tidyHeatmap基于ComplexHeatmap，遵循图形语法，最大的好处是直接使用长数据画热图，这是目前其他画热图的R包所不具备的。
目前大部分图形都是基于ggplot2绘制的，需要长数据，现在画热图也可以直接用长数据了，不需要再进行各种转换，这是我认为最大的优点。
tidyHeatmap的作者还开发了很多好用的、遵循tidy理念的R包，比如：tidybulk, tidyseurat, tidySingleCellExperiment, tidySummarizedExperiment等，看起来作者野心很大，能力也很强！tidybulk我用过，初体验还不错，以后有机会给大家介绍。
可直接访问作者的github：https://github.com/stemangiola/
# 经典2选1
devtools::install_github("stemangiola/tidyHeatmap") # 我装了开发版，功能多
install.packages("tidyHeatmap")
library(tidyHeatmap)
## ========================================
## tidyHeatmap version 1.10.0
## If you use tidyHeatmap in published research, please cite:
## 1) Mangiola et al. tidyHeatmap: an R package for modular heatmap production
## based on tidy principles. JOSS 2020.
## 2) Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
## This message can be suppressed by:
## suppressPackageStartupMessages(library(tidyHeatmap))
## ========================================
##
## Attaching package: 'tidyHeatmap'
## The following object is masked from 'package:stats':
##
## heatmap
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2
## ──
## ✔ ggplot2 3.4.1 ✔ purrr 1.0.1
## ✔ tibble 3.2.1 ✔ dplyr 1.1.1
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.4 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
首先一定是把数据变为长数据！每一列是一个变量，每一行是一个观测！
mtcars_long <- mtcars %>%
as_tibble(rownames = "car name") %>%
mutate_at(vars(- `car name`,-hp,-vs),scale) %>%
pivot_longer(cols = -c(`car name`,hp,vs),names_to = "property",values_to = "value")
mtcars_long
## # A tibble: 288 × 5
## `car name` hp vs property value[,1]
## <chr> <dbl> <dbl> <chr> <dbl>
## 1 Mazda RX4 110 0 mpg 0.151
## 2 Mazda RX4 110 0 cyl -0.105
## 3 Mazda RX4 110 0 disp -0.571
## 4 Mazda RX4 110 0 drat 0.568
## 5 Mazda RX4 110 0 wt -0.610
## 6 Mazda RX4 110 0 qsec -0.777
## 7 Mazda RX4 110 0 am 1.19
## 8 Mazda RX4 110 0 gear 0.424
## 9 Mazda RX4 110 0 carb 0.735
## 10 Mazda RX4 Wag 110 0 mpg 0.151
## # … with 278 more rows
主函数是heatmap，支持管道符：
mtcars_heatmap <- mtcars_long %>%
heatmap(.row = `car name`, .column = property, .value = value # 最主要的3个参数
, scale = "row"
) %>%
annotation_tile(hp) # 添加注释条，github已改为 annotation_xxx
mtcars_heatmap
保存：
mtcars_heatmap %>% save_pdf("mtcars_heatmap.pdf")
支持自定义颜色也是毫无问题：
mtcars_long %>%
heatmap(
`car name`,
property,
value,
scale = "row",
palette_value = c("red", "white", "blue")
)
热图分割的思路也是非常tidy，直接使用group_by()即可。
mtcars_long_split <- mtcars_long %>%
mutate(property_group = if_else(property %in% c("cyl","disp"),"engine","other"))
glimpse(mtcars_long_split)
## Rows: 288
## Columns: 6
## $ `car name` <chr> "Mazda RX4", "Mazda RX4", "Mazda RX4", "Mazda RX4", "Ma…
## $ hp <dbl> 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, 110, …
## $ vs <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1…
## $ property <chr> "mpg", "cyl", "disp", "drat", "wt", "qsec", "am", "gear…
## $ value <dbl[,1]> <matrix[26 x 1]>
## $ property_group <chr> "other", "engine", "engine", "other", "other", "oth…
mtcars_long_split %>%
group_by(vs, property_group) %>%
heatmap(.row = `car name`, .column = property, .value = value
, scale = "row"
, palette_grouping = list( # 可自定义注释条颜色
# For first grouping (vs)
c("#66C2A5", "#FC8D62"),
# For second grouping (property_group)
c("#b58b4c", "#74a6aa")
)
) %>%
annotation_tile(hp
,palette = c("red", "white", "blue")#注释条颜色也支持自定义
)
当然也是支持其他分割方法的，比如根据聚类树 分割等。
支持多种注释条，可自定义大小。
pasilla_plus <-
tidyHeatmap::pasilla |>
dplyr::mutate(act = activation) |>
tidyr::nest(data = -sample) |>
dplyr::mutate(size = rnorm(n(), 4,0.5)) |>
dplyr::mutate(age = runif(n(), 50, 200)) |>
tidyr::unnest(data)
library(grid)
pasilla_plus |>
heatmap(
.column = sample,
.row = symbol,
.value = `count normalised adjusted`,
scale = "row"
) |>
annotation_tile(condition, size = unit(0.3, "cm"), annotation_name_gp= gpar(fontsize = 8)) |>
annotation_point(activation, size = unit(0.3, "cm"), annotation_name_gp= gpar(fontsize = 8)) |>
annotation_tile(act, size = unit(0.3, "cm"), annotation_name_gp= gpar(fontsize = 8)) |>
annotation_bar(size, size = unit(0.3, "cm"), annotation_name_gp= gpar(fontsize = 8)) |>
annotation_line(age, size = unit(0.3, "cm"), annotation_name_gp= gpar(fontsize = 8))
增加边框，修改颜色
mtcars_long %>%
heatmap(
`car name`, property, value,
scale = "row",
rect_gp = grid::gpar(col = "red", lwd = 0.5)
)
可调价是否显示聚类树，聚类树的大小等
mtcars_long %>%
heatmap(`car name`, property, value
, scale = "row"
, cluster_rows = F
, column_dend_height = unit(0.2,"cm")
)
修改大小等
library(grid)
mtcars_long %>%
heatmap(`car name`, property, value
, scale = "row"
, row_names_gp = gpar(fontsize = 7)
, column_title_gp = gpar(fontsize=10, col="red")
)
需要借助wrap_heatmap函数，不能直接使用patchwork
library(ggplot2)
library(patchwork)
p_heatmap =
mtcars_long %>%
heatmap(
`car name`, property, value,
scale = "row",
show_heatmap_legend = FALSE,
row_names_gp = gpar(fontsize = 7)
)
p_ggplot = data.frame(value = 1:10) %>% ggplot(aes(value)) + geom_density()
wrap_heatmap(p_heatmap) +
p_ggplot +
# Add padding for better aesthetics
wrap_heatmap(
p_heatmap,
padding = grid::unit(c(-30, -0, -0, -10), "points" ),
clip = FALSE
) +
plot_layout(width = c(1, 0.3, 1))
除此之外，还支持很多ComplexHeatmap的特性，大家可以去作者的github探索很多细节。
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