摘要： 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被廣泛應用於各種大數據分析場景。同時，專用於大數據統計分析、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大、覆蓋全面的函數庫和工具包。因此，對相關從業者而言，用R 語言繪製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，詳細介紹熱圖繪製中遇到的各種問題和注意事項。
作者：taoyan
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被廣泛應用於各種大數據分析場景。同時，專用於大數據統計分析、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大、覆蓋全面的函數庫和工具包。因此，對相關從業者而言，用R 語言繪製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，詳細介紹熱圖繪製中遇到的各種問題和注意事項。
簡介
本文將繪製靜態與交互式熱圖，需要使用到以下R包和函數：
- heatmap():用於繪製簡單熱圖的函數
- heatmap.2():繪製增強熱圖的函數
- d3heatmap:用於繪製交互式熱圖的R包
- ComplexHeatmap:用於繪製、註釋和排列複雜熱圖的R&bioconductor包（非常適用於基因組數據分析）
數據準備
使用R內置數據集mtcars
df <- as.matrix((scale(mtcars))) #歸一化、矩陣化
使用基本函數繪製簡單簡單熱圖
主要是函數heatmap(x, scale=”row”)
- x: 數據矩陣
- 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)#自設置調色板dim(df)#查看行列數
## [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RowSideColors和ColSideColors用於分別註釋行和列顏色等,可help(heatmap)詳情
增強熱圖
函數heatmap.2()
在熱圖繪製方面提供許多擴展，此函數包裝在gplots 包裡。
library(gplots)heatmap.2(df, scale = "none", col=bluered(100), trace = "none", density.info = "none")#還有其他參數可參考help(heatmap.2())
交互式熱圖繪製
d3heatmap 包可用於生成交互式熱圖繪製,可通過以下代碼生成：
if (!require("devtools"))
install.packages("devtools")
devtools::install_github("rstudio/d3heatmap")
函數d3heatmap() 用於創建交互式熱圖，有以下功能：
● 將鼠標放在感興趣熱圖單元格上以查看行列名稱及相應值
● 可選擇區域進行縮放
library(d3heatmap)d3heatmap(df, colors = "RdBu", k_row = 4, k_col = 2)
k_row、k_col分別指定用於對行列中樹形圖分支進行著色所需組數。進一步信息可help(d3heatmap())獲取。
使用dendextend 包增強熱圖
軟件包dendextend 可以用於增強其他軟件包的功能
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heatmap()函數
heatmap(df, Rowv = Rowv, Colv = Colv, scale = "none")
#增強heatmap.2()函數 heatmap.2(df, scale = "none", col = bluered(100), Rowv = Rowv, Colv = Colv, trace = "none", density.info = "none")
#增強交互式繪圖函數 d2heatmap()d3heatmap(scale(mtcars), colors = "RdBu", Rowv = Rowv, Colv = Colv)
繪製複雜熱圖
ComplexHeatmap 包是bioconductor 包，用於繪製複雜熱圖，它提供了一個靈活的解決方案來安排和註釋多個熱圖。它還允許可視化來自不同來源的不同數據之間的關聯熱圖。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（如果矩陣是連續數）
- name：熱圖名稱
library(ComplexHeatmap) Heatmap(df, name = "mtcars")
#自設置顏色
library(circlize)
Heatmap(df, name = "mtcars", col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")))
使用調色板
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"))
熱圖及行列標題設置
Heatmap(df, name = "mtcars", col = mycol, column_title = "Column title", row_title = "Row title")
注意，行標題的默認位置是“left”，列標題的默認是“top”。可以使用以下選項更改：
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小： row_title_gp：用於繪製行文本的圖形參數 column_title_gp：用於繪製列文本的圖形參數 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。默認值為TRUE
- show_column_names：是否顯示列名稱。默認值為TRUE
Heatmap(df, name = "mtcars", show_row_names = FALSE)
更改聚類外觀
默認情況下，行和列是包含在聚類裡的。可以使用參數修改：
cluster_rows = FALSE。如果為TRUE，則在行上創建集群 cluster_columns = FALSE。如果為TRUE，則將列置於簇上 # 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寬度，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))
不同的聚類距離計算方式
參數clustering_distance_rows 和clustering_distance_columns
用於分別指定行和列聚類的度量標準，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clustering_distance_rows顯示示例。建議對參數clustering_distance_columns（列聚類的度量標準）使用相同的度量標準。
# 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")))
聚類方法
參數clustering_method_rows和clustering_method_columns可用於指定進行層次聚類的方法。允許的值是hclust()函數支持的值，包括“ward.D”，“ward.D2”，“single”，“complete”，“average”。
Heatmap(df, name = "mtcars", clustering_method_rows = "ward.D", clustering_method_columns = "ward.D")
熱圖拆分
有很多方法來拆分熱圖。一個解決方案是應用k-means使用參數km。
在執行k-means時使用set.seed()函數很重要，這樣可以在稍後精確地再現結果
set.seed(1122) # split into 2 groupsHeatmap(df, name = "mtcars", col = mycol, k = 2)
# split by a vector specifying row classes， 有點類似於ggplot2裡的分面 Heatmap(df, name = "mtcars", col = mycol, split = mtcars$cyl )
#split也可以是一個數據框，其中不同級別的組合拆分熱圖的行。 # Split by combining multiple variables Heatmap(df, name ="mtcars", col = mycol, split = data.frame(cyl = mtcars$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$clustering))
還可以將用戶定義的樹形圖和分割相結合。在這種情況下，split可以指定為單個數字：
row_dend = hclust(dist(df)) # row clusterin grow_dend = color_branches(row_dend, k = 4) Heatmap(df, name = "mtcars", col = mycol, cluster_rows = row_dend, split = 2)
熱圖註釋
利用HeatmapAnnotation()對行或列註釋。格式為： HeatmapAnnotation(df, name, col, show_legend)
- df：帶有列名的data.frame
- name：熱圖標註的名稱
- col：映射到df中列的顏色列表
# Transposedf <- t(df) # Heatmap of the transposed data Heatmap(df, name ="mtcars", col = mycol)
# Annotation data frame
annot_df <- data.frame(cyl = mtcars$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show_legend = FALSE來隱藏註釋圖例 ha <- HeatmapAnnotation(annot_df, col = col, show_legend = FALSE) Heatmap(df, name = "mtcars", col = mycol, top_annotation = ha)
#註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")}
#要在左側添加註釋名稱，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")}
複雜註釋
將熱圖與一些基本圖形結合起來進行註釋，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
熱圖組合
# 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，當組合多個熱圖時，第一個熱圖被視為主熱圖。剩餘熱圖的一些設置根據主熱圖的設置自動調整。這些設置包括：刪除行集群和標題，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show_heatmap_legend = FALSE，show_annotation_legend = FALSE刪除圖例。
基因表達矩陣
在基因表達數據中，行代表基因，列是樣品值。關於基因的更多信息可以在表達熱圖之後附加，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，DNA甲基化，…）
可視化矩陣中列的分佈
使用函數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
End.
轉貼自： 36大數據
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