前言
oncoprint 是一种通过热图的方式来可视化多个基因组变异事件。ComplexHeatmap 包提供了 oncoPrint() 函数来绘制这种类型的图。
默认的样式是 cBioPortal 格式，我们也可以根据需要不同类型的图形
常规设置
1. 输入数据格式
输入数据可以有两种格式：矩阵和矩阵列表
1.1 矩阵
对于矩阵类型的数据，行代表的是基因，列表示的是样本，矩阵的值表示基因在样本中方式的变异类型。例如
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)
对于这种字符型矩阵，还需要定义相应的变异类型提取函数，例如
> get_type_fun <- function(x) unlist(strsplit(x, ";"))
> get_type_fun(mat[1,1])
[1] "snv" "indel"
如果变异类型的编码方式为 snv|indel，只需把函数定义为按 | 分割就行
get_type_fun <- function(x) unlist(strsplit(x, "|"))
然后将该函数传递给 oncoPrint() 函数的 get_type 参数。
对于常见的分隔符：;:,|，oncoPrint 会自动解析，不需要指定解析函数
alter_fun 参数可以自定义每种变异类型在热图单元格中的绘制函数，函数接受 4 个参数，其中 x、y 用于标识格子位置，w、h 用于标识格子的大小，并使用 col 来标注颜色
col = c(snv = "#fb8072", indel = "#80b1d3")
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)),
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
)
注意：如果 alter_fun 设置为列表，元素的顺序会影响图形绘制顺序，先定义的先绘制
1.2 矩阵列表
第二种格式是矩阵列表，列表中的每种突变类型对应一个矩阵，矩阵仅包含 0-1 值，用于标识基因在样本中是否发生了这种类型的突变，并且列表名称与变异类型对应
> 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
需要保证所有矩阵具有相同的行名和列名
col = c(snv = "#fb8072", indel = "#80b1d3")
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
)
2. 定义 alter_fun
alter_fun 不仅可以传递一个函数列表，还可以传递一个函数，该函数多了一个参数，用于传递一个逻辑值向量，用于标识当前基因在当前样本中是否发生了对应的变异
oncoPrint(
mat, alter_fun = function(x, y, w, h, v) {
if (v["snv"])
grid.rect(x, y, w * 0.9, h * 0.9,
gp = gpar(fill = col["snv"], col = NA))
if (v["indel"])
grid.rect(x, y, w * 0.9, h * 0.4,
gp = gpar(fill = col["indel"], col = NA))
},
col = col
)
设置为单个函数，可以更灵活进行自定义
oncoPrint(
mat, alter_fun = function(x, y, w, h, v) {
n = sum(v) # 发生变异的数量
h = h * 0.9
if (n)
grid.rect(x,
y - h * 0.5 + 1:n / n * h,
w * 0.9,
1 / n * h,
gp = gpar(fill = col[names(which(v))], col = NA),
just = "top")
}, col = col)
设置为三角形填充
oncoPrint(
mat,
alter_fun = list(
# 控制背景的绘制，通常在放在第一个
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
)
在上面的例子中，我们添加了一个背景设置 background。背景需要放置第一个，如果想要删除背景，可以设置
background = function(...) NULL
在某些情况下，我们可能需要设置的变异类型较多，为了确保我们 alter_fun 设置正确，可以使用 test_alter_fun() 函数来进行测试。例如
alter_fun <- list(
mut1 = function(x, y, w, h)
grid.rect(x, y, w, h, gp = gpar(fill = "red", col = NA)),
mut2 = function(x, y, w, h)
grid.rect(x, y, w, h, gp = gpar(fill = "blue", col = NA)),
mut3 = function(x, y, w, h)
grid.rect(x, y, w, h, gp = gpar(fill = "yellow", col = NA)),
mut4 = function(x, y, w, h)
grid.rect(x, y, w, h, gp = gpar(fill = "purple", col = NA)),
mut5 = function(x, y, w, h)
grid.rect(x, y, w, h, gp = gpar(fill = NA, lwd = 2)),
mut6 = function(x, y, w, h)
grid.points(x, y, pch = 16),
mut7 = function(x, y, w, h)
grid.segments(x - w*0.5, y - h*0.5, x + w*0.5, y + h*0.5, gp = gpar(lwd = 2))
)
test_alter_fun(alter_fun)
3. 简化 alter_fun
如果只要绘制简单图形，如 矩形和散点图，可以使用 alter_graphic() 函数
oncoPrint(
mat,
alter_fun = list(
snv = alter_graphic(
"rect",
width = 0.9,
height = 0.9,
fill = col["snv"]
),
indel = alter_graphic(
"rect",
width = 0.9,
height = 0.4,
fill = col["indel"]
)
),
col = col
)
4. 复杂变异类型
大多数时候，我们需要展示的变异类型并不是单单一两种，可能会有很多种，如果单单用颜色来区分的话比较困难。
而且，有些变异类型是我们比较关注的，而其他的一些次要的变异类型没那么重要，就有一个主次关系。
例如，snv 和 indel 变异类型中又包含 intronic snv、exonic snv、intronic indel、exonic indel。主分类应该是 snv 和 indel，次分类是 intronic 和 exonic
所以，我们可以为主分类设置同样类型的图形，比如说，设置不同的颜色来区分；而次分类设置为不同的符号类型。
对于下面的数据
type <- c("snv;intronic", "snv;exonic", "indel;intronic", "indel;exonic", "")
m <- matrix(
sample(type, size = 100, replace = TRUE),
nrow = 10, ncol = 10,
dimnames = list(paste0("g", 1:10), paste0("s", 1:10))
)
定义 alter_fun
alter_fun <- list(
# 设置背景
background = function(x, y, w, h)
grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "#CCCCCC", col = NA)),
# SNV 颜色
snv = function(x, y, w, h)
grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "#fb8072", col = NA)),
# indel 颜色
indel = function(x, y, w, h)
grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "#80b1d3", col = NA)),
# 内含子设置为点
intronic = function(x, y, w, h)
grid.points(x, y, pch = 16),
# 外显子设置为 X
exonic = function(x, y, w, h) {
grid.segments(x - w*0.4, y - h*0.4, x + w*0.4, y + h*0.4, gp = gpar(lwd = 2))
grid.segments(x + w*0.4, y - h*0.4, x - w*0.4, y + h*0.4, gp = gpar(lwd = 2))
}
)
绘制
oncoPrint(m, alter_fun = alter_fun, col = c(snv = "#fb8072", indel = "#80b1d3"))
5. 其他参数设置
oncoPrint 本质上也是热图，所以很多热图的参数都可以使用，例如，显示列名
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))
)
oncoPrint(
mat, alter_fun = alter_fun,
col = col, show_column_names = TRUE
)
行名和百分比文本的显示可以使用 show_pct 和 show_row_names，位置可以使用 pct_side 和 row_names_side 设置，百分比精确度可以使用 pct_digits
oncoPrint(
mat,
alter_fun = alter_fun,
col = col,
row_names_side = "left",
pct_side = "right",
pct_digits = 2
)
使用 anno_oncoprint_barplot() 注释函数来控制条形图
oncoPrint(
mat,
alter_fun = alter_fun,
col = col,
top_annotation = HeatmapAnnotation(cbar = anno_oncoprint_barplot(height = unit(1, "cm"))),
right_annotation = rowAnnotation(rbar = anno_oncoprint_barplot(
width = unit(4, "cm"),
axis_param = list(
at = c(0, 2, 4),
labels = c("zero", "two", "four"),
side = "top",
labels_rot = 0
)
)),
)
或者，把右边的条形图往左边放放
oncoPrint(
mat,
alter_fun = alter_fun,
col = col,
left_annotation = rowAnnotation(rbar = anno_oncoprint_barplot(axis_param = list(direction = "reverse"))),
right_annotation = NULL
)
应用实例
我们使用 ComplexHeatmap 包中提供的数据，该数据来自于 cBioPortal 数据库
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))
该数据包含 Ras-Raf-MEK-Erk/JNK signaling 通路中的 26 个基因在 172 个肺腺癌样本中的突变即 CNV 变异信息
> mat[1:3,1:3]
TCGA-05-4384-01 TCGA-05-4390-01 TCGA-05-4425-01
KRAS " " "MUT;" " "
HRAS " " " " " "
BRAF " " " " " "
数据中包含 3 种变异：MUT、AMP、HOMDEL，现在，我们为每种变异类型定义图形
col <- c("HOMDEL" = "#ff7f00", "AMP" = "#984ea3", "MUT" = "#4daf4a")
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"])
)
我们只是设置格子的颜色，所以可以使用 alter_graphic 来设置
设置列标题和图例
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")
)
绘制图片
oncoPrint(
mat, alter_fun = alter_fun, col = col,
column_title = column_title,
heatmap_legend_param = heatmap_legend_param
)
我们可以看到，有很多空白的行和列，删掉它们
oncoPrint(
mat, alter_fun = alter_fun, col = col,
remove_empty_columns = TRUE,
remove_empty_rows = TRUE,
column_title = column_title,
heatmap_legend_param = heatmap_legend_param
)
row_order 或 column_order 可以设置行、列的顺序
oncoPrint(
mat, alter_fun = alter_fun, col = col,
column_title = column_title,
row_order = 1:nrow(mat),
remove_empty_columns = TRUE,
remove_empty_rows = TRUE,
heatmap_legend_param = heatmap_legend_param
)
我们可以使用 anno_oncoprint_barplot() 来修改条形图注释，且条形图默认都是显示变异的数量，可以在设置 show_fraction = TRUE 来显示频率
oncoPrint(
mat,
alter_fun = alter_fun,
col = col,
# 上方条形图只显示 MUT 的频率
top_annotation = HeatmapAnnotation(
column_barplot = anno_oncoprint_barplot(
"MUT", border = TRUE,
show_fraction = TRUE,
height = unit(4, "cm")
)),
# 右侧条形图显示 AMP 和 HOMDEL
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
)
类似于热图，我们可以使用 HeatmapAnnotation() 或 rowAnnotation() 来添加行列注释
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
)
起始 oncoPrint() 返回的是 Heatmap 对象，所以，我们可以在水平或竖直方向上添加热图或注释
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",
col = colorRamp2(c(-3, 0, 3), c("#8c510a", "white", "#01665e")),
width = unit(4, "cm")
)
draw(ht_list)
