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雷鋒網(wǎng)按：作為目前最常見的一種可視化手段，熱圖因其豐富的色彩變化和生動飽滿的信息表達(dá)被廣泛應(yīng)用于各種大數(shù)據(jù)分析場景。同時，專用于大數(shù)據(jù)統(tǒng)計分析、繪圖和可視化等場景的 R 語言，在可視化方面也提供了一系列功能強(qiáng)大、覆蓋全面的函數(shù)庫和工具包。因此，對相關(guān)從業(yè)者而言，用 R 語言繪制熱圖就成了一項最通用的必備技能。本文將以 R 語言為基礎(chǔ)，詳細(xì)介紹熱圖繪制中遇到的各種問題和注意事項。原文作者 taoyan，原載于作者個人博客，雷鋒網(wǎng)已獲授權(quán)。
本文將繪制靜態(tài)與交互式熱圖，需要使用到以下R包和函數(shù)：
● heatmap():用于繪制簡單熱圖的函數(shù)
● heatmap.2():繪制增強(qiáng)熱圖的函數(shù)
● d3heatmap:用于繪制交互式熱圖的R包
● ComplexHeatmap:用于繪制、注釋和排列復(fù)雜熱圖的R&bioconductor包（非常適用于基因組數(shù)據(jù)分析）
使用R內(nèi)置數(shù)據(jù)集 mtcars
df <- as.matrix((scale(mtcars))) #歸一化、矩陣化
主要是函數(shù) heatmap(x, scale="row")
● x: 數(shù)據(jù)矩陣
● scale：表示不同方向，可選值有：row, columa, none
● Default plotheatmap(df, scale = "none")
Use custom colorscol <- colorRampPalette(c("red", "white", "blue"))(256)heatmap(df, scale = "none", col=col)
#Use RColorBrewer color palette names
library(RColorBrewer)col <- colorRampPalette(brewer.pal(10, "RdYlBu"))(256)#自設(shè)置調(diào)色板dim(df)#查看行列數(shù)
## [1] 32 11
heatmap(df, scale = "none", col=col, RowSideColors = rep(c("blue", "pink"), each=16),
ColSideColors = c(rep("purple", 5), rep("orange", 6)))
#參數(shù)RowSideColors和ColSideColors用于分別注釋行和列顏色等,可help(heatmap)詳情
函數(shù) heatmap.2()
在熱圖繪制方面提供許多擴(kuò)展，此函數(shù)包裝在 gplots 包里。
library(gplots)heatmap.2(df, scale = "none", col=bluered(100),
trace = "none", density.info = "none")#還有其他參數(shù)可參考help(heatmap.2())
d3heatmap 包可用于生成交互式熱圖繪制,可通過以下代碼生成：
if (!require("devtools"))
install.packages("devtools")
devtools::install_github("rstudio/d3heatmap")
函數(shù) d3heatmap() 用于創(chuàng)建交互式熱圖，有以下功能：
● 將鼠標(biāo)放在感興趣熱圖單元格上以查看行列名稱及相應(yīng)值
● 可選擇區(qū)域進(jìn)行縮放
library(d3heatmap)d3heatmap(df, colors = "RdBu", k_row = 4, k_col = 2)
k_row、k_col分別指定用于對行列中樹形圖分支進(jìn)行著色所需組數(shù)。進(jìn)一步信息可help(d3heatmap())獲取。
軟件包 dendextend 可以用于增強(qiáng)其他軟件包的功能
library(dendextend)# order for rows
Rowv <- mtcars %>% scale %>% dist %>%
hclust %>% as.dendrogram %>%
set("branches_k_color", k = 3) %>%
set("branches_lwd", 1.2) %>% ladderize# Order for columns#
We must transpose the data
Colv <- mtcars %>% scale %>% t %>% dist %>%
hclust %>% as.dendrogram %>%
set("branches_k_color", k = 2, value = c("orange", "blue")) %>% set("branches_lwd", 1.2) %>% ladderize
#增強(qiáng)heatmap()函數(shù)
heatmap(df, Rowv = Rowv, Colv = Colv, scale = "none")
#增強(qiáng)heatmap.2()函數(shù)
heatmap.2(df, scale = "none", col = bluered(100), Rowv = Rowv, Colv = Colv, trace = "none", density.info = "none")
#增強(qiáng)交互式繪圖函數(shù)
d2heatmap()d3heatmap(scale(mtcars), colors = "RdBu", Rowv = Rowv, Colv = Colv)
ComplexHeatmap 包是 bioconductor 包，用于繪制復(fù)雜熱圖，它提供了一個靈活的解決方案來安排和注釋多個熱圖。它還允許可視化來自不同來源的不同數(shù)據(jù)之間的關(guān)聯(lián)熱圖。可通過以下代碼安裝：
if (!require("devtools")) install.packages("devtools")
devtools::install_github("jokergoo/ComplexHeatmap")
ComplexHeatmap 包的主要功能函數(shù)是 Heatmap()，格式為：Heatmap(matrix, col, name)
● matrix：矩陣
● col：顏色向量（離散色彩映射）或顏色映射函數(shù)（如果矩陣是連續(xù)數(shù)）
● name：熱圖名稱
library(ComplexHeatmap)
Heatmap(df, name = "mtcars")
#自設(shè)置顏色
library(circlize)
Heatmap(df, name = "mtcars", col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")))
Heatmap(df, name = "mtcars",col = colorRamp2(c(-2, 0, 2), brewer.pal(n=3, name="RdBu")))
#自定義顏色
mycol <- colorRamp2(c(-2, 0, 2), c("blue", "white", "red"))
Heatmap(df, name = "mtcars", col = mycol, column_title = "Column title", row_title =
"Row title")
注意，行標(biāo)題的默認(rèn)位置是“l(fā)eft”，列標(biāo)題的默認(rèn)是“top”?？梢允褂靡韵逻x項更改：
● row_title_side：允許的值為“左”或“右”（例如：row_title_side =“right”）
● column_title_side：允許的值為“top”或“bottom”（例如：column_title_side =“bottom”） 也可以使用以下選項修改字體和大?。?/p>
● row_title_gp：用于繪制行文本的圖形參數(shù)
● column_title_gp：用于繪制列文本的圖形參數(shù)
Heatmap(df, name = "mtcars", col = mycol, column_title = "Column title",
column_title_gp = gpar(fontsize = 14, fontface = "bold"),
row_title = "Row title", row_title_gp = gpar(fontsize = 14, fontface = "bold"))
在上面的R代碼中，fontface的可能值可以是整數(shù)或字符串：1 = plain，2 = bold，3 =斜體，4 =粗體斜體。如果是字符串，則有效值為：“plain”，“bold”，“italic”，“oblique”和“bold.italic”。
顯示行/列名稱：
● show_row_names：是否顯示行名稱。默認(rèn)值為TRUE
● show_column_names：是否顯示列名稱。默認(rèn)值為TRUE
Heatmap(df, name = "mtcars", show_row_names = FALSE)
默認(rèn)情況下，行和列是包含在聚類里的。可以使用參數(shù)修改：
● cluster_rows = FALSE。如果為TRUE，則在行上創(chuàng)建集群
● cluster_columns = FALSE。如果為TRUE，則將列置于簇上
# Inactivate cluster on rows
Heatmap(df, name = "mtcars", col = mycol, cluster_rows = FALSE)
如果要更改列集群的高度或?qū)挾?，可以使用選項column_dend_height 和 row_dend_width：
Heatmap(df, name = "mtcars", col = mycol, column_dend_height = unit(2, "cm"),
row_dend_width = unit(2, "cm") )
我們還可以利用 color_branches() 自定義樹狀圖外觀
library(dendextend)
row_dend = hclust(dist(df)) # row clustering
col_dend = hclust(dist(t(df))) # column clustering
Heatmap(df, name = "mtcars", col = mycol, cluster_rows =
color_branches(row_dend, k = 4), cluster_columns = color_branches(col_dend, k = 2))
參數(shù) clustering_distance_rows 和 clustering_distance_columns
用于分別指定行和列聚類的度量標(biāo)準(zhǔn)，允許的值有“euclidean”, “maximum”, “manhattan”, “canberra”, “binary”, “minkowski”, “pearson”, “spearman”, “kendall”。
Heatmap(df, name = "mtcars", clustering_distance_rows = "pearson",
clustering_distance_columns = "pearson")
#也可以自定義距離計算方式
Heatmap(df, name = "mtcars", clustering_distance_rows = function(m) dist(m))
Heatmap(df, name = "mtcars", clustering_distance_rows = function(x, y) 1 - cor(x, y))
請注意，在上面的R代碼中，通常為指定行聚類的度量的參數(shù) clustering_distance_rows顯示示例。建議對參數(shù)clustering_distance_columns（列聚類的度量標(biāo)準(zhǔn)）使用相同的度量標(biāo)準(zhǔn)。
# Clustering metric function
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
Heatmap(df, name = "mtcars", clustering_distance_rows = robust_dist,
clustering_distance_columns = robust_dist,
col = colorRamp2(c(-2, 0, 2), c("purple", "white", "orange")))
參數(shù)clustering_method_rows和clustering_method_columns可用于指定進(jìn)行層次聚類的方法。允許的值是hclust()函數(shù)支持的值，包括“ward.D”，“ward.D2”，“single”，“complete”，“average”。
Heatmap(df, name = "mtcars", clustering_method_rows = "ward.D",
clustering_method_columns = "ward.D")
有很多方法來拆分熱圖。一個解決方案是應(yīng)用k-means使用參數(shù)km。
在執(zhí)行k-means時使用set.seed()函數(shù)很重要，這樣可以在稍后精確地再現(xiàn)結(jié)果
set.seed(1122)
# split into 2 groupsHeatmap(df, name = "mtcars", col = mycol, k = 2)
# split by a vector specifying row classes， 有點(diǎn)類似于ggplot2里的分面
Heatmap(df, name = "mtcars", col = mycol, split = mtcars$cyl )
#split也可以是一個數(shù)據(jù)框，其中不同級別的組合拆分熱圖的行。
# Split by combining multiple variables
Heatmap(df, name ="mtcars", col = mycol, split = data.frame(cyl = mtcars$cyl, am = mtcars$am))
# Combine km and split
Heatmap(df, name ="mtcars", col = mycol, km = 2, split = mtcars$cyl)
#也可以自定義分割
library("cluster")
set.seed(1122)
pa = pam(df, k = 3)Heatmap(df, name = "mtcars", col = mycol, split = paste0("pam",
pa$clustering))
還可以將用戶定義的樹形圖和分割相結(jié)合。在這種情況下，split可以指定為單個數(shù)字：
row_dend = hclust(dist(df)) # row clusterin
grow_dend = color_branches(row_dend, k = 4)
Heatmap(df, name = "mtcars", col = mycol, cluster_rows = row_dend, split = 2)
利用HeatmapAnnotation()對行或列注釋。格式為： HeatmapAnnotation(df, name, col, show_legend)
● df：帶有列名的data.frame
● name：熱圖標(biāo)注的名稱
● col：映射到df中列的顏色列表
# Transposedf <- t(df)
# Heatmap of the transposed data
Heatmap(df, name ="mtcars", col = mycol)
# Annotation data frame
annot_df <- data.frame(cyl = mtcars$cyl, am = mtcars$am, mpg = mtcars$mpg)
# Define colors for each levels of qualitative variables
# Define gradient color for continuous variable (mpg)
col = list(cyl = c("4" = "green", "6" = "gray", "8" = "darkred"), am = c("0" = "yellow",
"1" = "orange"), mpg = colorRamp2(c(17, 25), c("lightblue", "purple")) )
# Create the heatmap annotation
ha <- HeatmapAnnotation(annot_df, col = col)
# Combine the heatmap and the annotation
Heatmap(df, name = "mtcars", col = mycol, top_annotation = ha)
#可以使用參數(shù)show_legend = FALSE來隱藏注釋圖例
ha <- HeatmapAnnotation(annot_df, col = col, show_legend = FALSE)
Heatmap(df, name = "mtcars", col = mycol, top_annotation = ha)
#注釋名稱可以使用下面的R代碼添加
library("GetoptLong")
# Combine Heatmap and annotation
ha <- HeatmapAnnotation(annot_df, col = col, show_legend = FALSE)
Heatmap(df, name = "mtcars", col = mycol, top_annotation = ha)
# Add annotation names on the right
for(an in colnames(annot_df)) {
seekViewport(qq("annotation_@{an}"))
grid.text(an, unit(1, "npc") + unit(2, "mm"), 0.5, default.units = "npc", just = "left")}
#要在左側(cè)添加注釋名稱，請使用以下代碼
# Annotation names on the left
for(an in colnames(annot_df)) { seekViewport(qq("annotation_@{an}")) grid.text(an,
unit(1, "npc") - unit(2, "mm"), 0.5, default.units = "npc", just = "left")}
將熱圖與一些基本圖形結(jié)合起來進(jìn)行注釋，利用anno_point()，anno_barplot()，anno_boxplot()，anno_density() 和 anno_histogram()。
# Define some graphics to display the distribution of columns
.hist = anno_histogram(df, gp = gpar(fill = "lightblue"))
.density = anno_density(df, type = "line", gp = gpar(col = "blue"))
ha_mix_top = HeatmapAnnotation(hist = .hist, density = .density)
# Define some graphics to display the distribution of rows
.violin = anno_density(df, type = "violin", gp = gpar(fill = "lightblue"), which = "row")
.boxplot = anno_boxplot(df, which = "row")
ha_mix_right = HeatmapAnnotation(violin = .violin, bxplt = .boxplot, which = "row",
width = unit(4, "cm"))
# Combine annotation with heatmap
Heatmap(df, name = "mtcars", col = mycol, column_names_gp = gpar(fontsize = 8),
top_annotation = ha_mix_top, top_annotation_height = unit(4, "cm")) + ha_mix_right
# Heatmap 1
ht1 = Heatmap(df, name = "ht1", col = mycol, km = 2, column_names_gp = gpar(fontsize = 9))
# Heatmap 2
ht2 = Heatmap(df, name = "ht2", col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")), column_names_gp = gpar(fontsize = 9))
# Combine the two heatmaps
ht1 + ht2
可以使用選項width = unit（3，“cm”））來控制熱圖大小。注意，當(dāng)組合多個熱圖時，第一個熱圖被視為主熱圖。剩余熱圖的一些設(shè)置根據(jù)主熱圖的設(shè)置自動調(diào)整。這些設(shè)置包括：刪除行集群和標(biāo)題，以及添加拆分等。
draw(ht1 + ht2,
# Titles
row_title = "Two heatmaps, row title",
row_title_gp = gpar(col = "red"),
column_title = "Two heatmaps, column title",
column_title_side = "bottom",
# Gap between heatmaps
gap = unit(0.5, "cm"))
可以使用參數(shù)show_heatmap_legend = FALSE，show_annotation_legend = FALSE刪除圖例。
在基因表達(dá)數(shù)據(jù)中，行代表基因，列是樣品值。關(guān)于基因的更多信息可以在表達(dá)熱圖之后附加，例如基因長度和基因類型。
expr = readRDS(paste0(system.file(package = "ComplexHeatmap"), "/extdata/gene_expression.rds"))
mat = as.matrix(expr[, grep("cell", colnames(expr))])
type = gsub("s\\d+_", "", colnames(mat))
ha = HeatmapAnnotation(df = data.frame(type = type))
Heatmap(mat, name = "expression", km = 5, top_annotation = ha, top_annotation_height = unit(4, "mm"),
show_row_names = FALSE, show_column_names = FALSE) +
Heatmap(expr$length, name = "length", width = unit(5, "mm"), col = colorRamp2(c(0, 100000), c("white", "orange"))) +
Heatmap(expr$type, name = "type", width = unit(5, "mm")) +
Heatmap(expr$chr, name = "chr", width = unit(5, "mm"), col = rand_color(length(unique(expr$chr))))
也可以可視化基因組變化和整合不同的分子水平（基因表達(dá)，DNA甲基化，…）
使用函數(shù)densityHeatmap()。
densityHeatmap(df)
8 Infos
sessionInfo()
## R version 3.3.3 (2017-03-06)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 8.1 x64 (build 9600)##
## 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] GetoptLong_0.1.6 cluster_2.0.5 circlize_0.3.10
## [4] ComplexHeatmap_1.12.0 dendextend_1.4.0 d3heatmap_0.6.1.1
##[7] gplots_3.0.1 RColorBrewer_1.1-2
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.9 DEoptimR_1.0-8 plyr_1.8.4
## [4] viridis_0.3.4 class_7.3-14 prabclus_2.2-6
## [7] bitops_1.0-6 base64enc_0.1-3 tools_3.3.3
## [10] digest_0.6.12 mclust_5.2.2 jsonlite_1.3
## [13] evaluate_0.10 tibble_1.2 gtable_0.2.0
## [16] lattice_0.20-34 png_0.1-7 yaml_2.1.14
## [19] mvtnorm_1.0-6 gridExtra_2.2.1 trimcluster_0.1-2
## [22] stringr_1.2.0 knitr_1.15.1 GlobalOptions_0.0.11
## [25] htmlwidgets_0.8 gtools_3.5.0 caTools_1.17.1
## [28] fpc_2.1-10 diptest_0.75-7 nnet_7.3-12
## [31] stats4_3.3.3 rprojroot_1.2 robustbase_0.92-7
## [34] flexmix_2.3-13 rmarkdown_1.3.9002 gdata_2.17.0
## [37] kernlab_0.9-25 ggplot2_2.2.1 magrittr_1.5
## [40] whisker_0.3-2 backports_1.0.5 scales_0.4.1
## [43] htmltools_0.3.5 modeltools_0.2-21 MASS_7.3-45
## [46] assertthat_0.1 shape_1.4.2 colorspace_1.3-2
## [49] KernSmooth_2.23-15 stringi_1.1.2 lazyeval_0.2.0
## [52] munsell_0.4.3 rjson_0.2.15
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