复现IF 48.5的期刊图，这个图帮你省下50%版面费！

最近，看到一篇Nature，频繁使用这种 趴着或者躺着 的Correlation Heatmap，美观+省版面空间 （ref: s41586-023-05794-2 ） ，

下面 使用R语言复现一下这种趴着或者躺着的Correlation Heatmap。

效果图：躺着Correlation Heatmap上三角Correlation Heatmap

首先，关闭xy轴刻度标签，并且开启对角线刻度标签，

library(ggcorrplot)

ggcorrplot(
corr_matrix,
type = "upper",
show.diag = TRUE, # 开启对角线刻度标签，
method = "square",
hc.order = TRUE,
lab = TRUE,
lab_size = 5,
lab_col = "black",
colors = c("#0571B0", "white", "#CA0020"),
outline.color = "gray30",
tl.cex = 10
) +
theme(
axis.text.x = element_blank, # 关闭xy轴刻度标签
axis.text.y = element_blank,
legend.position = "none", # 关闭图例
panel.grid = element_blank
)

然后，在对角线添加样本名称，

library(ggcorrplot)
library(ggplot2)

hc <- hclust(as.dist(1 - corr_matrix))
ordered_vars <- colnames(corr_matrix)[hc$order]
corr_matrix_ord <- corr_matrix[ordered_vars, ordered_vars]

p <- ggcorrplot(
corr_matrix_ord,
type = "upper",
show.diag = TRUE,
method = "square",
lab = TRUE,
lab_size = 5,
lab_col = "black",
colors = c("#0571B0", "white", "#CA0020"),
outline.color = "gray30",
tl.cex = 0
)

n <- length(ordered_vars)
diag_labels <- data.frame(
x = 1:n,
y = 1:n,
label = ordered_vars
)

# 添加样本名称
final_plot <- p +
geom_text(
data = diag_labels,
aes(x = x + 0.1, y = y - 0.8, label = label),
inherit.aes = FALSE,
angle = 0,
size = 5,
fontface = "plain",
color = "black",
hjust = 0.5,
vjust = 1
) +
coord_cartesian(ylim = c(0.2, n)) +
theme(
axis.text.x = element_blank,
axis.text.y = element_blank,
axis.ticks = element_blank,
panel.grid = element_blank,
legend.position = "none"
)

print(final_plot)

这里注意几个细节：

最后，通过ggdraw + draw_plot(ggplotify::as.ggplot(final_plot, angle = -45))旋转图形即可，

library(cowplot)

options(repr.plot.width = 10, repr.plot.height = 10, repr.plot.res = 200)

hc <- hclust(as.dist(1 - corr_matrix))
ordered_vars <- colnames(corr_matrix)[hc$order]
corr_matrix_ord <- corr_matrix[ordered_vars, ordered_vars]

p <- ggcorrplot(
corr_matrix_ord,
type = "upper",
show.diag = TRUE,
method = "square",
lab = TRUE,
lab_size = 5,
lab_col = "black",
colors = c("#0571B0", "white", "#CA0020"),
outline.color = "gray30",
tl.cex = 0
)

n <- length(ordered_vars)
diag_labels <- data.frame(
x = 1:n,
y = 1:n,
label = ordered_vars
)

final_plot <- p +
geom_text(
data = diag_labels,
aes(x = x + 0.3, y = y - 0.8, label = label),
inherit.aes = FALSE,
angle = 0,
size = 5,
fontface = "plain",
color = "black",
hjust = 0.5,
vjust = 1
) +
coord_cartesian(ylim = c(0, n + 0.5), xlim = c(0, n + 1)) +
theme(
axis.text.x = element_blank,
axis.text.y = element_blank,
axis.ticks = element_blank,
panel.grid = element_blank,
legend.position = "none",
plot.margin = unit(c(3.5, 3, 2, 3.5), "cm")
)

ggdraw +
draw_plot(ggplotify::as.ggplot(final_plot, angle = -45)) # 旋转图形

换个颜色，

方法和上面一样，注意通过aes(x = x + 0.1, y = y - 0.8, label = label)调整样本名称xy方向的坐标；通过ggdraw + draw_plot(ggplotify::as.ggplot(final_plot, angle = -45))旋转图形的角度，

当然，原图还有一些细节可借助PPT等工具完成！

数据

使用“ 1.4.9 gene数据集 ”，

用cor方法简单计算一下相关性。