前言
oncoprint 是一種通過(guò)熱圖的方式來(lái)可視化多個(gè)基因組變異事件。ComplexHeatmap 包提供了 oncoPrint() 函數(shù)來(lái)繪制這種類型的圖。
默認(rèn)的樣式是 cBioPortal 格式，我們也可以根據(jù)需要不同類型的圖形
常規(guī)設(shè)置
1. 輸入數(shù)據(jù)格式
輸入數(shù)據(jù)可以有兩種格式：矩陣和矩陣列表
1.1 矩陣
對(duì)于矩陣類型的數(shù)據(jù)，行代表的是基因，列表示的是樣本，矩陣的值表示基因在樣本中方式的變異類型。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)
對(duì)于這種字符型矩陣，還需要定義相應(yīng)的變異類型提取函數(shù)，例如
> get_type_fun <- function(x) unlist(strsplit(x, ";"))
> get_type_fun(mat[1,1])
[1] "snv" "indel"
如果變異類型的編碼方式為 snv|indel，只需把函數(shù)定義為按 | 分割就行
get_type_fun <- function(x) unlist(strsplit(x, "|"))
然后將該函數(shù)傳遞給 oncoPrint() 函數(shù)的 get_type 參數(shù)。
對(duì)于常見(jiàn)的分隔符：;:,|，oncoPrint 會(huì)自動(dòng)解析，不需要指定解析函數(shù)
alter_fun 參數(shù)可以自定義每種變異類型在熱圖單元格中的繪制函數(shù)，函數(shù)接受 4 個(gè)參數(shù)，其中 x、y 用于標(biāo)識(shí)格子位置，w、h 用于標(biāo)識(shí)格子的大小，并使用 col 來(lái)標(biāo)注顏色
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 設(shè)置為列表，元素的順序會(huì)影響圖形繪制順序，先定義的先繪制
1.2 矩陣列表
第二種格式是矩陣列表，列表中的每種突變類型對(duì)應(yīng)一個(gè)矩陣，矩陣僅包含 0-1 值，用于標(biāo)識(shí)基因在樣本中是否發(fā)生了這種類型的突變，并且列表名稱與變異類型對(duì)應(yīng)
> 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
需要保證所有矩陣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. 定義 alter_fun
alter_fun 不僅可以傳遞一個(gè)函數(shù)列表，還可以傳遞一個(gè)函數(shù)，該函數(shù)多了一個(gè)參數(shù)，用于傳遞一個(gè)邏輯值向量，用于標(biāo)識(shí)當(dāng)前基因在當(dāng)前樣本中是否發(fā)生了對(duì)應(yīng)的變異
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
)
設(shè)置為單個(gè)函數(shù)，可以更靈活進(jìn)行自定義
oncoPrint(
mat, alter_fun = function(x, y, w, h, v) {
n = sum(v) # 發(fā)生變異的數(shù)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)
設(shè)置為三角形填充
oncoPrint(
mat,
alter_fun = list(
# 控制背景的繪制，通常在放在第一個(gè)
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，我們添加了一個(gè)背景設(shè)置 background。背景需要放置第一個(gè)，如果想要?jiǎng)h除背景，可以設(shè)置
background = function(...) NULL
在某些情況下，我們可能需要設(shè)置的變異類型較多，為了確保我們 alter_fun 設(shè)置正確，可以使用 test_alter_fun() 函數(shù)來(lái)進(jìn)行測(cè)試。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. 簡(jiǎn)化 alter_fun
如果只要繪制簡(jiǎn)單圖形，如 矩形和散點(diǎn)圖，可以使用 alter_graphic() 函數(shù)
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. 復(fù)雜變異類型
大多數(shù)時(shí)候，我們需要展示的變異類型并不是單單一兩種，可能會(huì)有很多種，如果單單用顏色來(lái)區(qū)分的話比較困難。
而且，有些變異類型是我們比較關(guān)注的，而其他的一些次要的變異類型沒(méi)那么重要，就有一個(gè)主次關(guān)系。
例如，snv 和 indel 變異類型中又包含 intronic snv、exonic snv、intronic indel、exonic indel。主分類應(yīng)該是 snv 和 indel，次分類是 intronic 和 exonic
所以，我們可以為主分類設(shè)置同樣類型的圖形，比如說(shuō)，設(shè)置不同的顏色來(lái)區(qū)分；而次分類設(shè)置為不同的符號(hào)類型。
對(duì)于下面的數(shù)據(jù)
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))
)
定義 alter_fun
alter_fun <- list(
# 設(shè)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 顏色
snv = function(x, y, w, h)
grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "#fb8072", col = NA)),
# indel 顏色
indel = function(x, y, w, h)
grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "#80b1d3", col = NA)),
# 內(nèi)含子設(shè)置為點(diǎn)
intronic = function(x, y, w, h)
grid.points(x, y, pch = 16),
# 外顯子設(shè)置為 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))
}
)
繪制
oncoPrint(m, alter_fun = alter_fun, col = c(snv = "#fb8072", indel = "#80b1d3"))
5. 其他參數(shù)設(shè)置
oncoPrint 本質(zhì)上也是熱圖，所以很多熱圖的參數(shù)都可以使用，例如，顯示列名
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顯示可以使用 show_pct 和 show_row_names，位置可以使用 pct_side 和 row_names_side 設(shè)置，百分比精確度可以使用 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() 注釋函數(shù)來(lái)控制條形圖
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，把右邊的條形圖往左邊放放
oncoPrint(
mat,
alter_fun = alter_fun,
col = col,
left_annotation = rowAnnotation(rbar = anno_oncoprint_barplot(axis_param = list(direction = "reverse"))),
right_annotation = NULL
)
應(yīng)用實(shí)例
我們使用 ComplexHeatmap 包中提供的數(shù)據(jù)，該數(shù)據(jù)來(lái)自于 cBioPortal 數(shù)據(jù)庫(kù)
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))
該數(shù)據(jù)包含 Ras-Raf-MEK-Erk/JNK signaling 通路中的 26 個(gè)基因在 172 個(gè)肺腺癌樣本中的突變即 CNV 變異信息
> mat[1:3,1:3]
TCGA-05-4384-01 TCGA-05-4390-01 TCGA-05-4425-01
KRAS " " "MUT;" " "
HRAS " " " " " "
BRAF " " " " " "
數(shù)據(jù)中包含 3 種變異：MUT、AMP、HOMDEL，現(xiàn)在，我們?yōu)槊糠N變異類型定義圖形
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"])
)
我們只是設(shè)置格子的顏色，所以可以使用 alter_graphic 來(lái)設(shè)置
設(shè)置列標(biāo)題和圖例
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")
)
繪制圖片
oncoPrint(
mat, alter_fun = alter_fun, col = col,
column_title = column_title,
heatmap_legend_param = heatmap_legend_param
)
我們可以看到，有很多空白的行和列，刪掉它們
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 可以設(shè)置行、列的順序
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
)
我們可以使用 anno_oncoprint_barplot() 來(lái)修改條形圖注釋，且條形圖默認(rèn)都是顯示變異的數(shù)量，可以在設(shè)置 show_fraction = TRUE 來(lái)顯示頻率
oncoPrint(
mat,
alter_fun = alter_fun,
col = col,
# 上方條形圖只顯示 MUT 的頻率
top_annotation = HeatmapAnnotation(
column_barplot = anno_oncoprint_barplot(
"MUT", border = TRUE,
show_fraction = TRUE,
height = unit(4, "cm")
)),
# 右側(cè)條形圖顯示 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
)
類似于熱圖，我們可以使用 HeatmapAnnotation() 或 rowAnnotation() 來(lái)添加行列注釋
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 對(duì)象，所以，我們可以在水平或豎直方向上添加熱圖或注釋
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)
