如何轻松绘制基因表达聚类趋势图

# 清除当前环境中的变量

rm(list=ls())

# 设置工作路径

setwd("C:/Users/Dell/Desktop/")

# 加载所需的R包

library(ggplot2 )

library(pheatmap )

library(reshape2 )

# 读取测试数据

data <- read.table("test.txt",header = T, row.names = 1,check.names = F)

# 查看数据基本信息

head(data)

## Stage1_R1 Stage1_R2 Stage2_R1 Stage2_R2 Stage3_R1

## Unigene0001 -1.1777172 -1.036102 0.8423829 1.3458754 0.1080678

## Unigene0002 1.0596877 1.490939 -0.7663244 -0.6255567 -0.5333080

## Unigene0003 0.9206594 1.575844 -0.7861697 -0.3860003 -0.5501094

## Unigene0004 -1.3553173 -1.145970 0.2097526 0.7059886 0.9516353

## Unigene0005 1.0134516 1.445897 -0.9705129 -0.8560422 -0.2556562

## Unigene0006 0.8675939 1.575735 -1.0120718 -0.5856459 -0.2821991

## Stage3_R2

## Unigene0001 -0.08250721

## Unigene0002 -0.62543728

## Unigene0003 -0.77422398

## Unigene0004 0.63391053

## Unigene0005 -0.37713783

## Unigene0006 -0.56341216

# 使用pheatmap绘制基因表达热图，并进行层次聚类分成不同的cluster

p <- pheatmap(data, show_rownames = F, cellwidth =40, cluster_cols = F,

cutree_rows = 6,gaps_col = c(2,4,6), angle_col = 45,fontsize = 12)

# 获取聚类后的基因顺序

row_cluster = cutree(p$tree_row,k=6)

# 对聚类后的数据进行重新排序

newOrder = data[p$tree_row$order,]

newOrder[,ncol(newOrder)+1]= row_cluster[match(rownames(newOrder),names(row_cluster))]

colnames(newOrder)[ncol(newOrder)]="Cluster"

# 查看重新排序后的数据

head(newOrder)

## Stage1_R1 Stage1_R2 Stage2_R1 Stage2_R2 Stage3_R1 Stage3_R2

## Unigene0604 0.8097531 1.403759 -0.2668053 0.17819117 -0.9811268 -1.143771

## Unigene0262 0.8453759 1.408372 -0.2802646 0.12312391 -0.9767547 -1.119853

## Unigene0069 0.8279061 1.428306 -0.3124647 0.12820543 -0.9524584 -1.119494

## Unigene0219 0.8536163 1.423168 -0.3082219 0.09583306 -0.9584284 -1.105967

## Unigene0116 0.8282198 1.491489 -0.4344344 0.05187827 -0.8641523 -1.073000

## Unigene0297 0.8008572 1.459959 -0.3661415 0.13242699 -0.9111229 -1.115978

## Cluster

## Unigene0604 6

## Unigene0262 6

## Unigene0069 6

## Unigene0219 6

## Unigene0116 6

## Unigene0297 6

# 查看聚类后cluster的基本信息

unique(newOrder$Cluster)

## [1] 6 2 5 3 4 1

table(newOrder$Cluster)

##

## 1 2 3 4 5 6

## 258 314 68 9 12 39

# 将新排序后的数据保存输出

newOrder$Cluster = paste0("cluster",newOrder$Cluster)

write.table(newOrder, "expr_DE.pheatmap.cluster.txt",sep="t",quote = F,row.names = T,col.names = T)

# 绘制每个cluster的基因聚类趋势图

newOrder$gene = rownames(newOrder)

head(newOrder)

## Stage1_R1 Stage1_R2 Stage2_R1 Stage2_R2 Stage3_R1 Stage3_R2

## Unigene0604 0.8097531 1.403759 -0.2668053 0.17819117 -0.9811268 -1.143771

## Unigene0262 0.8453759 1.408372 -0.2802646 0.12312391 -0.9767547 -1.119853

## Unigene0069 0.8279061 1.428306 -0.3124647 0.12820543 -0.9524584 -1.119494

## Unigene0219 0.8536163 1.423168 -0.3082219 0.09583306 -0.9584284 -1.105967

## Unigene0116 0.8282198 1.491489 -0.4344344 0.05187827 -0.8641523 -1.073000

## Unigene0297 0.8008572 1.459959 -0.3661415 0.13242699 -0.9111229 -1.115978

## Cluster gene

## Unigene0604 cluster6 Unigene0604

## Unigene0262 cluster6 Unigene0262

## Unigene0069 cluster6 Unigene0069

## Unigene0219 cluster6 Unigene0219

## Unigene0116 cluster6 Unigene0116

## Unigene0297 cluster6 Unigene0297

library(reshape2)

# 将短数据格式转换为长数据格式

data_new = melt(newOrder)

## Using Cluster, gene as id variables

head(data_new)

## Cluster gene variable value

## 1 cluster6 Unigene0604 Stage1_R1 0.8097531

## 2 cluster6 Unigene0262 Stage1_R1 0.8453759

## 3 cluster6 Unigene0069 Stage1_R1 0.8279061

## 4 cluster6 Unigene0219 Stage1_R1 0.8536163

## 5 cluster6 Unigene0116 Stage1_R1 0.8282198

## 6 cluster6 Unigene0297 Stage1_R1 0.8008572

# 绘制基因表达趋势折线图

ggplot(data_new ,aes(variable ,value,group=gene ))+geom_line(color ="gray90",size =0.8)+

geom_hline(yintercept =0,linetype =2)+

stat_summary(aes(group=1),fun .y =mean ,geom ="line",size =1.2,color ="#c51b7d")+

facet_wrap(Cluster ~.)+

theme_bw()+

theme(panel .grid .major =element_blank(),panel .grid .minor =element_blank(),

axis .text =element_text(size =8,face ="bold"),

strip .text =element_text(size =8,face ="bold"))

sessionInfo

## R version 3.6.0 (2019-04-26)

## Platform: x86_64-w64-mingw32/x64 (64-bit)

## Running under: Windows 10 x64 (build 17763)

##

## 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] reshape2_1.4.3 pheatmap_1.0.12 ggplot2_3.2.0

##

## loaded via a namespace (and not attached):

## [1] Rcpp_1.0.1 knitr_1.23 magrittr_1.5

## [4] tidyselect_0.2.5 munsell_0.5.0 colorspace_1.4-1

## [7] R6_2.4.0 rlang_0.4.0 plyr_1.8.4

## [10] stringr_1.4.0 dplyr_0.8.3 tools_3.6.0

## [13] grid_3.6.0 gtable_0.3.0 xfun_0.8

## [16] withr_2.1.2 htmltools_0.3.6 yaml_2.2.0

## [19] lazyeval_0.2.2 digest_0.6.20 assertthat_0.2.1

## [22] tibble_2.1.3 crayon_1.3.4 RColorBrewer_1.1-2

## [25] purrr_0.3.2 glue_1.3.1 evaluate_0.14

## [28] rmarkdown_1.13 labeling_0.3 stringi_1.4.3

## [31] compiler_3.6.0 pillar_1.4.2 scales_1.0.0

## [34] pkgconfig_2.0.2

最后编辑：2023-03-03

作者：萌小白

一个热爱网络的青年！

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