R绘图|pheatmap热图绘制——中阶篇
2020-06-19 本文已影响0人
Clariom
本次内容在pheatmap热图绘制——基础篇的基础上展示如何在热图上体现不同的功能分区，以及如何根据聚类结果将不同的cluster分开？
首先清除环境，安装并加载所需要的R包
rm(list = ls()) #清除环境内存
#install.packages("pheatmap") #安装pheatmap包
#install.packages("readxl") #安装readxl包
#install.packages("ggplot2") #安装ggplot2包
#install.packages(c('officer', 'rvg', 'flextable', 'stargazer', 'broom' ))#export的依赖包
#下载export，链接https://cran.r-project.org/src/contrib/Archive/export/export_0.2.2.tar.gz
#install.packages("D:/R-3.6.2/library/export_0.2.2.tar.gz", repos = NULL)
library(ggplot2)
library(pheatmap) #加载pheatmap包
library(readxl) #加载readxl包
library(export) #用于输出不同格式的结果
读入分组信息，用于行注释
rm(list = ls()) #清除环境内存
#install.packages("pheatmap") #安装pheatmap包
#install.packages("readxl") #安装readxl包
#install.packages("ggplot2") #安装ggplot2包
#install.packages(c('officer', 'rvg', 'flextable', 'stargazer', 'broom' ))#export的依赖包
#下载export，链接https://cran.r-project.org/src/contrib/Archive/export/export_0.2.2.tar.gz
#install.packages("D:/R-3.6.2/library/export_0.2.2.tar.gz", repos = NULL)
library(ggplot2)
library(pheatmap) #加载pheatmap包
library(readxl) #加载readxl包
library(export) #用于输出不同格式的结果
读入表达数据并对数据做简单处理
data<-read_excel("demo2.xlsx",1) #读入excel数据
data<-as.data.frame(data) # 将data转换为data.frame格式
rownames(data)<-data[,1] #将第一列数据设置为行名
data<-data[,-1] #去除重复的第一列
colnames(data) = gsub("_FPKM","",colnames(data))#去除列名中的_FPKM
head(data)
## negative_1 negative_3 negative_4 neutral_1 neutral_3 neutral_4
## NCL 2.059622 1.896343 1.092175 2.936300 3.181986 4.710491
## PLEKHG5 0.712203 0.871737 0.493767 1.699708 0.652557 2.117785
## EIF2S3 1.590397 1.457507 1.710653 3.299838 2.262812 4.715623
## SMAD7 0.183231 0.102905 0.159121 0.298067 0.365457 0.299725
## HDLBP 0.443738 0.439725 0.297456 0.967719 0.845097 0.744160
## PTPRM 0.456168 0.330697 0.342640 0.760930 0.746550 0.957069
构建样本信息，用于列注释
sp= data.frame(CellType = c(rep("negative", 3),rep("neutral", 3)),Time = colnames(data))
rownames(sp)<-sp[,2]
sp[,2]<-NULL
head(sp)
## CellType
## negative_1 negative
## negative_3 negative
## negative_4 negative
## neutral_1 neutral
## neutral_3 neutral
## neutral_4 neutral
绘图并输出结果
p=pheatmap(data,fontsize_col=12,
show_rownames=F,
cluster_cols = T,cluster_rows = T,scale="row",
treeheight_row=25,treeheight_col=25,
main="Heatmap",
color = colorRampPalette(c("blue", "white", "red"))(50),
annotation_row=bx,annotation_col = sp,annotation_legend = T)
image
ggsave(p,filename = "demo2.pdf",width=6,height=8)
ggsave(p,filename = "demo2.png",width=6,height=8)
#也可以加载export包输出结果
#graph2pdf(file="demo2.pdf",width=6,height=8)#导出pdf格式文件
#graph2tif(file="demo2.tif",width=6,height=8)#导出tiff格式文件
#graph2jpg(file="demo2.jpg",width=6,height=8)#导出jpg格式文件
添加gap，区分不同cluster
1. 可以用cutree或者gap参数去定义，cutree只有在聚类的基础上方可使用，取值可聚类树大致的分块。
p1=pheatmap(data,fontsize_col=12,
show_rownames=F,
cluster_cols = T,cluster_rows = T,scale="row",
annotation_row=bx,annotation_col = sp,annotation_legend = T,
treeheight_row=25,treeheight_col=25,
main="Heatmap",
color = colorRampPalette(c("blue", "white", "red"))(50),
cutree_row = 4,cutree_col = 2)
image
ggsave(p1,filename = "demo2_gap1.pdf",width=6,height=8)
ggsave(p1,filename = "demo2_gap1.png",width=6,height=8)
2. gap可自定义间隔的位置,适用于那种不聚类保证美观顺序的情况,肉眼可区分分段区域
table(bx)#查看各个功能的基因数量
## bx
## Cell cycle Adhesion Junction Migration Tube formation
## 40 40 126 49 3
## Vasculogenesis
## 9
p2=pheatmap(data,fontsize_col=12,
show_rownames=F,
cluster_cols = T,cluster_rows = F,scale="row",
annotation_row=bx,annotation_col = sp,annotation_legend = T,
treeheight_row=25,treeheight_col=25,
main="Heatmap",
color = colorRampPalette(c("blue", "white", "red"))(50),
gaps_row=c(40,40+40,80+126,80+126+49,80+126+49+3,80+126+49+3+9),cutree_cols = 2)
image
ggsave(p2,filename = "demo2_gap2.pdf",width=6,height=8)
ggsave(p2,filename = "demo2_gap2.png",width=6,height=8)
显示运行环境
sessionInfo()
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 18363)
##
## Matrix products: default
##
## 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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] export_0.2.2 readxl_1.3.1 pheatmap_1.0.12 ggplot2_3.2.1
##
## loaded via a namespace (and not attached):
## [1] rgl_0.100.50 Rcpp_1.0.3 lattice_0.20-38
## [4] tidyr_1.0.2 assertthat_0.2.1 digest_0.6.24
## [7] mime_0.9 R6_2.4.1 cellranger_1.1.0
## [10] backports_1.1.5 evaluate_0.14 pillar_1.4.3
## [13] gdtools_0.2.1 rlang_0.4.4 lazyeval_0.2.2
## [16] uuid_0.1-4 miniUI_0.1.1.1 data.table_1.12.8
## [19] flextable_0.5.9 rmarkdown_2.1 webshot_0.5.2
## [22] stringr_1.4.0 htmlwidgets_1.5.1 munsell_0.5.0
## [25] shiny_1.4.0 broom_0.5.5 compiler_3.6.2
## [28] httpuv_1.5.2 xfun_0.12 pkgconfig_2.0.3
## [31] systemfonts_0.1.1 base64enc_0.1-3 rvg_0.2.4
## [34] htmltools_0.4.0 tidyselect_1.0.0 tibble_2.1.3
## [37] crayon_1.3.4 dplyr_0.8.4 withr_2.1.2
## [40] later_1.0.0 grid_3.6.2 nlme_3.1-142
## [43] jsonlite_1.6.1 xtable_1.8-4 gtable_0.3.0
## [46] lifecycle_0.1.0 magrittr_1.5 scales_1.1.0
## [49] zip_2.0.4 stringi_1.4.6 farver_2.0.3
## [52] promises_1.1.0 xml2_1.2.2 vctrs_0.2.3
## [55] generics_0.0.2 openxlsx_4.1.4 stargazer_5.2.2
## [58] RColorBrewer_1.1-2 tools_3.6.2 manipulateWidget_0.10.1
## [61] glue_1.3.1 officer_0.3.8 purrr_0.3.3
## [64] crosstalk_1.0.0 fastmap_1.0.1 yaml_2.2.1
## [67] colorspace_1.4-1 knitr_1.28
往期回顾
R绘图|ggplot2散点图的绘制
R绘图|pheatmap热图绘制——基础篇
今天的内容就到这里~~，更多内容可关注公共号“YJY技能修炼”~~
