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目錄

第一章 簡介

1.1 設(shè)計理念

1.2 各章節(jié)速覽

第二章 單個熱圖

2.1 顏色

2.2 行標題/列標題

2.3 聚類

2.3.1 距離計算方法

2.3.2 聚類方法

2.3.3 自定義聚類樹顏色

2.3.4 重新排列聚類樹

2.4 改變行/列順序

2.5 Seriation包排序

2.6 行名/列名

2.7 熱圖分割

2.7.1 通過K-means方法分割

2.7.2 通過離散型變量分割

2.7.3 通過聚類樹分割

2.7.4 切片順序

2.7.5 分割標題

2.7.6 分割的圖形參數(shù)

2.7.7 分割寬度

2.7.8 分割注釋條

2.8 光柵圖（略）

2.9 自定義熱圖主體

2.9.1 cell_fun

2.9.2 layer_fun

2.10 熱圖大小

---

本系列是對ComplexeHeatmap包的學習筆記，部分內(nèi)容根據(jù)自己的理解有適當?shù)母膭?#xff0c;但總體不影響原文。如有不明之處，以原文為準。原文請見：ComplexHeatmap Complete Reference

第一章 簡介

復雜熱圖可用于展示同一個數(shù)據(jù)集或不同數(shù)據(jù)集之間的關(guān)系或揭示內(nèi)部規(guī)律。ComplexHeatmap包可提供靈活的熱圖展示及高度自定義的注釋圖形。

1.1 設(shè)計理念

一個完整的熱圖由熱圖主體和熱圖組件構(gòu)成。熱圖主體可以被分為不同的行和列，熱圖組件包括行/列標題，聚類樹，行名/列名，行注釋條/列注釋條。

熱圖列表由多個熱圖主體和熱圖注釋組成，但不同的熱圖主體和注釋被有序排列，使得彼此之間具有較好的可比性。

ComplexHeatmap包是面向?qū)ο蟮?#xff0c;主要包括以下類：

• Heatmap class: 單個熱圖，包括熱圖主體，行名/列名，標題，聚類樹，行注釋條/列注釋條；

• HeatmapList class: 多個熱圖主體和熱圖注釋；

• HeatmapAnnotation class: 定義一系列的行注釋/列注釋，這些注釋既可以作為熱圖組件，又可以獨立于熱圖；

還有一些其他類：

• SingleAnnotation class: 定義單個行注釋/列注釋，包含在 HeatmapAnnotation class中；

• ColorMapping class: 映射顏色，包括熱圖主體顏色和各種注釋的顏色

• AnnotationFunction class: 創(chuàng)建用戶自定義的注釋 ComplexHeatmap是基于grid的，充分利用此包需要用戶了解grid繪圖系統(tǒng)的知識。

1.2 各章節(jié)速覽

2. 單個熱圖

介紹單個熱圖的組成

3. 熱圖注釋

熱圖注釋概念，如何繪制簡單注釋和復雜注釋，簡單注釋和復雜注釋的不同

4. 熱圖列表

如何繪制多個熱圖和注釋，它們的位置排布是怎樣安排的

5. 圖例

如何繪制熱圖主體和注釋條的圖例，如何自定義圖例

6. 熱圖裝飾

如何添加用戶自定義圖形

7. 瀑布圖

8. UpSet plot

9. 其他高階圖形

10. 和其他R包交互

11. 交互式熱圖

12. 更多例子

第二章 單個熱圖

單個熱圖是最常見的可視化圖形，雖然ComplexHeatmap包的閃光點是可以同時繪制多個熱圖，但是作為基本圖形，對單個熱圖的繪制也是很重要的。

首先隨機生成一個矩陣

set.seed(123) nr1 = 4; nr2 = 8; nr3 = 6; nr = nr1 + nr2 + nr3 nc1 = 6; nc2 = 8; nc3 = 10; nc = nc1 + nc2 + nc3 mat = cbind(rbind(matrix(rnorm(nr1*nc1, mean = 1, ? sd = 0.5), nr = nr1),matrix(rnorm(nr2*nc1, mean = 0, ? sd = 0.5), nr = nr2),matrix(rnorm(nr3*nc1, mean = 0, ? sd = 0.5), nr = nr3)),rbind(matrix(rnorm(nr1*nc2, mean = 0, ? sd = 0.5), nr = nr1),matrix(rnorm(nr2*nc2, mean = 1, ? sd = 0.5), nr = nr2),matrix(rnorm(nr3*nc2, mean = 0, ? sd = 0.5), nr = nr3)),rbind(matrix(rnorm(nr1*nc3, mean = 0.5, sd = 0.5), nr = nr1),matrix(rnorm(nr2*nc3, mean = 0.5, sd = 0.5), nr = nr2),matrix(rnorm(nr3*nc3, mean = 1, ? sd = 0.5), nr = nr3))) mat = mat[sample(nr, nr), sample(nc, nc)] # random shuffle rows and columns rownames(mat) = paste0("row", seq_len(nr)) colnames(mat) = paste0("column", seq_len(nc)) dim(mat) ## [1] 18 24

Heatmap()函數(shù)是繪制熱圖的基本函數(shù)，它會繪制一個熱圖主體，行名，列名，聚類樹和注釋。默認的顏色是黃色系的。

library(ComplexHeatmap) ## 載入需要的程輯包：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)) ## ======================================== heatmap(mat)

2.1 顏色

對于熱圖可視化，顏色是數(shù)據(jù)矩陣的主要表示形式。在大多數(shù)情況下，熱圖用于可視化連續(xù)數(shù)值矩陣。在這種情況下，用戶應(yīng)提供顏色映射功能。顏色映射函數(shù)接受數(shù)值型向量，并返回對應(yīng)的顏色向量。用戶應(yīng)始終使用circlize::colorRamp2()函數(shù)在Heatmap()中生成顏色映射。colorRamp2()的兩個參數(shù)是離散型數(shù)值向量和對應(yīng)的顏色向量。colorRamp2()通過LAB顏色空間在每個間隔內(nèi)線性插值顏色。另外，使用colorRamp2()有助于生成帶有適當刻度線的圖例。

在以下示例中，線性插值-2和2之間的值以獲得相應(yīng)的顏色，大于2的值都映射為紅色，小于-2的值都映射為綠色。

library(circlize) ## ======================================== ## circlize version 0.4.13 ## CRAN page: https://cran.r-project.org/package=circlize ## Github page: https://github.com/jokergoo/circlize ## Documentation: https://jokergoo.github.io/circlize_book/book/ ## ## If you use it in published research, please cite: ## Gu, Z. circlize implements and enhances circular visualization ## ? in R. Bioinformatics 2014. ## ## This message can be suppressed by: ## ? suppressPackageStartupMessages(library(circlize)) ## ======================================== col_fun = colorRamp2(c(-2, 0, 2), c("green", "white", "red")) col_fun(seq(-3, 3)) ## [1] "#00FF00FF" "#00FF00FF" "#B1FF9AFF" "#FFFFFFFF" "#FF9E81FF" "#FF0000FF" ## [7] "#FF0000FF" Heatmap(mat, name = "mat", col = col_fun)

使用colorRamp2()可以精確控制顏色映射范圍，并且不會受到極端值的影響。

mat2 = mat mat2[1, 1] = 100000 Heatmap(mat2, name = "mat", col = col_fun, column_title = "a matrix with outliers")

另外，使用colorRamp2()可以使得多個熱圖之間的顏色具有可比性，如下所示，在3個熱圖中，相同的顏色總是對應(yīng)相同的數(shù)值：

p1 <- Heatmap(mat, name = "mat", col = col_fun, column_title = "mat") p2 <- Heatmap(mat/4, name = "mat/4", col = col_fun, column_title = "mat/4") p3 <- Heatmap(abs(mat), name = "abs(mat)", col = col_fun, column_title = "abs(mat)") p1 + p2 + p3

如果矩陣是連續(xù)的，也可以簡單地提供顏色的向量，并且顏色將被線性插值。 但是此方法對異常值不友好，因為映射總是從矩陣中的最小值開始，以最大值結(jié)束。

Heatmap(mat, name = "mat", col = rev(rainbow(10)), column_title = "set a color vector for continuous matrix")

還可以可視化NA，使用na_col = "xxx"指定NA的顏色：

mat_with_na = mat na_index = sample(c(TRUE, FALSE), nrow(mat)*ncol(mat), replace = TRUE, prob = c(1, 9)) mat_with_na[na_index] = NA Heatmap(mat_with_na, name = "mat with na", na_col = "black", column_title = "a matrix with na")

改變colorRamp2()函數(shù)的線性插值

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")p1 <- Heatmap(mat, name = "mat1", col = f1, column_title = "color space in LAB") p2 <- Heatmap(mat, name = "mat2", col = f2, column_title = "color space in RGB") p1 + p2

熱圖邊框的樣式通過border_gp函數(shù)控制，熱圖每個小格子的樣式由rect_gp = gpar()函數(shù)控制。

Heatmap(mat, name = "mat1", border_gp = gpar(lty = 2, col = "red"), column_title = "set heatmap border")

Heatmap(mat, name = "mat2", column_title = "set cell border", rect_gp = gpar(col = "white", lty = 1, lwd = 2))

如果設(shè)置type = "none"，熱圖主體部分不會畫任何東西，可以通過cell_fun和layer_fun自定義，后面會介紹。

Heatmap(mat, name = "lalala", rect_gp = gpar(type="none"), column_title = "no heatmap body")

2.2 行標題/列標題

添加行標題和列標題：

Heatmap(mat, name = "color", column_title = "i am column title", row_title = "i am row title")

更改標題位置：

Heatmap(mat, name = "color", column_title = "i am column title", row_title = "i am row title", column_title_side = "bottom", row_title_side = "right")

旋轉(zhuǎn)行/列標題：

Heatmap(mat, name = "color", column_title = "i am title", column_title_rot = 90, row_title = "i am row title", row_title_rot = 0)

更改行/列標題樣式：

Heatmap(mat, name = "color", column_title = "i am column title", row_title = "i am row title", column_title_gp = gpar(fontsize = 20, fontface = "bold"),row_title_gp = gpar(col = "steelblue", fontsize = 16, fill = "red", border = "green"))

標題是公式：

Heatmap(mat, name = "mat", column_title = expression(hat(beta) == (X^t * X)^{-1} * X^t * y))

2.3 聚類

支持各種自定義

關(guān)閉聚類（不聚類）：

p1 <- Heatmap(mat) p2 <- Heatmap(mat, cluster_rows = F, cluster_columns = F) p1 + p2

聚類但是不顯示聚類樹：

Heatmap(mat, show_row_dend = T, show_column_dend = F)

調(diào)整聚類樹的位置：

Heatmap(mat, row_dend_side = "right", column_dend_side = "bottom")

調(diào)整聚類樹的高度和寬度：

Heatmap(mat, row_dend_width = unit(4, "cm"), column_dend_height = unit(3, "cm"))

2.3.1 距離計算方法

支持：

• pearson, spearson, kendall,三選一；

• 自定義距離計算函數(shù)

p1 <- Heatmap(mat, name = "mat1", clustering_distance_rows = "pearson",column_title = "pre-defined distance method(1-pearson)") p2 <- Heatmap(mat, name = "mat2", clustering_distance_rows = function(m) dist(m),column_title = "a function that calculates distance matrix") p1 + p2

2.3.2 聚類方法

支持hclust函數(shù)提供的方法

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

2.3.3 自定義聚類樹顏色

可以借助dendextend包自定義聚類樹的顏色，具體做法如下：

library(dendextend) ## ## --------------------- ## Welcome to dendextend version 1.15.1 ## Type citation('dendextend') for how to cite the package. ## ## Type browseVignettes(package = 'dendextend') for the package vignette. ## The github page is: https://github.com/talgalili/dendextend/ ## ## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues ## Or contact: <tal.galili@gmail.com> ## ## To suppress this message use: suppressPackageStartupMessages(library(dendextend)) ## --------------------- ## ## 載入程輯包：'dendextend' ## The following object is masked from 'package:stats': ## ## cutree row_dend = as.dendrogram(hclust(dist(mat))) row_dend = color_branches(row_dend, k = 2) # `color_branches()` returns a dendrogram object Heatmap(mat, name = "mat", cluster_rows = row_dend)

row_dend_gp和column_dend_gp參數(shù)控制聚類樹樣式，使用此參數(shù)會覆蓋row_dend和column_dend:

Heatmap(mat, name = "mat", cluster_rows = row_dend, row_dend_gp = gpar(col = "red"))

從2.5.6版本以后，可以通過提供合適的nodePar給樹的節(jié)點使用不同的形狀：

row_dend = dendrapply(row_dend, function(d) {attr(d, "nodePar") = list(cex = 0.8, pch = sample(20, 1), col = rand_color(1))return(d) }) Heatmap(mat, name = "mat", cluster_rows = row_dend, row_dend_width = unit(2, "cm"))

2.3.4 重新排列聚類樹

在Heatmap()函數(shù)中，對聚類樹進行重新排序，以使具有較大差異的行/列彼此分離（請參閱reorder.dendrogram()文檔）。 此處的差異（或稱權(quán)重）是通過行/列的均值來計算的。如果將其設(shè)置為邏輯值，則row_dend_reorder和column_dend_reorder控制是否應(yīng)用聚類樹重排序。 如果將兩個參數(shù)設(shè)置為數(shù)值向量，則它們還控制重排序的權(quán)重（會被傳遞給reorder.dendrogram()的wts參數(shù)）。可以通過設(shè)置row_dend_reorder = F來關(guān)閉重新排序。

默認情況下，如果將cluster_rows/cluster_columns設(shè)置為邏輯值或聚類函數(shù)，聚類樹會重新排序。 如果將cluster_rows/cluster_columns設(shè)置為聚類對象，則會關(guān)閉重排序。

m2 = matrix(1:100, nr = 10, byrow = TRUE) Heatmap(m2, name = "mat1", row_dend_reorder = FALSE, column_title = "no reordering")

Heatmap(m2, name = "mat2", row_dend_reorder = TRUE, column_title = "apply reordering")

還有非常多重新排序聚類樹的方法，可以使用使用dendsort包，所有的重新排序的方法都是返回排列好的聚類樹對象，因此我們可以先生成排列好的行/列聚類樹對象，然后再傳遞給cluster_rows和cluster_columns參數(shù)。

Heatmap(mat, name = "mat", column_title = "default reordering")

library(dendsort) row_dend = dendsort(hclust(dist(mat))) col_dend = dendsort(hclust(dist(t(mat)))) Heatmap(mat, name = "mat", cluster_rows = row_dend, cluster_columns = col_dend,column_title = "reorder by dendsort")

2.4 改變行/列順序

聚類可以改變行/列順序，我們也可以通過row_order和column_order手動改變行/列順序

Heatmap(mat, name = "mat", row_order = order(as.numeric(gsub("row", "", rownames(mat)))), column_order = order(as.numeric(gsub("column", "", colnames(mat)))),column_title = "reorder matrix")

Heatmap(mat, name = "mat", row_order = sort(rownames(mat)), column_order = sort(colnames(mat)),column_title = "reorder matrix by row/column names")

2.5 Seriation包排序

Seriation包是專門用來排序的，（詳見: Make Patterns Pop Out of Heatmaps with Seriation），一些用法如下：

library(seriation) o = seriate(max(mat) - mat, method = "BEA_TSP") Heatmap(max(mat) - mat, name = "mat", row_order = get_order(o, 1), column_order = get_order(o, 2),column_title = "seriation by BEA_TSP method")

o1 = seriate(dist(mat), method = "TSP") o2 = seriate(dist(t(mat)), method = "TSP") Heatmap(mat, name = "mat", row_order = get_order(o1), column_order = get_order(o2),column_title = "seriation from the distance matrix")

o1 = seriate(dist(mat), method = "GW") ## Registered S3 method overwritten by 'gclus': ## method from ## reorder.hclust seriation o2 = seriate(dist(t(mat)), method = "GW")Heatmap(mat, name = "mat", cluster_rows = as.dendrogram(o1[[1]]), cluster_columns = as.dendrogram(o2[[1]]))

2.6 行名/列名

默認顯示，如果不想顯示行名/列名，使用show_row_names和show_column_names參數(shù)

Heatmap(mat, name = "mat", show_row_names = F, show_column_names = F)

調(diào)整位置，使用row_names_side和column_names_side：

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

調(diào)整行名/列名樣式，使用row_names_gp和column_names_gp:

Heatmap(mat, name = "mat", row_names_gp = gpar(fontsize = 20), column_names_gp = gpar(col = c(rep("red", 10), rep("blue", 8))))

居中對齊：

Heatmap(mat, name = "mat", row_names_centered = TRUE, column_names_centered = TRUE)

旋轉(zhuǎn)方向：

Heatmap(mat, name = "mat", column_names_rot = 45)

Heatmap(mat, name = "mat", column_names_rot = 45, column_names_side = "top",column_dend_side = "bottom")

行名/列名太長怎么辦？也能調(diào)整：

mat2 = mat rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "") Heatmap(mat2, name = "mat", row_title = "default row_names_max_width")

Heatmap(mat2, name = "mat", row_title = "row_names_max_width as length of a*",row_names_max_width = max_text_width(rownames(mat2), gp = gpar(fontsize = 12)))

自定義行名/列名，可用于解決原始矩陣行名/列名不能有重復的問題，或使用特殊符號等：

# use a named vector to make sure the correspondance between row names and row labels is correct row_labels = structure(paste0(letters[1:24], 1:24), names = paste0("row", 1:24)) column_labels = structure(paste0(LETTERS[1:24], 1:24), names = paste0("column", 1:24)) row_labels ## row1 row2 row3 row4 row5 row6 row7 row8 row9 row10 row11 row12 row13 ## "a1" "b2" "c3" "d4" "e5" "f6" "g7" "h8" "i9" "j10" "k11" "l12" "m13" ## row14 row15 row16 row17 row18 row19 row20 row21 row22 row23 row24 ## "n14" "o15" "p16" "q17" "r18" "s19" "t20" "u21" "v22" "w23" "x24" Heatmap(mat, name = "mat", row_labels = row_labels[rownames(mat)], column_labels = column_labels[colnames(mat)])

Heatmap(mat, name = "mat", row_labels = expression(alpha, beta, gamma, delta, epsilon, zeta, eta, theta, iota, kappa, lambda, mu, nu, xi, omicron, pi, rho, sigma))

2.7 熱圖分割

主要通過四個參數(shù)調(diào)整：

• row_km

• row_split

• column_km

• column_split

2.7.1 通過K-means方法分割

Heatmap(mat, name = "mat", row_km = 2)

Heatmap(mat, name = "mat", column_km = 3)

Heatmap(mat, name = "mat", row_km = 2, column_km = 3)

2.7.2 通過離散型變量分割

# split by a vector Heatmap(mat, name = "mat", row_split = rep(c("A", "B"), 9), column_split = rep(c("C", "D"), 12))

# split by a data frame Heatmap(mat, name = "mat", row_split = data.frame(rep(c("A", "B"), 9), rep(c("C", "D"), each = 9)))

# split on both dimensions Heatmap(mat, name = "mat", row_split = factor(rep(c("A", "B"), 9)),column_split = factor(rep(c("C", "D"), 12)))

2.7.3 通過聚類樹分割

Heatmap(mat, name = "mat", row_split = 2, column_split = 3)

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

split = data.frame(cutree(hclust(dist(mat)), k = 2), rep(c("A", "B"), 9)) Heatmap(mat, name = "mat", row_split = split)

2.7.4 切片順序

默認情況下，當把row_split/column_split設(shè)置為類別變量（向量或數(shù)據(jù)框）或設(shè)置row_km/column_km時，會對切片的平均值使用聚類，以顯示切片級別中的層次結(jié)構(gòu)。 在這種情況下，無法精確地控制切片的順序，因為它是由切片的聚類控制的。但是可以將cluster_row_slices或cluster_column_slices設(shè)置為FALSE以關(guān)閉切片聚類，然后就可以精確地控制切片的順序了。

如果沒有切片聚類，則可以通過row_split/column_split中的每個變量的級別來控制每個切片的順序（在這種情況下，每個變量應(yīng)該是一個因子）。 如果所有變量都是字符，則默認順序為unique(row_split)或unique(column_split)。

Heatmap(mat, name = "mat", row_split = rep(LETTERS[1:3], 6),column_split = rep(letters[1:6], 4))

# clustering is similar as previous heatmap with branches in some nodes in the dendrogram flipped Heatmap(mat, name = "mat", row_split = factor(rep(LETTERS[1:3], 6), levels = LETTERS[3:1]),column_split = factor(rep(letters[1:6], 4), levels = letters[6:1]))

# now the order is exactly what we set Heatmap(mat, name = "mat", row_split = factor(rep(LETTERS[1:3], 6), levels = LETTERS[3:1]),column_split = factor(rep(letters[1:6], 4), levels = letters[6:1]),cluster_row_slices = FALSE, cluster_column_slices = FALSE)

2.7.5 分割標題

split = data.frame(rep(c("A", "B"), 9), rep(c("C", "D"), each = 9)) Heatmap(mat, name = "mat", row_split = split, row_title = "%s|%s")

map = c("A" = "aaa", "B" = "bbb", "C" = "333", "D" = "444") Heatmap(mat, name = "mat", row_split = split, row_title = "@{map[ x[1] ]}|@{map[ x[2] ]}")

Heatmap(mat, name = "mat", row_split = split, row_title = "{map[ x[1] ]}|{map[ x[2] ]}")

Heatmap(mat, name = "mat", row_split = split, row_title = "%s|%s", row_title_rot = 0)

Heatmap(mat, name = "mat", row_split = 2, row_title = "cluster_%s")

Heatmap(mat, name = "mat", row_split = split, row_title = c("top_slice", "middle_top_slice", "middle_bottom_slice", "bottom_slice"),row_title_rot = 0)

Heatmap(mat, name = "mat", row_split = split, row_title = "there are four slices")

ht = Heatmap(mat, name = "mat", row_split = split, row_title = "%s|%s") # This row_title is actually a heatmap-list-level row title draw(ht, row_title = "I am a row title")

Heatmap(mat, name = "mat", row_split = split, row_title = NULL)

2.7.6 分割的圖形參數(shù)

# 默認情況下標題頂部沒有空間，現(xiàn)在我們增加4pt的空間 ht_opt$TITLE_PADDING = unit(c(4, 4), "points") Heatmap(mat, name = "mat", row_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)),column_km = 3, column_title_gp = gpar(fill = c("red", "blue", "green"), font = 1:3),column_names_gp = gpar(col = c("green", "orange", "purple"), fontsize = c(10, 14, 8)))

2.7.7 分割寬度

Heatmap(mat, name = "mat", row_km = 3, row_gap = unit(5, "mm"))

Heatmap(mat, name = "mat", row_km = 3, row_gap = unit(c(2, 4), "mm"))

Heatmap(mat, name = "mat", row_km = 2, column_km = 3, border = TRUE)

Heatmap(mat, name = "mat", row_km = 2, column_km = 3, row_gap = unit(0, "mm"), column_gap = unit(0, "mm"), border = TRUE)

2.7.8 分割注釋條

Heatmap(mat, name = "mat", row_km = 2, column_km = 3,top_annotation = HeatmapAnnotation(foo1 = 1:24, bar1 = anno_points(runif(24))),right_annotation = rowAnnotation(foo2 = 18:1, bar2 = anno_barplot(runif(18))) )

2.8 光柵圖（略）

2.9 自定義熱圖主體

2.9.1 cell_fun

用來調(diào)整每一個小格子，需要提供一個函數(shù)，函數(shù)共有7個參數(shù)（參數(shù)名字可以不同，但是順序必須一樣）：

• i: 行索引

• j: 列索引

• x: 小格子中心點橫坐標

• y: 小格子中心點縱坐標

• width: 小格子寬度

• height: 小格子高度

• fill: 小格子填充色

最常見的用法是在熱圖中添加數(shù)字：

small_mat = mat[1:9, 1:9] col_fun = colorRamp2(c(-2, 0, 2), c("green", "white", "red")) Heatmap(small_mat, name = "mat", col = col_fun,cell_fun = function(j, i, x, y, width, height, fill) {grid.text(sprintf("%.1f", small_mat[i, j]), x, y, gp = gpar(fontsize = 10)) })

也可以選擇只添加大于0的數(shù)字：

Heatmap(small_mat, name = "mat", col = col_fun,cell_fun = function(j, i, x, y, width, height, fill) {if(small_mat[i, j] > 0)grid.text(sprintf("%.1f", small_mat[i, j]), x, y, gp = gpar(fontsize = 10)) })

直接分割熱圖，無需對cell_fun()做啥：

Heatmap(small_mat, name = "mat", col = col_fun,row_km = 2, column_km = 2,cell_fun = function(j, i, x, y, width, height, fill) {grid.text(sprintf("%.1f", small_mat[i, j]), x, y, gp = gpar(fontsize = 10)) })

可視化相關(guān)性矩陣：

cor_mat = cor(small_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 = 10))}}, cluster_rows = FALSE, cluster_columns = FALSE,show_row_names = FALSE, show_column_names = FALSE)

畫一個棋盤：

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]"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)go_mat = matrix(nrow = 19, ncol = 19) rownames(go_mat) = letters[1:19] colnames(go_mat) = letters[1:19] for(i in seq_along(row)) {go_mat[row[i], column[i]] = type[i] } go_mat[1:4, 1:4] ## a b c d ## a NA NA NA "W" ## b "W" "W" "W" "B" ## c "B" "B" "W" "W" ## d "B" "W" "B" "W" Heatmap(go_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(go_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(go_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))*0.45if(is.na(go_mat[i, j])) {} else if(go_mat[i, j] == "W") {grid.circle(x, y, r, gp = gpar(fill = "white", col = "white"))} else if(go_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"), border = "black") )

2.9.2 layer_fun

用法差不多，layer_fun其實是cell_fun的“向量化版本”，速度更快，但其實作用是一樣的，學會一個就可以，都會更好。

# code only for demonstration Heatmap(..., layer_fun = function(j, i, x, y, w, h, fill) {...}) # or you can capitalize the arguments to mark they are vectors, # the names of the argumetn do not matter Heatmap(..., layer_fun = function(J, I, X, Y, W, H, F) {...}) col_fun = colorRamp2(c(-2, 0, 2), c("green", "white", "red")) Heatmap(small_mat, name = "mat", col = col_fun,layer_fun = function(j, i, x, y, width, height, fill) {# since grid.text can also be vectorizedgrid.text(sprintf("%.1f", pindex(small_mat, i, j)), x, y, gp = gpar(fontsize = 10)) })

Heatmap(small_mat, name = "mat", col = col_fun,row_km = 2, column_km = 2,layer_fun = function(j, i, x, y, width, height, fill) {v = pindex(small_mat, i, j)grid.text(sprintf("%.1f", v), x, y, gp = gpar(fontsize = 10))if(sum(v > 0)/length(v) > 0.75) {grid.rect(gp = gpar(lwd = 2, fill = "transparent"))} })

Heatmap(small_mat, name = "mat", col = col_fun,row_km = 2, column_km = 2,layer_fun = function(j, i, x, y, w, h, fill) {# restore_matrix() is explained after this chunk of codeind_mat = restore_matrix(j, i, x, y)for(ir in seq_len(nrow(ind_mat))) {# start from the second columnfor(ic in seq_len(ncol(ind_mat))[-1]) {ind1 = ind_mat[ir, ic-1] # previous columnind2 = ind_mat[ir, ic] # current columnv1 = small_mat[i[ind1], j[ind1]]v2 = small_mat[i[ind2], j[ind2]]if(v1 * v2 > 0) { # if they have the same signcol = ifelse(v1 > 0, "darkred", "darkgreen")grid.segments(x[ind1], y[ind1], x[ind2], y[ind2],gp = gpar(col = col, lwd = 2))grid.points(x[c(ind1, ind2)], y[c(ind1, ind2)], pch = 16, gp = gpar(col = col), size = unit(4, "mm"))}}}} )

2.10 熱圖大小

heatmap_width和heatmap_height控制整個熱圖的大小（包括圖例），width和height只控制熱圖主體的大小

Heatmap(mat, name = "mat", width = unit(8, "cm"), height = unit(8, "cm"))

Heatmap(mat, name = "mat", heatmap_width = unit(8, "cm"), heatmap_height = unit(8, "cm"))

sessionInfo() ## R version 4.1.0 (2021-05-18) ## Platform: x86_64-w64-mingw32/x64 (64-bit) ## Running under: Windows 10 x64 (build 19044) ## ## Matrix products: default ## ## locale: ## [1] LC_COLLATE=Chinese (Simplified)_China.936 ## [2] LC_CTYPE=Chinese (Simplified)_China.936 ## [3] LC_MONETARY=Chinese (Simplified)_China.936 ## [4] LC_NUMERIC=C ## [5] LC_TIME=Chinese (Simplified)_China.936 ## ## attached base packages: ## [1] grid stats graphics grDevices utils datasets methods ## [8] base ## ## other attached packages: ## [1] seriation_1.3.0 dendsort_0.3.4 dendextend_1.15.1 ## [4] circlize_0.4.13 ComplexHeatmap_2.8.0 ## ## loaded via a namespace (and not attached): ## [1] shape_1.4.6 GetoptLong_1.0.5 tidyselect_1.1.1 ## [4] xfun_0.25 purrr_0.3.4 colorspace_2.0-2 ## [7] vctrs_0.3.8 generics_0.1.0 viridisLite_0.4.0 ## [10] stats4_4.1.0 utf8_1.2.2 rlang_0.4.11 ## [13] pillar_1.6.2 glue_1.4.2 DBI_1.1.1 ## [16] BiocGenerics_0.38.0 RColorBrewer_1.1-2 registry_0.5-1 ## [19] matrixStats_0.60.0 foreach_1.5.1 lifecycle_1.0.0 ## [22] stringr_1.4.0 munsell_0.5.0 gtable_0.3.0 ## [25] GlobalOptions_0.1.2 codetools_0.2-18 evaluate_0.14 ## [28] knitr_1.33 IRanges_2.26.0 Cairo_1.5-12.2 ## [31] doParallel_1.0.16 parallel_4.1.0 fansi_0.5.0 ## [34] highr_0.9 Rcpp_1.0.7 scales_1.1.1 ## [37] S4Vectors_0.30.0 magick_2.7.3 gridExtra_2.3 ## [40] rjson_0.2.20 ggplot2_3.3.5 png_0.1-7 ## [43] digest_0.6.27 gclus_1.3.2 stringi_1.7.3 ## [46] dplyr_1.0.7 clue_0.3-59 tools_4.1.0 ## [49] magrittr_2.0.1 tibble_3.1.3 cluster_2.1.2 ## [52] crayon_1.4.1 pkgconfig_2.0.3 ellipsis_0.3.2 ## [55] viridis_0.6.1 assertthat_0.2.1 iterators_1.0.13 ## [58] TSP_1.1-10 R6_2.5.1 compiler_4.1.0

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