画出美哭了的热图
2018-06-21 本文已影响216人
PriscillaBai
声明：本帖中代码为小丫画图出品，我只是辛勤的搬运工，侵删。如想学习更多生信知识，请关注下方二维码，打赏后进群，群内干货多多。生信路上自学愉快～（为啥二维码这么大）
微信号：epigenomics
热图必备三要素：配色美美哒，边栏有scale，第三个我编不出来了……
场景一：基因表达量的热图
1 读取数据，加载包
df<-read.table("mRNA.txt",row.names = 1,header = T,as.is = T)
head(df)
library(pheatmap)
2 设置分组，用来画scale
annotation_col = data.frame(
Gender = factor(rep(c("F","M"),32)),#按性别分组
type = factor(rep(c("DG","PFC","PCC","CA1","CB","OC","TC","PC"),c(8,8,8,8,8,8,8,8)))) #按样本类别分组
rownames(annotation_col) = colnames(df)
3 设置scale的颜色
ann_colors = list(
Gender = c(F = "#FFA42D", M = "#A9D9DF"),#给性别分组设置颜色
type = c(DG = "blue", PFC = "green", PCC = "red", CA1 = "black",
CB = "pink", OC = "grey", TC = "yellow", PC = "purple")
4 画热图
pheatmap(df[1:1000,], #横行是画基因的相关性
cellwidth = 8, cellheight = 1, fontsize = 8, #调整格子和字体的大小
method="spearman", #计算gene或sample之间的相关性的方法，可选"pearson" (default), "kendall", or "spearman"
scale="row", #为基因做scale
cluster_rows=T,#为基因做聚类
cluster_cols=T,#为sample做聚类
color = colorRampPalette(c("navy", "white", "firebrick3"))(20), #调颜色
show_colnames=F,show_rownames =F,#图太大了的话横轴和纵轴名就都不显示
annotation_col = annotation_col, #scale的名字
annotation_colors = ann_colors, #scale的颜色
#treeheight_row = "0",treeheight_col = "0",#不画树
border_color = "NA")
image.png
场景二：样本相关性的热图
1. 计算样本间的相关性
cormat<-round(cor(df,method = "spearman"),2)
2. 画热图
library(pheatmap)
pheatmap(cormat,cellwidth = 8, cellheight = 8,fontsize = 8,
color = colorRampPalette(c("#3C7DAF", "#EAF4F1","#FFFCBA", "#E83140"))(20),
show_colnames=T,show_rownames =T,#显示sample的名字
#border_color = "NA",#默认有边框，不要边框的话就加这行
treeheight_row = "0",treeheight_col = "0")#不画树
image.png
3. 显示数字的热图
pheatmap(cormat[4:24,4:24],cellwidth = 15, cellheight = 15,fontsize = 8,
color = colorRampPalette(c("#3C7DAF", "#EAF4F1","#FFFCBA", "#E83140"))(20),
show_colnames=T,show_rownames =T,
display_numbers = TRUE,#显示数字
treeheight_row = "0",treeheight_col = "0")#不画树
image.png
4. 显示scale 改改配色
#按照sample的顺序，告诉R，它是属于哪个组的
annotation_col = data.frame(
type = factor(rep(c("DG","PFC","PCC","CA1","CB","OC","TC","PC"),c(8,8,8,8,8,8,8,8))))
rownames(annotation_col) = colnames(df)
annotation_row = data.frame(
type = factor(rep(c("DG","PFC","PCC","CA1","CB","OC","TC","PC"),c(8,8,8,8,8,8,8,8))))
rownames(annotation_row) = colnames(df)
#然后给每个组设置颜色
ann_colors = list(
type = c(DG = "blue", PFC = "green", PCC = "red", CA1 = "black",
CB = "pink", OC = "grey", TC = "yellow", PC = "purple")
)
pheatmap(cormat,cellwidth = 8, cellheight = 8,
fontsize = 8,
#display_numbers = TRUE,
color = colorRampPalette(c("navy", "white", "firebrick3"))(20),
show_colnames=F,show_rownames =F,#不显示sample的名字
annotation_col = annotation_col, annotation_row = annotation_row,
annotation_colors = ann_colors,
treeheight_row = "0",treeheight_col = "0",#不画树
border_color = "NA")#不显示边框
image.png
