Author: Zuguang Gu ( z.gu@dkfz.de )
翻譯：詩翔
Date: 2018-10-30

一個簡單的熱圖通常用戶快速瀏覽數據。一個熱圖列表的特殊例子就是只包含一個熱圖。相比于已經存在的工具， ComplexHeatmap包提供了一種更靈活的方式支持單個熱圖的可視化。在下面的例子中，我們會說明如何設置參數以顯示一個簡單的熱圖。

首先讓我們載入包并生成一個隨機矩陣。

library(ComplexHeatmap)
library(circlize)

set.seed(123)

mat = cbind(rbind(matrix(rnorm(16, -1), 4), matrix(rnorm(32, 1), 8)),
            rbind(matrix(rnorm(24, 1), 4), matrix(rnorm(48, -1), 8)))

# 置換行列
mat = mat[sample(nrow(mat), nrow(mat)), sample(ncol(mat), ncol(mat))]

rownames(mat) = paste0("R", 1:12)
colnames(mat) = paste0("C", 1:10)

使用默認的設置繪制熱圖。熱圖默認的樣式跟其他相似熱圖函數生成的效果很接近。

Heatmap(mat)

顏色

大多數情況下，熱圖可視化含連續值得矩陣。在這種情況下，用戶需要提供一個顏色映射函數。一個顏色映射函數需要接收一個數值向量并返回對應的顏色。circlize包提供的colorRamp2()對于生成這樣的函數很有用。當前該函數通過LAB顏色空間線性地在每個區間插入顏色。

在下面的例子中，-3到3的區間被線性插入值用于獲取對應的顏色，值大于3的被映射為紅色，小于-3的被映射為綠色（因此這里的顏色對于異常值具有魯棒性）。

mat2 = mat
mat2[1, 1] = 100000
Heatmap(mat2, col = colorRamp2(c(-3, 0, 3), c("green", "white", "red")), 
    cluster_rows = FALSE, cluster_columns = FALSE)

如果矩陣值是連續的，你也可以提供一個顏色向量，顏色會根據第"k"個百分位進行插值。但是記住這種方法對于異常點沒有魯棒性。

Heatmap(mat, col = rev(rainbow(10)))

如果矩陣包含離散值（要么是數值的要么是字符串），顏色應該指定為一個命名向量用于將離散值映射為顏色。如果顏色沒有名字，那么顏色的順序會對應于unique(mat)的順序。

discrete_mat = matrix(sample(1:4, 100, replace = TRUE), 10, 10)
colors = structure(circlize::rand_color(4), names = c("1", "2", "3", "4"))
Heatmap(discrete_mat, col = colors)

或者一個字符串矩陣：

discrete_mat = matrix(sample(letters[1:4], 100, replace = TRUE), 10, 10)
colors = structure(circlize::rand_color(4), names = letters[1:4])
Heatmap(discrete_mat, col = colors)

你可以看到，對于數值型矩陣（無論它是連續映射還是離散映射），默認兩個維度都會進行聚類。而對于字符串矩陣，聚類默認是被抑制的。

熱圖中允許存在NA值。你可以通過na_col參數控制NA值的顏色。包含NA值矩陣也可以使用Heatmap()函數聚類（因為dist()函數接收NA值），使用“pearson”、 “spearman” 或 “kendall” 方法會給出警告信息。

mat_with_na = mat
mat_with_na[sample(c(TRUE, FALSE), nrow(mat)*ncol(mat), replace = TRUE, prob = c(1, 9))] = NA
Heatmap(mat_with_na, na_col = "orange", clustering_distance_rows = "pearson")
## Warning in get_dist(submat, distance): NA exists in the matrix, calculating distance by removing NA
## values.

對顏色插值來說顏色空間非常重要。默認情況下，顏色都是在LAB color space中線性插值，但你可以使用,colorRamp2()函數選擇其他的顏色空間。比較下面的兩幅圖：

f1 = colorRamp2(seq(min(mat), max(mat), length = 3), c("blue", "#EEEEEE", "red"))
f2 = colorRamp2(seq(min(mat), max(mat), length = 3), c("blue", "#EEEEEE", "red"), space = "RGB")
Heatmap(mat, col = f1, column_title = "LAB color space") +
Heatmap(mat, col = f2, column_title = "RGB color space")

下面圖形顯示了不同顏色空間的差別（使用HilbertCurve包繪制）。

標題

熱圖的名字默認用作熱圖圖例的標題。如果你將多個熱圖放到一起，名字可以作為唯一的標識符。

Heatmap(mat, name = "foo")

熱圖圖例的標題可以通過參數heatmap_legend_param進行更改。

Heatmap(mat, heatmap_legend_param = list(title = "legend"))

你可以設定熱圖的行與列標題，行與列圖形參數分別通過row_title_gp和column_title_gp選項指定，使用gpar()函數進行具體的設置。

Heatmap(mat, name = "foo", column_title = "I am a column title", 
    row_title = "I am a row title")

Heatmap(mat, name = "foo", column_title = "I am a big column title", 
    column_title_gp = gpar(fontsize = 20, fontface = "bold"))

標題的選擇可以使用row_title_rot和column_title_rot設置，但只支持水平和垂直旋轉。

Heatmap(mat, name = "foo", row_title = "row title", row_title_rot = 0)

聚類

聚類是熱圖可視化的關鍵特征之一。該包支持高度靈活的聚類設定。

首先有一些聚類的通用設定，例如是否顯示樹狀圖、其大小。

Heatmap(mat, name = "foo", cluster_rows = FALSE)

Heatmap(mat, name = "foo", show_column_dend = FALSE)

Heatmap(mat, name = "foo", row_dend_side = "right")

Heatmap(mat, name = "foo", column_dend_height = unit(2, "cm"))

有3種方式指定聚類的距離度量：

• 使用提前設定的選項，合法的值包括dist()函數支持的方法以及pearson、spearman和kendall。
• 一個從矩陣中計算距離的自定義函數，函數僅包含一個參數
• 一個從兩個向量中計算距離的自定義函數，函數僅包含2個參數

Heatmap(mat, name = "foo", clustering_distance_rows = "pearson")

Heatmap(mat, name = "foo", clustering_distance_rows = function(m) dist(m))

Heatmap(mat, name = "foo", clustering_distance_rows = function(x, y) 1 - cor(x, y))

基于這個特征，我們開源使用配對距離應用聚類使得可以魯棒地處理異常值。

mat_with_outliers = mat
for(i in  1:10) mat_with_outliers[i, i] = 1000
robust_dist = function(x, y) {
    qx = quantile(x, c(0.1, 0.9))
    qy = quantile(y, c(0.1, 0.9))
    l = x > qx[1] & x < qx[2] & y > qy[1] & y < qy[2]
    x = x[l]
    y = y[l]
    sqrt(sum((x - y)^2))
}
Heatmap(mat_with_outliers, name = "foo", 
    col = colorRamp2(c(-3, 0, 3), c("green", "white", "red")),
    clustering_distance_rows = robust_dist,
    clustering_distance_columns = robust_dist)

如果提供了距離方法，你也可以對字符串矩陣進行聚類。cell_fun參數會在后面進行解釋。

mat_letters = matrix(sample(letters[1:4], 100, replace = TRUE), 10)
# distance in th ASCII table
dist_letters = function(x, y) {
    x = strtoi(charToRaw(paste(x, collapse = "")), base = 16)
    y = strtoi(charToRaw(paste(y, collapse = "")), base = 16)
    sqrt(sum((x - y)^2))
}
Heatmap(mat_letters, name = "foo", col = structure(2:5, names = letters[1:4]),
    clustering_distance_rows = dist_letters, clustering_distance_columns = dist_letters,
    cell_fun = function(j, i, x, y, w, h, col) {
        grid.text(mat_letters[i, j], x, y)
    })

創建層次聚類的方法可以通過選項clustering_method_rows和clustering_method_columns指定，可以使用hclust()函數支持的方法。

Heatmap(mat, name = "foo", clustering_method_rows = "single")

默認，聚類由hclust()實施。但你可以通過cluster_rows或cluster_columns指定由其他方法生成的hclust或dendrogram對象。在下面的例子中，我們使用來自cluster包的diana()和agnes()函數進行聚類。

library(cluster)
Heatmap(mat, name = "foo", cluster_rows = as.dendrogram(diana(mat)),
   cluster_columns = as.dendrogram(agnes(t(mat))))

在原始的Heatmap()函數中，行或列的樹狀圖都是根據使得特征可以最大地進行分隔而排序的，Heatmap()提供了選項進行調整。除了默認的重排序方法，你也可以先生成一個樹狀圖，然后應用一些重排序的方法，然后將重排序的樹狀圖傳給cluster_rows參數。

比較下面3幅圖：

pushViewport(viewport(layout = grid.layout(nr = 1, nc = 3)))
pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 1))
draw(Heatmap(mat, name = "foo", row_dend_reorder = FALSE, column_title = "no reordering"), newpage = FALSE)
upViewport()

pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 2))
draw(Heatmap(mat, name = "foo", row_dend_reorder = TRUE, column_title = "applied reordering"), newpage = FALSE)
upViewport()

library(dendsort)
dend = dendsort(hclust(dist(mat)))
pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 3))
draw(Heatmap(mat, name = "foo", cluster_rows = dend, row_dend_reorder = FALSE, 
    column_title = "reordering by dendsort"), newpage = FALSE)
upViewport(2)

你可以使用dendextend包渲染你的dendrogram對象，自定義樹狀圖。

library(dendextend)
dend = hclust(dist(mat))
dend = color_branches(dend, k = 2)
Heatmap(mat, name = "foo", cluster_rows = dend)

更通用地，cluster_rows和cluster_columns可以提供計算聚類的函數。自定義函數的輸入需要是一個矩陣，返回值需要時一個hclust或者dendrogram對象。

Heatmap(mat, name = "foo", cluster_rows = function(m) as.dendrogram(diana(m)),
    cluster_columns = function(m) as.dendrogram(agnes(m)))

fastcluster::hclust實現了更快版本的hclust。

# code not run when building the vignette
Heatmap(mat, name = "foo", cluster_rows = function(m) fastcluster::hclust(dist(m)),
    cluster_columns = function(m) fastcluster::hclust(dist(m))) # for column cluster, m will be automatically transposed

為了更方便的使用快速版本的hclust，我們可以設定一個全局選項。

# code not run when building the vignette
ht_global_opt(fast_hclust = TRUE)
# now hclust from fastcluster package is used in all heatmaps
Heatmap(mat, name = "foo")

聚類可以幫助調整行和列的順序。但是你仍然需要手動設定row_order和column_order來設定順序。注意這個時候你需要將聚類給關掉，另外如果矩陣有行名和列名也可以直接通過名字調整順序。

Heatmap(mat, name = "foo", cluster_rows = FALSE, cluster_columns = FALSE, 
    row_order = 12:1, column_order = 10:1)

注意row_dend_reorder和row_order是不同的。前者應用于樹狀圖。因為對于樹狀圖的任何結點，旋轉兩個葉子都會給出唯一的樹狀圖。當row_order設置時，樹狀圖會被抑制。

維度名字

維度名字的側邊、可視度和圖形參數可以進行如下設置。

Heatmap(mat, name = "foo", row_names_side = "left", row_dend_side = "right", 
    column_names_side = "top", column_dend_side = "bottom")

Heatmap(mat, name = "foo", show_row_names = FALSE)

Heatmap(mat, name = "foo", row_names_gp = gpar(fontsize = 20))

Heatmap(mat, name = "foo", row_names_gp = gpar(col = c(rep("red", 4), rep("blue", 8))))

當前行名和列名不支持旋轉。文字旋轉可以通過熱圖注釋實現（這在熱圖注釋手冊中將會看到）。

按行切分熱圖

熱圖可以按行切分。這可以增加熱圖中的分組可視化。參數km設置大于1的值意味著對行實施K-means聚類并在每個子類中實施聚類。

Heatmap(mat, name = "foo", km = 2)

更通用地，split可以傳入一個分割熱圖行不同組合水平的向量或是數據框。實際上k-means聚類也是先聚類得到行的分類然后使用split實現切分。每一個行切片的標題可以通過combined_name_fun參數設定。每個切片的順序通過split中每個變量的水平控制。

Heatmap(mat, name = "foo", split = rep(c("A", "B"), 6))

Heatmap(mat, name = "foo", split = data.frame(rep(c("A", "B"), 6), rep(c("C", "D"), each = 6)))

Heatmap(mat, name = "foo", split = data.frame(rep(c("A", "B"), 6), rep(c("C", "D"), each = 6)), 
    combined_name_fun = function(x) paste(x, collapse = "\n"))

Heatmap(mat, name = "foo", km = 2, split = factor(rep(c("A", "B"), 6), levels = c("B", "A")), 
    combined_name_fun = function(x) paste(x, collapse = "\n"))

Heatmap(mat, name = "foo", km = 2, split = rep(c("A", "B"), 6), combined_name_fun = NULL)

如果你不喜歡默認的k-means分類方法，你可以通過將分類向量傳入split的方式使用其他方法。

pa = pam(mat, k = 3)
Heatmap(mat, name = "foo", split = paste0("pam", pa$clustering))

如果row_order設置了，在每個切片里面，行依然是按順序排列的。

Heatmap(mat, name = "foo", row_order = 12:1, cluster_rows = FALSE, km = 2)

gap的高度可以通過gap參數控制（單個unit或者units向量）。

Heatmap(mat, name = "foo", split = paste0("pam", pa$clustering), gap = unit(5, "mm"))

字符串矩陣也可以通過split參數切分。

Heatmap(discrete_mat, name = "foo", col = 1:4,
    split = rep(letters[1:2], each = 5))

當按行切分的時候，也可以通過圖形參數自定義行標題和行名。

Heatmap(mat, name = "foo", km = 2, row_title_gp = gpar(col = c("red", "blue"), font = 1:2),
    row_names_gp = gpar(col = c("green", "orange"), fontsize = c(10, 14)))

用戶可能已經有一個行的樹狀圖了，他們可能想要將樹狀圖分為k個子樹。這種情況下，split可以指定一個數。

dend = hclust(dist(mat))
dend = color_branches(dend, k = 2)
Heatmap(mat, name = "foo", cluster_rows = dend, split = 2)

或者可以直接指定split一個整數。注意這跟km不同。如果km設置了，首先是要k-means聚類，然后對每個子類進行聚類。當split是一個整數的時候，直接對整個矩陣進行聚類，然后根據cutree()切分。

Heatmap(mat, name = "foo", split = 2)

自定義熱圖主體

rect_gp參數提供了熱圖主體的基本圖形設置（注意fill參數已經被禁用了）。

Heatmap(mat, name = "foo", rect_gp = gpar(col = "green", lty = 2, lwd = 2))

熱圖主體可以自定義。默認熱圖主體由帶不同填充色的矩形數組組成（這里稱為cell）。如果rect_gp中的type設置為none，整個cell數組被初始化但沒有圖形，然后用戶可以通過cell_fun自定義他們自己的圖形函數。cell_fun應用于熱圖的每一個cell，它需要為每一個cell提供下面的信息：

• j - 矩陣中的列索引。
• i - 矩陣中的行索引
• x - cell中心點的x坐標
• y - cell中心點的y坐標
• width - cell的寬度
• height - cell 的高度
• fill - cell的填充色

最常見的使用是給熱圖添加數值信息。

Heatmap(mat, name = "foo", cell_fun = function(j, i, x, y, width, height, fill) {
    grid.text(sprintf("%.1f", mat[i, j]), x, y, gp = gpar(fontsize = 10))
})

下面的例子中，我們創建一個類似corrplot包提供的相關矩陣圖。

cor_mat = cor(mat)
od = hclust(dist(cor_mat))$order
cor_mat = cor_mat[od, od]
nm = rownames(cor_mat)
col_fun = circlize::colorRamp2(c(-1, 0, 1), c("green", "white", "red"))
# `col = col_fun` here is used to generate the legend
Heatmap(cor_mat, name = "correlation", col = col_fun, rect_gp = gpar(type = "none"), 
    cell_fun = function(j, i, x, y, width, height, fill) {
        grid.rect(x = x, y = y, width = width, height = height, gp = gpar(col = "grey", fill = NA))
        if(i == j) {
            grid.text(nm[i], x = x, y = y)
        } else if(i > j) {
            grid.circle(x = x, y = y, r = abs(cor_mat[i, j])/2 * min(unit.c(width, height)), 
                gp = gpar(fill = col_fun(cor_mat[i, j]), col = NA))
        } else {
            grid.text(sprintf("%.1f", cor_mat[i, j]), x, y, gp = gpar(fontsize = 8))
        }
    }, cluster_rows = FALSE, cluster_columns = FALSE,
    show_row_names = FALSE, show_column_names = FALSE)

最后一個例子是可視化圍棋，輸入數據記錄在游戲中的形勢。

str = "B[cp];W[pq];B[dc];W[qd];B[eq];W[od];B[de];W[jc];B[qk];W[qn]
;B[qh];W[ck];B[ci];W[cn];B[hc];W[je];B[jq];W[df];B[ee];W[cf]
;B[ei];W[bc];B[ce];W[be];B[bd];W[cd];B[bf];W[ad];B[bg];W[cc]
;B[eb];W[db];B[ec];W[lq];B[nq];W[jp];B[iq];W[kq];B[pp];W[op]
;B[po];W[oq];B[rp];W[ql];B[oo];W[no];B[pl];W[pm];B[np];W[qq]
;B[om];W[ol];B[pk];W[qp];B[on];W[rm];B[mo];W[nr];B[rl];W[rk]
;B[qm];W[dp];B[dq];W[ql];B[or];W[mp];B[nn];W[mq];B[qm];W[bp]
;B[co];W[ql];B[no];W[pr];B[qm];W[dd];B[pn];W[ed];B[bo];W[eg]
;B[ef];W[dg];B[ge];W[gh];B[gf];W[gg];B[ek];W[ig];B[fd];W[en]
;B[bn];W[ip];B[dm];W[ff];B[cb];W[fe];B[hp];W[ho];B[hq];W[el]
;B[dl];W[fk];B[ej];W[fp];B[go];W[hn];B[fo];W[em];B[dn];W[eo]
;B[gp];W[ib];B[gc];W[pg];B[qg];W[ng];B[qc];W[re];B[pf];W[of]
;B[rc];W[ob];B[ph];W[qo];B[rn];W[mi];B[og];W[oe];B[qe];W[rd]
;B[rf];W[pd];B[gm];W[gl];B[fm];W[fl];B[lj];W[mj];B[lk];W[ro]
;B[hl];W[hk];B[ik];W[dk];B[bi];W[di];B[dj];W[dh];B[hj];W[gj]
;B[li];W[lh];B[kh];W[lg];B[jn];W[do];B[cl];W[ij];B[gk];W[bl]
;B[cm];W[hk];B[jk];W[lo];B[hi];W[hm];B[gk];W[bm];B[cn];W[hk]
;B[il];W[cq];B[bq];W[ii];B[sm];W[jo];B[kn];W[fq];B[ep];W[cj]
;B[bk];W[er];B[cr];W[gr];B[gk];W[fj];B[ko];W[kp];B[hr];W[jr]
;B[nh];W[mh];B[mk];W[bb];B[da];W[jh];B[ic];W[id];B[hb];W[jb]
;B[oj];W[fn];B[fs];W[fr];B[gs];W[es];B[hs];W[gn];B[kr];W[is]
;B[dr];W[fi];B[bj];W[hd];B[gd];W[ln];B[lm];W[oi];B[oh];W[ni]
;B[pi];W[ki];B[kj];W[ji];B[so];W[rq];B[if];W[jf];B[hh];W[hf]
;B[he];W[ie];B[hg];W[ba];B[ca];W[sp];B[im];W[sn];B[rm];W[pe]
;B[qf];W[if];B[hk];W[nj];B[nk];W[lr];B[mn];W[af];B[ag];W[ch]
;B[bh];W[lp];B[ia];W[ja];B[ha];W[sf];B[sg];W[se];B[eh];W[fh]
;B[in];W[ih];B[ae];W[so];B[af]"

然后我們將它轉換為一個矩陣：

str = gsub("\\n", "", str)
step = strsplit(str, ";")[[1]]
type = gsub("(B|W).*", "\\1", step)
row = gsub("(B|W)\\[(.).\\]", "\\2", step)
column = gsub("(B|W)\\[.(.)\\]", "\\2", step)

mat = matrix(nrow = 19, ncol = 19)
rownames(mat) = letters[1:19]
colnames(mat) = letters[1:19]
for(i in seq_along(row)) {
    mat[row[i], column[i]] = type[i]
}
mat

##   a   b   c   d   e   f   g   h   i   j   k   l   m   n   o   p   q   r   s  
## a NA  NA  NA  "W" "B" "B" "B" NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA 
## b "W" "W" "W" "B" "W" "B" "B" "B" "B" "B" "B" "W" "W" "B" "B" "W" "B" NA  NA 
## c "B" "B" "W" "W" "B" "W" NA  "W" "B" "W" "W" "B" "B" "B" "B" "B" "W" "B" NA 
## d "B" "W" "B" "W" "B" "W" "W" "W" "W" "B" "W" "B" "B" "B" "W" "W" "B" "B" NA 
## e NA  "B" "B" "W" "B" "B" "W" "B" "B" "B" "B" "W" "W" "W" "W" "B" "B" "W" "W"
## f NA  NA  NA  "B" "W" "W" NA  "W" "W" "W" "W" "W" "B" "W" "B" "W" "W" "W" "B"
## g NA  NA  "B" "B" "B" "B" "W" "W" NA  "W" "B" "W" "B" "W" "B" "B" NA  "W" "B"
## h "B" "B" "B" "W" "B" "W" "B" "B" "B" "B" "B" "B" "W" "W" "W" "B" "B" "B" "B"
## i "B" "W" "B" "W" "W" "W" "W" "W" "W" "W" "B" "B" "B" "B" NA  "W" "B" NA  "W"
## j "W" "W" "W" NA  "W" "W" NA  "W" "W" NA  "B" NA  NA  "B" "W" "W" "B" "W" NA 
## k NA  NA  NA  NA  NA  NA  NA  "B" "W" "B" NA  NA  NA  "B" "B" "W" "W" "B" NA 
## l NA  NA  NA  NA  NA  NA  "W" "W" "B" "B" "B" NA  "B" "W" "W" "W" "W" "W" NA 
## m NA  NA  NA  NA  NA  NA  NA  "W" "W" "W" "B" NA  NA  "B" "B" "W" "W" NA  NA 
## n NA  NA  NA  NA  NA  NA  "W" "B" "W" "W" "B" NA  NA  "B" "B" "B" "B" "W" NA 
## o NA  "W" NA  "W" "W" "W" "B" "B" "W" "B" NA  "W" "B" "B" "B" "W" "W" "B" NA 
## p NA  NA  NA  "W" "W" "B" "W" "B" "B" NA  "B" "B" "W" "B" "B" "B" "W" "W" NA 
## q NA  NA  "B" "W" "B" "B" "B" "B" NA  NA  "B" "W" "B" "W" "W" "W" "W" NA  NA 
## r NA  NA  "B" "W" "W" "B" NA  NA  NA  NA  "W" "B" "B" "B" "W" "B" "W" NA  NA 
## s NA  NA  NA  NA  "W" "W" "B" NA  NA  NA  NA  NA  "B" "W" "W" "W" NA  NA  NA

基于矩陣的值我們放上黑子和白子。

Heatmap(mat, name = "go", rect_gp = gpar(type = "none"),
    cell_fun = function(j, i, x, y, w, h, col) {
        grid.rect(x, y, w, h, gp = gpar(fill = "#dcb35c", col = NA))
        if(i == 1) {
            grid.segments(x, y-h*0.5, x, y)
        } else if(i == nrow(mat)) {
            grid.segments(x, y, x, y+h*0.5)
        } else {
            grid.segments(x, y-h*0.5, x, y+h*0.5)
        }
        if(j == 1) {
            grid.segments(x, y, x+w*0.5, y)        
        } else if(j == ncol(mat)) {
            grid.segments(x-w*0.5, y, x, y)
        } else {
            grid.segments(x-w*0.5, y, x+w*0.5, y)
        }

        if(i %in% c(4, 10, 16) & j %in% c(4, 10, 16)) {
            grid.points(x, y, pch = 16, size = unit(2, "mm"))
        }

        r = min(unit.c(w, h))*0.45
        if(is.na(mat[i, j])) {
        } else if(mat[i, j] == "W") {
            grid.circle(x, y, r, gp = gpar(fill = "white", col = "white"))
        } else if(mat[i, j] == "B") {
            grid.circle(x, y, r, gp = gpar(fill = "black", col = "black"))
        }
    },
    col = c("B" = "black", "W" = "white"),
    show_row_names = FALSE, show_column_names = FALSE,
    column_title = "One famous GO game",
    heatmap_legend_param = list(title = "Player", at = c("B", "W"), 
        labels = c("player1", "player2"), grid_border = "black")
)

將熱圖主體設置為光柵圖像

將圖形以PDF格式保存時保存質量的最好方式。然而，如果行數太多（> 10000），輸出的PDF文件將非常之大。將熱圖渲染為光柵圖像可以減少文件大小。Heatmap()函數中有4個選項控制如何生成光柵圖像：use_raster、raster_device、raster_quality和raster_device_param。

你可以通過raster_device選擇圖像設備（png、jpeg和tiff），使用raster_quality控制圖像質量，raster_device_param可以傳入更多參數。

會話信息

sessionInfo()
## R version 3.5.1 Patched (2018-07-12 r74967)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
## 
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
##  [4] LC_COLLATE=C               LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] stats4    parallel  grid      stats     graphics  grDevices utils     datasets  methods  
## [10] base     
## 
## other attached packages:
##  [1] dendextend_1.9.0      dendsort_0.3.3        cluster_2.0.7-1       IRanges_2.16.0       
##  [5] S4Vectors_0.20.0      BiocGenerics_0.28.0   HilbertCurve_1.12.0   circlize_0.4.4       
##  [9] ComplexHeatmap_1.20.0 knitr_1.20            markdown_0.8         
## 
## loaded via a namespace (and not attached):
##  [1] mclust_5.4.1           Rcpp_0.12.19           mvtnorm_1.0-8          lattice_0.20-35       
##  [5] png_0.1-7              class_7.3-14           assertthat_0.2.0       mime_0.6              
##  [9] R6_2.3.0               GenomeInfoDb_1.18.0    plyr_1.8.4             evaluate_0.12         
## [13] ggplot2_3.1.0          highr_0.7              pillar_1.3.0           GlobalOptions_0.1.0   
## [17] zlibbioc_1.28.0        rlang_0.3.0.1          lazyeval_0.2.1         diptest_0.75-7        
## [21] kernlab_0.9-27         whisker_0.3-2          GetoptLong_0.1.7       stringr_1.3.1         
## [25] RCurl_1.95-4.11        munsell_0.5.0          compiler_3.5.1         pkgconfig_2.0.2       
## [29] shape_1.4.4            nnet_7.3-12            tidyselect_0.2.5       gridExtra_2.3         
## [33] tibble_1.4.2           GenomeInfoDbData_1.2.0 viridisLite_0.3.0      crayon_1.3.4          
## [37] dplyr_0.7.7            MASS_7.3-51            bitops_1.0-6           gtable_0.2.0          
## [41] magrittr_1.5           scales_1.0.0           stringi_1.2.4          XVector_0.22.0        
## [45] viridis_0.5.1          flexmix_2.3-14         bindrcpp_0.2.2         robustbase_0.93-3     
## [49] fastcluster_1.1.25     HilbertVis_1.40.0      rjson_0.2.20           RColorBrewer_1.1-2    
## [53] tools_3.5.1            fpc_2.1-11.1           glue_1.3.0             trimcluster_0.1-2.1   
## [57] DEoptimR_1.0-8         purrr_0.2.5            colorspace_1.3-2       GenomicRanges_1.34.0  
## [61] prabclus_2.2-6         bindr_0.1.1            modeltools_0.2-22