當(dāng)需要展示多個數(shù)據(jù)集之間的關(guān)聯(lián)并揭示可能存在的潛在模式時蹂季，ComplexHeatmap包就有了用武之地。下面提供一個實例。

1. 文件準(zhǔn)備。

需要4個文件融蹂。包括兩個heatmap數(shù)據(jù)文件mRNA.txt和circRNA.txt，兩個anotation文件anotation_con.txt和anotation_dis.txt廓潜。其中mRNA.txt和circRNA.txt里是基因的表達譜數(shù)值阱佛，anotation_con.txt里是連續(xù)型變量屬性值，anotation_dis.txt里是離散型變量屬性值耍缴。4個文件的第一列需要保持一致砾肺。

a. mRNA.txt

mRNA.txt

b. circRNA.txt

circRNA.txt

c. anotation_con.txt

anotation_con.txt

d. anotation_dis.txt

anotation_dis.txt

2. 代碼。

a. 加載包防嗡，讀取表達譜文件变汪，構(gòu)建聚類樹。

library(ComplexHeatmap)
library(circlize)

mydata1 <- read.table("mRNA.txt", header =T, row.names=1, sep = "\t", quote = "\"'", check.names=F)
groupn1 <- sub("\\d+", "", colnames(mydata1), perl=TRUE)
mydata1_colnames <- colnames(mydata1)
mydata1 <- t(apply(mydata1, 1, scale))              # 進行標(biāo)準(zhǔn)化
colnames(mydata1) <- mydata1_colnames

mydata2 <- read.table("circRNA.txt", header =T, row.names=1, sep = "\t", quote = "\"'", check.names=F)
groupn2 <- sub("\\d+", "", colnames(mydata2), perl=TRUE)
mydata2_colnames <- colnames(mydata2)
mydata2 <- t(apply(mydata2, 1, scale))
colnames(mydata2) <- mydata2_colnames

column_tree = hclust(dist(t(mydata1)))        # 對mydata1聚類
colset <- colorRamp2(seq(min(mydata1, mydata2),max(mydata1, mydata2),length=3), c("blue", "#EEEEEE", "red"))        # 設(shè)置顏色

b. 配置heatmap1蚁趁。行列分別聚類裙盾。

annot1 <- data.frame(Group1=groupn1)
col_group1=list(Group1=structure(rainbow(length(unique(groupn1))), names=unique(groupn1)))
ha1 = HeatmapAnnotation(df=annot1, col=col_group1, show_legend = F, show_annotation_name=F)
H1 = Heatmap(mydata1, clustering_distance_rows = "euclidean", clustering_method_rows = "average", clustering_distance_columns = "euclidean", clustering_method_columns = "average", cluster_columns = column_tree, column_dend_reorder = F, show_row_names = T, show_column_dend=T, column_title = "mRNA", name = "mRNA", col = colset, top_annotation = ha1, row_title_gp = gpar(fontsize = 8))

c. 配置heatmap2。按照heatmap1的聚類模式進行聚類他嫡。

annot2 <- data.frame(Group2=groupn2)
col_group2=list(Group2=structure(rainbow(length(unique(groupn2))), names=unique(groupn2[column_tree$order])))
ha2 = HeatmapAnnotation(df=annot2, col=col_group2, show_legend = F, show_annotation_name=F)
H2 = Heatmap(mydata2[, column_tree$order], name = "circRNA", col = colset, cluster_columns = F, top_annotation = ha2, show_row_names = T, column_title = "circRNA", show_heatmap_legend=T)

d. 配置連續(xù)型anotation的heatmap番官。

andata_con <- read.table("anotation_con.txt", header =T, row.names=1, sep = "\t", quote = "\"'", check.names=F)
Han_con1 = Heatmap(andata_con[,1], name = colnames(andata_con)[1], col = colorRamp2(seq(min(andata_con[,1]),max(andata_con[,1]),length=2), c("white", "red")))
Han_con2 = Heatmap(andata_con[,2], name = colnames(andata_con)[2], col = colorRamp2(seq(min(andata_con[,2]),max(andata_con[,2]),length=2), c("white", "red")))

e. 配置離散型anotation的heatmap。

andata_dis <- read.table("anotation_dis.txt", header =T, row.names=1, sep = "\t", quote = "\"'", check.names=F)
Han_dis1 = Heatmap(andata_dis[,1], name = colnames(andata_dis)[1])
Han_dis2 = Heatmap(andata_dis[,2], name = colnames(andata_dis)[2])

f. 配置并輸出圖片钢属。

ht_opt(legend_title_gp = gpar(fontsize = 12, fontface = "bold"), legend_labels_gp = gpar(fontsize = 10), heatmap_column_names_gp = gpar(fontsize = 8))
draw(H1 + H2 + Han_con1 + Han_con2 + Han_dis1 + Han_dis2, newpage = F, auto_adjust = FALSE, column_title = "Comprehensive correspondence between mRNA and CircRNA", column_title_gp = gpar(fontsize = 15, fontface = "bold"), heatmap_legend_side = "right")

3. 最終的圖片徘熔。

ComplexHeatmap.jpg