高度定制的go和kegg富集分析R语言绘图 | Circular barplot

生信技能树

发布于 2022-01-21 11:00:59

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发布于 2022-01-21 11:00:59

文章被收录于专栏：生信技能树

我前面的甲基化教程主要是针对450k这样的芯片，所以champ流程就绰绰有余，很多小伙伴在咱们公众号后台咨询甲基化测序数据分析，恰好最近实习生投稿:

下面是去年实习生的分享

前言

前阵子复现单细胞数据，发现个很有趣的富集图。通过compareCluster功能将不同细胞群的通路用Circular barplot展示出来，下面来复现下这张图：

来源：Stromal cell diversity associated with immune evasion in human triple-negative breast cancer，doi: 10.15252/embj.2019104063，IF=11.598，The EMBO Journal。数据https://github.com/sunnyzwu/stromal_subclasses

学习了单细胞的小伙伴也可以做下练手，一起交流下（还可以参加曾老师的独家单细胞分享会，只此一家，千万别错过哦）

腾讯会议
会议主题：单细胞学徒培养2022
会议时间：2022/01/02-2022/07/20 21:00-21:30(GMT+08:00) 中国标准时间 - 北京, 每天
(周三周四休息哦)
点击链接入会，或添加至会议列表：
https://meeting.tencent.com/dm/EWBbxqqpD6Fa

#腾讯会议：507-1242-3763
会议密码：2022

会议直播：
https://meeting.tencent.com/l/BDR6rygede4B

复现过程

载入数据

首先我前期已做好差异分析，分别求出四个细胞群cluster的差异基因
接下来分别把不同细胞群基因提取出来，构成list，再转换ID

###### step1:导入数据 ###### 
rm(list=ls())
options(stringsAsFactors = F)
library(dplyr)
setwd("3-cell")

sce.markers <- read.csv('celltype_deg_sce.markers.csv',row.names = 1)
table(sce.markers$cluster)
#dPVL  iCAFs  imPVL myCAFs 
#   120     22    129     46

dPVL=sce.markers[sce.markers$cluster=='dPVL',]$gene
imPVL=sce.markers[sce.markers$cluster=='imPVL',]$gene
iCAFs=sce.markers[sce.markers$cluster=='iCAFs',]$gene
myCAFs=sce.markers[sce.markers$cluster=='myCAFs',]$gene
total <- list(dPVL=dPVL,imPVL=imPVL,iCAFs=iCAFs,myCAFs=myCAFs)

# 转ID
library(org.Hs.eg.db)
i=1
for (i in 1:4) {
  #i=1
  ## 把SYMBOL改为ENTREZID
  total[[i]]=as.character(na.omit(AnnotationDbi::select(org.Hs.eg.db,
                                             keys = total[[i]],
                                             columns = 'ENTREZID',
                                             keytype = 'SYMBOL')[,2]))}
lapply(total, head)

> lapply(total, head)
$dPVL
[1] "58189" "10398" "6876"  "59"    "11034" "64129"

$imPVL
[1] "8490"   "397"    "2167"   "948"    "2701"   "221395"

$iCAFs
[1] "9452"  "347"   "10563" "6387"  "8406"  "8788" 

$myCAFs
[1] "115908" "6423"   "165"    "151887" "1278"   "10631"

多组富集分析compareCluster

可修改fun = enrichKEGG 为KEGG分析

Chapter 14 Biological theme comparison | Biomedical Knowledge Mining using GOSemSim and clusterProfiler (yulab-smu.top)

###### step2:富集分析 ###### 
library(clusterProfiler)
xx <- compareCluster(total, fun="enrichGO",
                     OrgDb = org.Hs.eg.db,
                     pvalueCutoff=0.99) # pvalueCutoff显著性应该为0.05
table(xx@compareClusterResult$Cluster) #每个基因集富集个数
head(as.data.frame(xx)) #查看完整结果
#dotplot(xx) #气泡图
df_go_diff <- as.data.frame(xx)

接下来选择可视化的通路。
为了配合像文中的堆叠感，这里选择有细胞群交集的通路。

## 选择富集到两组样本以上的通路可视化
df <- as.data.frame(table(df_go_diff$Description))
# > head(df)
# Var1 Freq
# 1          actin binding    2
# 2 actin filament binding    1
# 3        actinin binding    2
# 4        alcohol binding    2
# 5  alpha-actinin binding    2
# 6          amide binding    2
df <- df[order(df$Freq,decreasing = T),][1:20,]

df_go_diff <- df_go_diff[df_go_diff$Description %in% df$Var1,]
df_go_diff <- df_go_diff[order(df_go_diff$Description,decreasing = F),]

colnames(df_go_diff)
#[1] "Cluster"     "ID"          "Description" "GeneRatio"   "BgRatio"    
# [6] "pvalue"      "p.adjust"    "qvalue"      "geneID"      "Count"
table(df_go_diff$Description)

###### step3:Circular barplot ###### 
## 相似通路合并(给个大分类)
df_go_diff$group <- factor(c(rep('A',10),rep('B',10),rep('C',16),rep('D',16)))

# 分割弦图需添加NA Set a number of 'empty bar'
empty_bar <- 4
# Add lines to the initial dataset
nObsType <- nlevels(as.factor(df_go_diff$Description)) #20
to_add <- data.frame(matrix(NA, empty_bar*nlevels(df_go_diff$Cluster),ncol(df_go_diff))) # 11列，16个NA
colnames(to_add) <- colnames(df_go_diff)
to_add$group <- rep(levels(df_go_diff$group),each=empty_bar)

data <- rbind(df_go_diff, to_add)
data <- data %>% arrange(group) # 根据group排序
write.csv(data,file = 'data_for_go.csv')

id列是根据Description添加序号，同个通路序号相同，但同个通路的数量不确定。不知道r语言如何操作，就直接在excel完成

可视化

确定标签及倾斜角度

# 在excel中手动添加按顺序添加id
data <- read.csv('data_for_go.csv',row.names = 1)
colnames(data)

# Get the name and the y position of each label
label_data <- data %>% group_by(id, Description) %>% summarize(tot=sum(-log10(qvalue)))
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.2) /number_of_bar     # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)

> label_data
# A tibble: 24 x 5
# Groups:   id [24]
      id Description                 tot hjust angle
   <int> <chr>                     <dbl> <dbl> <dbl>
 1     1 actin binding              8.17     0   78 
 2     2 actinin binding            3.38     0   63 
 3     3 alcohol binding            5.26     0   48.
 4     4 alpha-actinin binding      1.62     0   33 
 5     5 amide binding              2.78     0   18 
 6     6 NA                        NA        0    3 
 7     7 amyloid-beta binding       3.16     0  -12 
 8     8 calmodulin binding         2.02     0  -27 
 9     9 carboxylic acid binding    3.64     0  -42.
10    10 carboxypeptidase activity  1.70     0  -57.
# ... with 14 more rows

确定圈内的注释

# prepare a data frame for base lines 添加A-D分组
empty_bar <- 4
base_data <- data %>% 
  group_by(group) %>% 
  summarize(start=min(id), end=max(id) - 1) %>% 
  rowwise() %>% 
  mutate(title=mean(c(start, end)))
# > head(base_data)
# # A tibble: 4 x 4
# # Rowwise: 
# group start   end title
# <chr> <int> <dbl> <dbl>
# 1 A         1     5     3 #从第一注释块到第五个画线，标记A组
# 2 B         7    11     9
# 3 C        13    17    15
# 4 D        19    23    21

library(ggplot2)
library(viridis)
p <- ggplot(data,aes(x=as.factor(id),y=-log10(qvalue),fill=Cluster))+
  # Add the stacked bar
  geom_bar(stat = "identity",alpha=0.5)+
  scale_fill_viridis(discrete=TRUE)+
  theme_minimal()+
  # 旋转
  coord_polar(start = 0)+ 
  theme_minimal()+
  ylim(-5,10)+
  theme(
    #legend.position = "none",
    axis.text = element_blank(),
    axis.title = element_blank(),
    panel.grid = element_blank()
    #plot.margin = margin(0.5, -5, 0.5, 0.5, "cm") 
  ) + coord_polar()+
  # Add labels on top of each bar
  geom_text(data=label_data, aes(x=id, y=tot+0.3, label=Description, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=3, angle= label_data$angle, inherit.aes = FALSE )+
  # geom_vline(aes(xintercept = 24.1),color="grey",slope=24)+
  # Add base line information
  geom_segment(data=base_data, aes(x = start, y = -0.5, xend = end, yend = -0.5), colour = "black", alpha=0.6, size=0.8, inherit.aes = FALSE )+
  geom_text(data=base_data, aes(x = title, y = -1, label=group), hjust=c(1,1,0,0), colour = "black", alpha=0.8, size=4, fontface="bold", inherit.aes = FALSE)+
  # Add text showing the value of each 100/75/50/25 lines
  ggplot2::annotate("text", x = c(rep(max(data$id),5),24.5), y = c(0, 2,4,6,8,9), label = c("0", "2", "4","6","8","-log10(qvalue)") , color="grey", size=2 , angle=c(rep(0,5),6), fontface="bold", hjust=1)
  
p
ggsave(p,filename = 'Circular barplot.pdf',units = 'cm',width = 22,height = 15)

疑问

但是图片的通路名字一直显示不全，尝试过通过调整与边距的距离`theme（plot.margin = margin(0.5, -5, 0.5, 0.5, "cm"), 还有 par(mar=c(8, 4.1, 4.1, 2.1)) 都解决不了
也尝试过用

library(yyplot2) ggpar(mar=rep(6,4)) 也不行。开头的直方图有变化，但运用到整个弦图没变化

用par设置ggplot2参数？这个可以有！(guangchuangyu.github.io)

ggpar(mar=rep(6,4))
p <- ggplot(data,aes(x=as.factor(id),y=-log10(qvalue),fill=Cluster))+
  # Add the stacked bar
  geom_bar(stat = "identity",alpha=0.5)+
  scale_fill_viridis(discrete=TRUE)
p

ggpar(mar=rep(1,4))
p <- ggplot(data,aes(x=as.factor(id),y=-log10(qvalue),fill=Cluster))+
  # Add the stacked bar
  geom_bar(stat = "identity",alpha=0.5)+
  scale_fill_viridis(discrete=TRUE)
p