", guide = F, na.value = "white") +
theme_void() +
scale_x_discrete(position = "top") +
theme(
axis.text.x = element_text(color = "black", size = 10, angle = 90, hjust = 1, vjust = 0.5))
gg1 <- ggplot(df.bar, aes(1, row, fill = grp)) +
geom_tile() +
scale_fill_manual(values = c("grp1" = "orange", "grp2" = "green")) +
theme_void() +
theme(legend.position = "left")


我们现在可以使用egg::ggarrange定位所有三个图，使y轴范围对齐。

library(egg)
ggarrange(gg1, gg2, gg3, ncol = 3, widths = c(0.1, 1, 3))


样本数据

library(tidyverse)
set.seed(2018)
nrow <- 7
ncol <- 20
df.cor <- matrix(runif(nrow * ncol, min = -1, max = 1), nrow = nrow) %>%
as_tibble(.name_repair = ~seq(1:ncol)) %>%
rowid_to_column("row") %>%
gather(col, Correlation, -row) %>%
mutate(
row = factor(
paste("row", row, sep = ""),
levels = paste("row", 1:nrow, sep = "")),
col = factor(
paste("col", col, sep = ""),
levels = paste("col", 1:ncol, sep = "")))
nrow <- 7
ncol <- 10
df.flag <- matrix(runif(nrow * ncol, min = -1, max = 1), nrow = nrow) %>%
as_tibble(.name_repair = ~seq(1:ncol)) %>%
rowid_to_column("row") %>%
gather(col, Correlation, -row) %>%
mutate(
row = factor(
paste("row", row, sep = ""),
levels = paste("row", 1:nrow, sep = "")),
col = factor(
paste("col", col, sep = ""),
levels = paste("col", 1:ncol, sep = ""))) %>%
mutate(Correlation = ifelse(abs(Correlation) <0.5, NA, Correlation))
df.bar <- data.frame(
row = 1:nrow,
grp = paste("grp", c(rep(1, nrow - 3), rep(2, 3)), sep = "")) %>%
mutate(
row = factor(
paste("row", row, sep = ""),
levels = paste("row", 1:nrow, sep = "")))


以下是我为获得结果所做的最终更改，但是，如果您不太投资于热图，我建议您使用Maurits Evers的建议。不要忽视我对图像尺寸所做的更改。

# creates my own colour palette
my_palette <- colorRampPalette(c("snow", "yellow", "darkorange", "red"))(n = 399)
# (optional) defines the colour breaks manually for a "skewed" colour transition
col_breaks = c(seq(0,0.09,length=100), #white 'snow'
seq(0.1,0.19,length=100), # for yellow
seq(0.2,0.29,length=100), # for orange 'darkorange'
seq(0.3,1,length=100)) # for red
# creates an image
png(paste(SourceDir, "Heatmap_XYZ.png" )
# create PNG for the heat map
width = 5*580, # 5 x 580 pixels
height = 5*420, # 5 x 420 pixels
res = 300, # 300 pixels per inch
pointsize =11) # smaller font size
heatmap.2(ConditionsMtx[[ConditionsAbbr[i]]],
cellnote = ConditionsMtx[[ConditionsAbbr[i]]], # same data set for cell labels
main = "XYZ", # heat map title
notecol="black", # change font color of cell labels to black
density.info="none", # turns off density plot inside color legend
trace="none", # turns off trace lines inside the heat map
margins=c(0,0), # widens margins around plot
col=my_palette, # use on color palette defined earlier
breaks=col_breaks, # enable color transition at specified limits
dendrogram="none", # only draw a row dendrogra