今天小编向大家介绍一下使用gapmap和dendsort包生成带间隙的热图绘制方法及效果。
install.packages("gapmap") #安装包library(gapmap)library(RColorBrewer) #R语言配色包RdBu = rev(brewer.pal(11, name="RdBu"))RdYlBu = rev(brewer.pal(11, name="RdYlBu"))data("sample_tcga") #加载数据矩阵
dataTable <- t(sample_tcga) #矩阵转置row_dist <- as.dist(1-cor(t(dataTable), method = "pearson"))col_dist <- as.dist(1-cor(dataTable, method = "pearson"))#计算相关性,并且将矩阵转化为聚类分析用的距离col_hc <- hclust(col_dist, method = "complete")row_hc <- hclust(row_dist, method = "complete")#层次聚类col_d <- as.dendrogram(col_hc)row_d <- as.dendrogram(row_hc)#生成系统树图
一、gapmap
1.绘制没有间隙的聚类图
gapmap(m = as.matrix(dataTable),d_row = rev(row_d), d_col = col_d,#rev将向量或矩阵进行翻转#d_row是行的系统树数据，d_col是列的系统树数据ratio =0 , verbose=FALSE, col=RdBu,#ratio是图形中空白间隙所占宽度百分比。#verbose是否详细显示进行的步骤label_size=2,v_ratio= c(0.8,0.8,0.8), h_ratio=c(0.1,0.8,0.1)#设置图形中垂直各组分所占图形的比例，它参数分别为顶部竖线，中心聚类图，底部标签。#设置图形中水平各组分所占图形的比例，它参数分别为左侧横线，中心聚类图，右侧标签。)
2.有间隙的聚类图
gapmap(m = as.matrix(dataTable),d_row = rev(row_d), d_col = col_d,mode = "quantitative",#mode是间隙模式，"阈值" 或 "定量"（"threshold" 或 "quantitative"）形式mapping="exponential",#在quantitative间隙模式的情况下，调控间隙的形式，“线性”或“指数” ("linear" or "exponential" )col=RdBu,ratio = 0.3, verbose=FALSE,scale = 0.5, label_size=2,#scale 指mapping="exponential"（指数间隙模式）下，间隙映射的比例#label_size 标签文字大小v_ratio= c(0.1,0.8,0.1), h_ratio=c(0.1,0.8,0.1))
3.更改颜色绘制热图
gapmap(m = as.matrix(dataTable),d_row = rev(row_d), d_col = col_d,mode = "quantitative",mapping="exponential",col=RdYlBu, #更换颜色ratio = 0.3, verbose=FALSE,scale = 0.5, label_size=2,v_ratio= c(0.1,0.8,0.1), h_ratio=c(0.1,0.8,0.1))
二、 dendsort
dendsort包与gapmap结合，可以重新排列树状图的结构。
dendsort对树状图对象进行排序，通常用于层次聚类后。根据每个合并点处子树的平均距离，对生成的树状图中的子树进行排序。较紧密的群（平均距离较小的群集）位于分支的左侧。当叶子与簇合并时，叶子将放置在右侧。
install.packages("dendsort")library(dendsort)
1.绘制排序后的热图
gapmap(m = as.matrix(dataTable),d_row = rev(dendsort(row_d, type = "average")),d_col = dendsort(col_d, type = "average"),#dendsort对树状图对象进行排序mode = "quantitative", mapping="exponential",ratio = 0.3, verbose=FALSE, scale = 0.5, v_ratio= c(0.1,0.8,0.1),h_ratio=c(0.1,0.8,0.1), label_size=2, show_legend=TRUE, col=RdBu)
2.绘制空白树状图(gapped dendrogram)。
gap_dendrogram 是ggplot2绘制空白树状图的方法，输入数据类型为gapdata class，由gap_data()生成
row_data <- gap_data(d= dendsort(row_d, type = "average"),mode = "quantitative", mapping="exponential",ratio=0.3, scale= 0.5) #生成数据dend <- gap_dendrogram(data = row_data,leaf_labels = TRUE, #是否加标签rotate_label = TRUE) #是否旋转标签dend + theme(axis.ticks.length= grid::unit(0,"lines") )+theme(axis.ticks.margin = grid::unit(-0.8, "lines"))#将axis.tick.margin设置为负值,使文本标签更接近树状图
小编总结：
R语言中绘制聚类热图的方法有很多，比如pheatmap、heatmap还有我们今天介绍的gapmap等，小伙伴们可以比较优势，选择适合自己作图的R包哦~
