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超详细的R语言热图之complexheatmap系列07
我们继续学习complexheatmap包的内容，这是本系列的第7篇文章。本系列内容非常多，将通过多篇推文逐渐介绍，欢迎大家关注我的公众号：医学和生信笔记。
本篇内容主要介绍瀑布图的画法，这也是各种SCI文章中常见的图，不得不说，这个图太强大了！
complexheatmap系列第1篇：超详细的R语言热图之complexheatmap系列1
complexheatmap系列第2篇：超详细的R语言热图之complexheatmap系列2
complexheatmap系列第3篇：超详细的R语言热图之complexheatmap系列3
complexheatmap系列第4篇：超详细的R语言热图之complexheatmap系列4
complexheatmap系列第5篇：超详细的R语言热图之complexheatmap系列5
complexheatmap系列第6篇：超详细的R语言热图之complexheatmap系列6
第七章 瀑布图
在生信文章中很常见的可视化突变数据的瀑布图，就是基于complexheatmap画出来的。瀑布图可用于展示多个基因的突变情况，是热图的一种变体。complexheatmap包提供oncoPrint()用于画瀑布图。默认的样式是cBioPortal提供的，可以根据自己的喜好进行更改。
7.1 可视化突变数据的瀑布图
直接使用cBioPortal的数据画一张图，肺癌数据已内置在complexheatmap包中。可以自己去cBioPortal官网下载其他癌症数据，前提是要有一定的生物学知识哦！
# 读取内置数据
mat = read.table(system.file("extdata", package = "ComplexHeatmap", "tcga_lung_adenocarcinoma_provisional_ras_raf_mek_jnk_signalling.txt"), header = TRUE, stringsAsFactors = FALSE, sep = "\t")
mat[is.na(mat)] = ""
rownames(mat) = mat[, 1]
mat = mat[, -1]
mat= mat[, -ncol(mat)]
mat = t(as.matrix(mat))
mat[1:3, 1:3]
## TCGA-05-4384-01 TCGA-05-4390-01 TCGA-05-4425-01
## KRAS " " "MUT;" " "
## HRAS " " " " " "
## BRAF " " " " " "
首先为不同的变异类型设置颜色。
col = c("HOMDEL" = "blue", "AMP" = "red", "MUT" = "#008000")
alter_fun = list(
background = function(x, y, w, h) {
grid.rect(x, y, w-unit(2, "pt"), h-unit(2, "pt"),
gp = gpar(fill = "#CCCCCC", col = NA))
},
HOMDEL = function(x, y, w, h) {
grid.rect(x, y, w-unit(2, "pt"), h-unit(2, "pt"),
gp = gpar(fill = col["HOMDEL"], col = NA))
},
AMP = function(x, y, w, h) {
grid.rect(x, y, w-unit(2, "pt"), h-unit(2, "pt"),
gp = gpar(fill = col["AMP"], col = NA))
},
MUT = function(x, y, w, h) {
grid.rect(x, y, w-unit(2, "pt"), h*0.33,
gp = gpar(fill = col["MUT"], col = NA))
}
)
也提供了另外一种设置颜色的方法：
# 另一种方法
library(ComplexHeatmap)
## 载入需要的程辑包：grid
## ========================================
## ComplexHeatmap version 2.8.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
##
## If you use it in published research, please cite:
## Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
##
## The new InteractiveComplexHeatmap package can directly export static
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================
alter_fun = list(
background = alter_graphic("rect", fill = "#CCCCCC"),
HOMDEL = alter_graphic("rect", fill = col["HOMDEL"]),
AMP = alter_graphic("rect", fill = col["AMP"]),
MUT = alter_graphic("rect", height = 0.33, fill = col["MUT"])
)
下面就是画图时刻：
column_title = "OncoPrint for TCGA Lung Adenocarcinoma, genes in Ras Raf MEK JNK signalling"
heatmap_legend_param = list(title = "Alternations", at = c("HOMDEL", "AMP", "MUT"),
labels = c("Deep deletion", "Amplification", "Mutation"))
oncoPrint(mat,
alter_fun = alter_fun, col = col,
column_title = column_title, heatmap_legend_param = heatmap_legend_param)
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
7.2.1 移除空的行和列
当一个样本或基因没有突变时，默认也会画出来，可以通过设置去除。
oncoPrint(mat, alter_fun = alter_fun, col = col,
remove_empty_columns = T, # 删除空列
remove_empty_rows = T, # 删除空行
column_title = column_title, heatmap_legend_param = heatmap_legend_param
)
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
需要注意一个小问题，当此图和其他注释条、热图等联合时，可能会出错，需要手动调整好数据。
7.2.2 重新排列瀑布图
可以通过自定义重新排列行或列的顺序。
# 自定义顺序
sample_order = scan(paste0(system.file("extdata", package = "ComplexHeatmap"), "/sample_order.txt"), what = "character")
oncoPrint(mat, alter_fun = alter_fun, col = col,row_order = 1:nrow(mat),
column_order = sample_order, remove_empty_columns = TRUE,
remove_empty_rows = TRUE,
column_title = column_title,
heatmap_legend_param = heatmap_legend_param)
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
7.2.3瀑布图注释
默认的瀑布图包括了很多注释，比如上侧的条形图表示每个基因的突变情况，右侧条形图表示每个样本的突变情况，左侧数字表示某个基因突变样本占的比例。
条形图是通过anno_oncoprint_barplot()函数设置的。
oncoPrint(mat,
alter_fun = alter_fun, col = col,
top_annotation = HeatmapAnnotation(
column_barplot = anno_oncoprint_barplot("MUT", border = TRUE,
height = unit(4, "cm"))
),
right_annotation = rowAnnotation(
row_barplot = anno_oncoprint_barplot(c("AMP", "HOMDEL"),
border = TRUE, height = unit(4, "cm"),
axis_param = list(side = "bottom", labels_rot = 90))
),
remove_empty_columns = TRUE, remove_empty_rows = TRUE,
column_title = column_title, heatmap_legend_param = heatmap_legend_param)
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
条形图注释条默认显示数字，可以通过设置show_fraction = TRUE变为比例：
oncoPrint(mat,
alter_fun = alter_fun, col = col,
top_annotation = HeatmapAnnotation(
column_barplot = anno_oncoprint_barplot(show_fraction = TRUE)
),
right_annotation = rowAnnotation(
row_barplot = anno_oncoprint_barplot(show_fraction = TRUE)
),
remove_empty_columns = TRUE, remove_empty_rows = TRUE,
column_title = column_title, heatmap_legend_param = heatmap_legend_param)
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
通过pct_side = "right"，row_names_side = "left"设置左右两侧的数字和基因：
oncoPrint(mat,
alter_fun = alter_fun, col = col,
remove_empty_columns = TRUE, remove_empty_rows = TRUE,
pct_side = "right", row_names_side = "left",
column_title = column_title, heatmap_legend_param = heatmap_legend_param)
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
还可以自己添加更多注释：
oncoPrint(mat,
alter_fun = alter_fun, col = col,
remove_empty_columns = TRUE, remove_empty_rows = TRUE,
top_annotation = HeatmapAnnotation(cbar = anno_oncoprint_barplot(),
foo1 = 1:172,
bar1 = anno_points(1:172)
),
left_annotation = rowAnnotation(foo2 = 1:26),
right_annotation = rowAnnotation(bar2 = anno_barplot(1:26)),
column_title = column_title, heatmap_legend_param = heatmap_legend_param)
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
7.2.4 瀑布图作为热图组图
oncoPrint返回的还是一个Heatmap类，当然可以和其他热图继续进行组合！
ht_list = oncoPrint(mat,
alter_fun = alter_fun, col = col,
column_title = column_title, heatmap_legend_param = heatmap_legend_param) +
Heatmap(matrix(rnorm(nrow(mat)*10), ncol = 10), name = "expr", width = unit(4, "cm"))
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
draw(ht_list)
或者竖着组图：
ht_list = oncoPrint(mat,
alter_fun = alter_fun, col = col,
column_title = column_title, heatmap_legend_param = heatmap_legend_param) %v%
Heatmap(matrix(rnorm(ncol(mat)*10), nrow = 10), name = "expr", height = unit(4, "cm"))
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
draw(ht_list)
当然也可以进行热图分割：
ht_list = oncoPrint(mat,
alter_fun = alter_fun, col = col,
column_title = column_title, heatmap_legend_param = heatmap_legend_param) +
Heatmap(matrix(rnorm(nrow(mat)*10), ncol = 10), name = "expr", width = unit(4, "cm"))
## All mutation types: MUT, AMP, HOMDEL.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
draw(ht_list, row_split = sample(c("a", "b"), nrow(mat), replace = TRUE))
上面就是画瀑布图的小例子，可以发挥你的想象力，进行各种自定义设置，大家可以自行探索。
下面是对一些内置函数的解释，为什么要这样设置等的说明。
7.2 一般设置
7.2.1 输入数据的格式
输入数据是矩阵，行是基因，列是样本，内容是突变类型，类似以下矩阵：
mat = read.table(textConnection(
"s1,s2,s3
g1,snv;indel,snv,indel
g2,,snv;indel,snv
g3,snv,,indel;snv"), row.names = 1, header = TRUE, sep = ",", stringsAsFactors = FALSE)
mat = as.matrix(mat)
mat
## s1 s2 s3
## g1 "snv;indel" "snv" "indel"
## g2 "" "snv;indel" "snv"
## g3 "snv" "" "indel;snv"
在这个例子中，我们需要设置一个函数提取不同的突变类型，方便使用不同的颜色。但其实oncoPrint内置了这样的函数，不需要自己写，只需要你的分隔符是; : , |，其中的任意一种都可以。
# 这样的函数其实不用自己写
get_type_fun = function(x) strsplit(x, ";")[[1]]
get_type_fun(mat[1, 1])
## [1] "snv" "indel"
对于一个样本中的一个基因，不同的突变类型可能会在画在一个单元格中，我们需要定义去和添加这些元素，主要包括元素的位置（x和y），和宽度和高度。
col = c(snv = "red", indel = "blue") # 定义颜色
oncoPrint(mat,
alter_fun = list(
snv = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.9,
gp = gpar(fill = col["snv"], col = NA)), # 设置 snv 的属性
indel = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.4,
gp = gpar(fill = col["indel"], col = NA)) # 设置 indel 的属性
), col = col)
## All mutation types: snv, indel.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
输入数据也可以是列表格式。例如对于上面一幅图，输入数据因该是一个含有2个矩阵的列表，每个矩阵都是由0和1组成的，分别代表不同的突变类型（snv, indel）。两个矩阵的名字应该和突变类型吻合（snv, indel）。
mat_list <- list(
snv = matrix(c(1, 0, 1, 1, 1, 0, 0, 1, 1), nrow = 3),
indel = matrix(c(1, 0, 0, 0, 1, 0, 1, 0, 0), nrow = 3))
rownames(mat_list$snv) <- rownames(mat_list$indel) <- c("g1", "g2", "g3")
colnames(mat_list$snv) <- colnames(mat_list$indel) <- c("s1", "s2", "s3")
mat_list
## $snv
## s1 s2 s3
## g1 1 1 0
## g2 0 1 1
## g3 1 0 1
##
## $indel
## s1 s2 s3
## g1 1 0 1
## g2 0 1 0
## g3 0 0 0
oncoPrint需要列表中的所有矩阵都有相同的行名和列名
# now you don't need `get_type`
oncoPrint(mat_list,
alter_fun = list(
snv = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.9,
gp = gpar(fill = col["snv"], col = NA)),
indel = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.4,
gp = gpar(fill = col["indel"], col = NA))
), col = col)
## All mutation types: snv, indel.
## `alter_fun` is assumed vectorizable. If it does not generate correct
## plot, please set `alter_fun_is_vectorized = FALSE` in `oncoPrint()`.
7.2.2 自定义alter_fun()
oncoPrint(mat,
alter_fun = list(
background = function(x, y, w, h) {
grid.polygon(
unit.c(x - 0.5*w, x - 0.5*w, x + 0.5*w),
unit.c(y - 0.5*h, y + 0.5*h, y - 0.5*h),
gp = gpar(fill = "grey", col = "white"))
grid.polygon(
unit.c(x + 0.5*w, x + 0.5*w, x - 0.5*w),
unit.c(y + 0.5*h, y - 0.5*h, y + 0.5*h),
gp = gpar(fill = "grey", col = "white"))
},
snv = function(x, y, w, h) {
grid.polygon(
unit.c(x - 0.5*w, x - 0.5*w, x + 0.5*w),
unit.c(y - 0.5*h, y + 0.5*h, y - 0.5*h),
gp = gpar(fill = col["snv"], col = "white"))
},
indel = function(x, y, w, h) {
grid.polygon(
unit.c(x + 0.5*w, x + 0.5*w, x - 0.5*w),
unit.c(y + 0.5*h, y - 0.5*h, y + 0.5*h),
gp = gpar(fill = col["indel"], col = "white"))
}
), col = col)
## All mutation types: snv, indel.
以上就是今天的内容。
本系列所有代码会在本系列结束后给出获取方式，欢迎点赞，关注，转发！
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