circos图r语言 circos r语言

准备数据

需要准备两个数据：一个是基因表达谱，另一个是基因的注释（可以为KO注释，也可以是别的什么注释）

基因表达谱

	sample1	sample2	sample3	...
gene1	1.0	2.0	2.0	...
gene2	3.0	3.0	4.0	...
gene3	5.0	5.0	5.0	...
gene4	6.0	7.0	9.0	...
...	...	...	...	...

通路信息

gene	KO	pathway
gene1	KO1	pathway1
gene2	KO2	pathway1
gene3	KO2	pathway2
...	...	...

模拟数据

library(tidyverse)
library(magrittr)
library(circlize)
#模拟数据
## Data1
fpkm <- rbind(cbind(matrix(rnorm(500*3, mean = 1), nr = 500), 
                   matrix(rnorm(500*3, mean = 2), nr = 500),
                   matrix(rnorm(500*3, mean = 3), nr = 500)))
fpkm <- fpkm[sample(500, 500), ] # randomly permute rows
rownames(fpkm) <- paste0("gene", seq(500))
colnames(fpkm) <- c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3")
fpkm %<>% as.data.frame() %>% mutate(gene = row.names(.))
# Data2
pathways <- rep(paste0("pathway", seq(6)),  sample(12:20, size = 6)) %>% sample(70)
KOs <- rep(paste0("KO", seq(20)),  sample(5:20, size = 20, replace = TRUE)) %>% sample(70)
KOannotation <- data.frame(KO=KOs, pathway=pathways)
KOannotation <- KOannotation[sample(70, 200, TRUE),]
KOannotation$gene <- sample(paste0("gene", seq(500)),200)
             
# 假设你有富集到的想要可视化的通路
maps <- c("pathway1", "pathway2", "pathway3")
samples <- c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3")

思考画图数据类型

首先简单介绍一下circos对象。正如普通的图有x轴和Y轴，你可以理解为一个circos图有很多个轴（具体数量由你数据确定）。那么每个轴上自然就有对应位置（如下图中的1、2、3、4）。

那么自然而然的很容易想象，如果要画link，需要一个类似这样的数据

from_axis	from_position	to_axis	to_position
A	1	B	4
A	2	C	5
A	3	D	6

进一步的，如果要控制link的宽度，那应该需要规定每个link的起始和结束的位置

from_axis	from_position_start	from_position_end	to_axis	to_position_start	to_position_end
A	0.5	1.5	B	3.5	4.5
A	1.5	2.5	C	4.5	5.5
A	2.5	3.5	D	5.5	6.5

考虑完link之后，考虑热图。首先确认是用circos.heatmap()画的热图（如上图）。这个数据比较简单，因此考虑先画出热图，再考虑怎么画中间的和弦图。

热图

数据清洗

# 准备热图颜色
col_fun1 = colorRamp2(c(-2, 0, 2), c("#247ab5", "white", "#fda1a0"))
# 需要画图的基因
plot_gene <- KOannotation %>% 
  filter(pathway %in% maps) %>% 
  pull(gene) %>%
  {filter(fpkm, row.names(fpkm) %in% .)}
# 需要画图的KO
plot_KO <- plot_gene %>% 
  left_join(KOannotation) %>%
  filter(pathway %in% maps) %>% # There are some unenriched map
  group_by(KO) %>%
  summarise(across(samples,sum))
# 需要画图的Pathway
plot_map <- plot_gene %>% 
    left_join(KOannotation) %>%
    filter(pathway %in% maps) %>% # There are some unenriched map
    group_by(pathway) %>%
    summarise(across(samples,sum))

plot_data1 <- bind_rows(plot_gene %>% rename(id=gene), 
                        plot_KO %>% rename(id=KO), 
                        plot_map %>% rename(id=pathway)) %>%
    `row.names<-`(.$id) %>%
    select(-id)
plot_data1 <- t(scale(t(plot_data1))) %>% as.data.frame()
# 在热图上划分为gene、KO、pathway
lev_split = row.names(plot_data1) %>% str_match("[a-zA-Z]+") %>% factor()

画图

circos.clear()
circos.par(gap.degree=10, track.height=0.1)
# 分多次画表达谱数据，更有层次
circos.heatmap(plot_data1[samples[1:3]], split = lev_split , col = col_fun1, rownames.side = "outside", cluster = TRUE)
circos.heatmap(plot_data1[samples[7:9]], split = lev_split , col = col_fun1)
circos.heatmap(plot_data1[samples[4:6]], split = lev_split , col = col_fun1)

根据lev_split,这个热图划分为gene,KO,和pathway三个轴。这里需要指出的是，每个gene、KO、pathway在每个轴上占的长度都是1，例如gene67在gene轴上的位置为0-1，pathway2在pathway轴上的位置为0-1.因此我们要画link的话，根据之前的讨论，如果有三个KO需要连接到pathway2，应该需要一个类似下面的数据：

from_axis	from_position_start	from_position_end	to_axis	to_position_start	to_position_end
KO	0.5	1.5	pathway	0	0.333
KO	1.5	2.5	pathway	0.333	0667
KO	2.5	3.5	pathway	0.667	1

注意上表，可能存在多个KO连接到一个pathway的情况，因此我们要对start和end位置进行合理的拆分，以避免重叠。这样做的另一个好处是可以让link线有粗有细，看上去美观许多。所以

另一方面我们要注意，由于circos热图上的基因的是根据聚类结果排布的，所以和数据框里的数据顺序已经不一样了，因此我们首先需要获取画图后的每个gene、KO、pathway在其对应轴上的坐标，这时就需要用到circlize的get.cell.meta.data从图上获得相应信息

和弦图

数据清洗

根据上面的讨论结果，我们的数据清洗要实现两个目的：

1. 获取热图生成后的gene、KO、pathway上每个轴的信息，可以整理成如下格式：

id	sector	position
gene1	gene	23
KO1	KO	45
pathway1	pathway	56

表A

1. 将一对多的gene-KO关系、KO-pathway关系进行计算，以获得相对的位置

from_axis	from_position_start	from_position_end	to_axis	to_position_start	to_position_end
gene	0.5	1.5	KO	5	5.33
gene	1.5	2.5	KO	5.33	5.66
gene	2.5	3.5	KO	5.66	6

表B

注意观察上表，由于3个基因连接到了相同的KO，所以我将他们分别连接到了KO不同的位置

进一步，想要获得这个表，可以把过程拆分为以下几步：

2.1生成一个连接对象表

from	to
gene1	KO2
gene2	KO1
KO1	pathway1

2.2 根据连接对象在表中出现的次数，再计算一个位移

from	to	from_start	from_end	to_start	to_end
gene1	KO2	0	1	0	0.333
gene2	KO1	0	1	0.333	0.667
KO1	pathway1	0	0.5	0.667	1
KO1	pathway2	0.5	1	0	0.0333

表C

2.3 结合表A和表C，计算得到表B

明确思路以后，下面是代码

# 1 获得gene、KO、pathway在每个轴上的位置
plot_data3 = data.frame()
for(lev in levels(lev_split)){
  a <- rownames(plot_data1)[lev_split==lev][get.cell.meta.data("row_order", sector.index = lev)]
  a <- seq(length(a)) %>% `names<-`(a) %>% enframe("id", "position")
  a$sector = lev
  plot_data3 <- rbind(plot_data3, a)
} 
plot_data3$position <- plot_data3$position - 1 # 因为每个元素的范围是0~1，所以统一减一方便后面相加
#2.1 获取点对点连接表
plot_data2 <- KOannotation %>%
    filter(gene %in% row.names(plot_data1), KO %in% row.names(plot_data1)) %>%
    select(gene, KO) %>%
    rename(from=gene, to=KO)
tmp_obj1 <- KOannotation %>%
    filter(KO %in% row.names(plot_data1), pathway %in% row.names(plot_data1)) %>%
    select(KO, pathway) %>%
    rename(from=KO, to=pathway)
plot_data2 %<>% bind_rows(tmp_obj1) %>% distinct(from, to)
#2.2 计算位移
tmp_obj1<-plot_data2 %>% 
    group_by(from) %>% 
    mutate(V1=1/n()) %>% 
    group_by(from) %>% 
    mutate(from_end = cumsum(V1), 
           from_start = from_end-V1) %>%
    select(from, to, from_start, from_end)
tmp_obj2<-plot_data2 %>% 
    group_by(to) %>% 
    mutate(V1=1/n()) %>% 
    group_by(to) %>% 
    mutate(to_end = cumsum(V1), 
           to_start = to_end-V1) %>%
    select(from, to, to_start, to_end)
plot_data2 %<>% left_join(tmp_obj1) %>% left_join(tmp_obj2)
# 2.3 合并上述两表
plot_data2 %<>% 
  left_join(plot_data3, by=c('from'='id')) %>% 
  rename('from_position'=position, 'from_sector'=sector) %>%
  left_join(plot_data3, by=c('to'='id')) %>%
  rename('to_position'=position, 'to_sector'=sector)

由于上述plot_data2包含了全部点之间的连线关系，我们可能不需要这么多，所以可以挑选自己想要展示的数据。这一步可能需要反复画图几次确定需要展示的数据

#3. 进一步筛选想要展示的连线
# highlight the pathway I wanted
highlight_pathway <- c("pathway1", "pathway2")
highlight_KO <- c("KO5", "KO6", "KO20")
highlight_gene <-  c("gene292", "gene256", "gene67", "gene146", "gene52", "gene391", "gene139", "gene327", "gene218", "gene142", "gene375", "gene194")

plot_link <- plot_data2 %>% filter(to %in% c(highlight_pathway, highlight_KO)) %>% mutate(col="#dcdcdc80")
highlight_link <- plot_link %>% filter (from %in% c(highlight_gene, highlight_KO)) %>% mutate(col="#fb9b9a80")
plot_link <- plot_link %>% filter (!from %in% c(highlight_gene, highlight_KO))
plot_data2<-rbind(plot_link, highlight_link)

使用circos.link循环作图

for ( idx in seq(nrow(plot_data2))){
  tmp_obj <- plot_data2[idx,]
  circos.link(tmp_obj[['from_sector']], 
              c(tmp_obj[['from_position']] + tmp_obj[['from_start']], 
                tmp_obj[['from_position']] + tmp_obj[['from_end']]), 
              tmp_obj[['to_sector']], 
              c(tmp_obj[['to_position']] + tmp_obj[['to_start']], 
                tmp_obj[['to_position']] + tmp_obj[['to_end']]),
              col = tmp_obj[['col']],
              border = NA
              )
}

全部代码

library(tidyverse)
library(magrittr)
library(circlize)
#模拟数据
## Data1
fpkm <- rbind(cbind(matrix(rnorm(500*3, mean = 1), nr = 500), 
                   matrix(rnorm(500*3, mean = 2), nr = 500),
                   matrix(rnorm(500*3, mean = 3), nr = 500)))
fpkm <- fpkm[sample(500, 500), ] # randomly permute rows
rownames(fpkm) <- paste0("gene", seq(500))
colnames(fpkm) <- c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3")
fpkm %<>% as.data.frame() %>% mutate(gene = row.names(.))
# Data2
pathways <- rep(paste0("pathway", seq(6)),  sample(12:20, size = 6)) %>% sample(70)
KOs <- rep(paste0("KO", seq(20)),  sample(5:20, size = 20, replace = TRUE)) %>% sample(70)
KOannotation <- data.frame(KO=KOs, pathway=pathways)
KOannotation <- KOannotation[sample(70, 200, TRUE),]
KOannotation$gene <- sample(paste0("gene", seq(500)),200)
             
# 假设你有富集到的想要可视化的通路
maps <- c("pathway1", "pathway2", "pathway3")
samples <- c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3")
# 准备热图颜色
col_fun1 = colorRamp2(c(-2, 0, 2), c("#247ab5", "white", "#fda1a0"))
# 需要画图的基因
plot_gene <- KOannotation %>% 
  filter(pathway %in% maps) %>% 
  pull(gene) %>%
  {filter(fpkm, row.names(fpkm) %in% .)}
# 需要画图的KO
plot_KO <- plot_gene %>% 
  left_join(KOannotation) %>%
  filter(pathway %in% maps) %>% # There are some unenriched map
  group_by(KO) %>%
  summarise(across(samples,sum))
# 需要画图的Pathway
plot_map <- plot_gene %>% 
    left_join(KOannotation) %>%
    filter(pathway %in% maps) %>% # There are some unenriched map
    group_by(pathway) %>%
    summarise(across(samples,sum))

plot_data1 <- bind_rows(plot_gene %>% rename(id=gene), 
                        plot_KO %>% rename(id=KO), 
                        plot_map %>% rename(id=pathway)) %>%
    `row.names<-`(.$id) %>%
    select(-id)
plot_data1 <- t(scale(t(plot_data1))) %>% as.data.frame()
# 在热图上划分为gene、KO、pathway
lev_split = row.names(plot_data1) %>% str_match("[a-zA-Z]+") %>% factor()
circos.clear()
circos.par(gap.degree=10, track.height=0.1)
# 分多次画表达谱数据，更有层次
circos.heatmap(plot_data1[samples[1:3]], split = lev_split , col = col_fun1, rownames.side = "outside", cluster = TRUE)
circos.heatmap(plot_data1[samples[7:9]], split = lev_split , col = col_fun1)
circos.heatmap(plot_data1[samples[4:6]], split = lev_split , col = col_fun1)
plot_data3 = data.frame()
for(lev in levels(lev_split)){
  a <- rownames(plot_data1)[lev_split==lev][get.cell.meta.data("row_order", sector.index = lev)]
  a <- seq(length(a)) %>% `names<-`(a) %>% enframe("id", "position")
  a$sector = lev
  plot_data3 <- rbind(plot_data3, a)
} 
plot_data3$position <- plot_data3$position - 1 # 因为每个元素的范围是0~1，所以统一减一方便后面相加
#2.1 获取点对点连接表
plot_data2 <- KOannotation %>%
    filter(gene %in% row.names(plot_data1), KO %in% row.names(plot_data1)) %>%
    select(gene, KO) %>%
    rename(from=gene, to=KO)
tmp_obj1 <- KOannotation %>%
    filter(KO %in% row.names(plot_data1), pathway %in% row.names(plot_data1)) %>%
    select(KO, pathway) %>%
    rename(from=KO, to=pathway)
plot_data2 %<>% bind_rows(tmp_obj1) %>% distinct(from, to)
#2.2 计算位移
tmp_obj1<-plot_data2 %>% 
    group_by(from) %>% 
    mutate(V1=1/n()) %>% 
    group_by(from) %>% 
    mutate(from_end = cumsum(V1), 
           from_start = from_end-V1) %>%
    select(from, to, from_start, from_end)
tmp_obj2<-plot_data2 %>% 
    group_by(to) %>% 
    mutate(V1=1/n()) %>% 
    group_by(to) %>% 
    mutate(to_end = cumsum(V1), 
           to_start = to_end-V1) %>%
    select(from, to, to_start, to_end)
plot_data2 %<>% left_join(tmp_obj1) %>% left_join(tmp_obj2)
# 2.3 合并上述两表
plot_data2 %<>% 
  left_join(plot_data3, by=c('from'='id')) %>% 
  rename('from_position'=position, 'from_sector'=sector) %>%
  left_join(plot_data3, by=c('to'='id')) %>%
  rename('to_position'=position, 'to_sector'=sector)
highlight_pathway <- c("pathway1", "pathway2")
highlight_KO <- c("KO5", "KO6", "KO20")
highlight_gene <-  c("gene292", "gene256", "gene67", "gene146", "gene52", "gene391", "gene139", "gene327", "gene218", "gene142", "gene375", "gene194")

plot_link <- plot_data2 %>% filter(to %in% c(highlight_pathway, highlight_KO)) %>% mutate(col="#dcdcdc80")
highlight_link <- plot_link %>% filter (from %in% c(highlight_gene, highlight_KO)) %>% mutate(col="#fb9b9a80")
plot_link <- plot_link %>% filter (!from %in% c(highlight_gene, highlight_KO))
plot_data2<-rbind(plot_link, highlight_link)
for ( idx in seq(nrow(plot_data2))){
  tmp_obj <- plot_data2[idx,]
  circos.link(tmp_obj[['from_sector']], 
              c(tmp_obj[['from_position']] + tmp_obj[['from_start']], 
                tmp_obj[['from_position']] + tmp_obj[['from_end']]), 
              tmp_obj[['to_sector']], 
              c(tmp_obj[['to_position']] + tmp_obj[['to_start']], 
                tmp_obj[['to_position']] + tmp_obj[['to_end']]),
              col = tmp_obj[['col']],
              border = NA
              )
}

成品

注意点

1. 如果画多层圈图，会根据行号对齐，但是不会根据行名对齐。所以所有组的表达谱数据必须一步准备。
2. 热图和和弦图代码之间进行了大量数据清洗和计算，不要怕，没关系。因为只有拿到热图之后才能获取对应gene、KO、pathway的位置
3. 每次画图之前习惯性的用circos.clear()清理一下缓存。