[英]R: circlize circos plot - how to plot unconnected areas between sectors with minimal overlap
我有一組數據框,在4組患者和細胞類型之間具有共同特征。 我有很多不同的功能,但共享的功能(存在於多個組中)只是少數幾個。
我想制作一個圓圈圖,它反映了患者群體和細胞類型之間共享特征之間的少數聯系,同時了解每組中有多少非共享特征。
我想到它的方式,它應該是一個有4個扇區的圖(每組患者和細胞類型一個),它們之間有一些連接。 每個扇區大小應反映組中的要素總數,並且該區域的大部分不應連接到其他組,而是空的。
這就是我到目前為止所做的,但我不希望扇區專用於每個功能,只需要每組患者和細胞類型。
MWE:
library(circlize)
patients <- c(rep("patient1",20), rep("patient2",10))
cell.types <- c(rep("cell1",12), rep("cell2",8),rep("cell1",6), rep("cell2",4))
features <- c(paste("feature",1:12,sep="_"), paste("feature",9:16,sep="_"), paste("feature",c(1,2,9,10,17,18),sep="_"), paste("feature",c(1,18,19,20),sep="_"))
dat <- data.frame(patient=patients, cell.type=cell.types, feature=features)
dat
dat <- with(dat, table(paste(patient,cell.type,sep='|'), feature))
dat
chordDiagram(as.data.frame(dat), transparency = 0.5)
編輯!!
@ m-dz在他的回答中顯示的實際上是我正在尋找的格式,4個不同的患者/ cell.type組合的4個扇區,只顯示連接,而未連接的功能,雖然未顯示,應該占該部門的規模。
但是,我意識到我的情況比上面的MWE更復雜。
的特征被認為是出現在2患者/ cell.type基團,而不是僅當它是在2組相同的 ,而且當它是類似 ...(高於閾值的序列同一性)。 這樣,我有裁員......
患者1細胞1中的特征A可以連接到患者2細胞1中的特征A,但也可以連接到特征B ...特征A應該僅對患者1細胞1計數一次(唯一計數),並擴展到患者2中的2個不同特征 - 小區1。
請參閱下面的示例,了解我的實際數據如何更精確,看看是否可以使用此示例,我們可以獲得最終的圓圈圖! 謝謝!!
##MWE
#NON OVERLAPPING SETS!
#1: non-shared features
nonshared <- data.frame(patient=c(rep("pat1",20), rep("pat2",10)), cell.type=c(rep("cell1",12), rep("cell2",8),rep("cell1",6), rep("cell2",4)), feature=paste("a",1:30,sep=''))
nonshared
#2: features shared between cell types within same patient
sharedcells <- data.frame(patient=c(rep("pat1",3), rep("pat2",4)), cell.types=c(rep("cell1||cell2",3),rep("cell1||cell2",4)), features=c("b1||b1","b1||b1","b1||b1","b2||b2","b3||b3","b4||b4","b4||b5"))
sharedcells
#3: features shared between patients within same cell types
sharedpats <- data.frame(patients=c(rep("pat1||pat2",2), rep("pat1||pat2",6)), cell.type=c(rep("cell1",2),rep("cell2",6)), features=c("c1||c1","c2||c1","c3||c3","c3||c4","c3||c5","c6||c5","c7||c7","c8||c8"))
sharedpats
#4: features shared between patients and cell types
#4.1: shared across pat1-cell1, pat1-cell2, pat2-cell1, pat2-cell2
sharedall1 <- data.frame(both=c(rep("pat1-cell1||pat1-cell2||pat2-cell1||pat2-cell2",4)), features=c("d1||d1||d1||d1","d2||d2||d2||d3","d4||d4||d3||d3","d5||d5||d5||d5"))
#4.2: shared across pat1-cell1, pat1-cell2, pat2-cell1
sharedall2 <- data.frame(both=c(rep("pat1-cell1||pat1-cell2||pat2-cell1",2)), features=c("d6||d6||d6","d7||d7||d7"))
#4.3: shared across pat1-cell1, pat1-cell2, pat2-cell2
sharedall3 <- data.frame(both="pat1-cell1||pat1-cell2||pat2-cell2", features="d8||d8||d9")
#4.4: shared across pat1-cell1, pat2-cell1, pat2-cell2
sharedall4 <- data.frame(both="pat1-cell1||pat2-cell1||pat2-cell2", features="d10||d10||d9")
#4.5: shared across pat1-cell2, pat2-cell1, pat2-cell2
sharedall5 <- data.frame(both=c(rep("pat1-cell2||pat2-cell1||pat2-cell2",3)), features=c("d11||d11||d11","d12||d13||d13","d12||d14||d14"))
#4.6: shared across pat1-cell1, pat2-cell2
sharedall6 <- data.frame()
#4.7: shared across pat1-cell2, pat2-cell1
sharedall7 <- data.frame(both=c(rep("pat1-cell2||pat2-cell1",2)), features=c("d15||d16","d17||d17"))
sharedall <- rbind(sharedall1, sharedall2, sharedall3, sharedall4, sharedall5, sharedall6, sharedall7)
sharedall
#you see there might be overlaps between the different subsets of sharedall, but not between sharedall, sharedparts, sharedcells, and nonshared
#I NEED A CIRCOS PLOT THAT SHOWS ALL THE CONNECTIONS. THE NON-CONNECTED (nonshared) FEATURES SHOULD NOT BE SHOWN, BUT THE SHOULD COUNT TO THE SIZE OF THE SECTOR (CORRESPONDING TO A PATIENT-CELL COMBINATION)
#THE FEATURES SHOULD BE COUNT UNIQUELY, SO IF THERE ARE ENTRIES LIKE:
#3 pat1||pat2 cell2 c3||c3
#4 pat1||pat2 cell2 c3||c4
#5 pat1||pat2 cell2 c3||c5
#THE FEATURE c3 SHOULD BE COUNT ONCE FOR pat1, AND EXPAND TO 3 DIFFERENT FEATURES IN pat2
關於預期結果的附注:目的是創建一個圖表,顯示共享多少要素,忽略單個要素(下面的第1個圖)或共享要素重疊(例如,在第二個圖上看起來所有相同的要素共享群體,這看起來不是第一個情節,但重要的是群體之間共享的特征的比例)。
下面的代碼產生以下兩個數字(圖1左側供參考):
所有個人特色
簡單的獨特和共享功能
其中一個應該滿足期望。
# Prep. data --------------------------------------------------------------
nonshared <- data.frame(patient=c(rep("pat1",20), rep("pat2",10)), cell.type=c(rep("cell1",12), rep("cell2",8),rep("cell1",6), rep("cell2",4)), feature=paste("a",1:30,sep=''))
sharedcells <- data.frame(patient=c(rep("pat1",3), rep("pat2",4)), cell.types=c(rep("cell1||cell2",3),rep("cell1||cell2",4)), features=c("b1||b1","b1||b1","b1||b1","b2||b2","b3||b3","b4||b4","b4||b5"))
sharedpats <- data.frame(patients=c(rep("pat1||pat2",2), rep("pat1||pat2",6)), cell.type=c(rep("cell1",2),rep("cell2",6)), features=c("c1||c1","c2||c1","c3||c3","c3||c4","c3||c5","c6||c5","c7||c7","c8||c8"))
sharedall1 <- data.frame(both=c(rep("pat1-cell1||pat1-cell2||pat2-cell1||pat2-cell2",4)), features=c("d1||d1||d1||d1","d2||d2||d2||d3","d4||d4||d3||d3","d5||d5||d5||d5"))
sharedall2 <- data.frame(both=c(rep("pat1-cell1||pat1-cell2||pat2-cell1",2)), features=c("d6||d6||d6","d7||d7||d7"))
sharedall3 <- data.frame(both="pat1-cell1||pat1-cell2||pat2-cell2", features="d8||d8||d9")
sharedall4 <- data.frame(both="pat1-cell1||pat2-cell1||pat2-cell2", features="d10||d10||d9")
sharedall5 <- data.frame(both=c(rep("pat1-cell2||pat2-cell1||pat2-cell2",3)), features=c("d11||d11||d11","d12||d13||d13","d12||d14||d14"))
sharedall6 <- data.frame()
sharedall7 <- data.frame(both=c(rep("pat1-cell2||pat2-cell1",2)), features=c("d15||d16","d17||d17"))
sharedall <- rbind(sharedall1, sharedall2, sharedall3, sharedall4, sharedall5, sharedall6, sharedall7)
#I NEED A CIRCOS PLOT THAT SHOWS ALL THE CONNECTIONS. THE NON-CONNECTED (nonshared) FEATURES SHOULD NOT BE SHOWN, BUT THE SHOULD COUNT TO THE SIZE OF THE SECTOR (CORRESPONDING TO A PATIENT-CELL COMBINATION)
#THE FEATURES SHOULD BE COUNT UNIQUELY, SO IF THERE ARE ENTRIES LIKE:
#3 pat1||pat2 cell2 c3||c3
#4 pat1||pat2 cell2 c3||c4
#5 pat1||pat2 cell2 c3||c5
#THE FEATURE c3 SHOULD BE COUNT ONCE FOR pat1, AND EXPAND TO 3 DIFFERENT FEATURES IN pat2
# Start -------------------------------------------------------------------
library(circlize)
library(data.table)
library(magrittr)
library(stringr)
library(RColorBrewer)
# Split and pad with 0 ----------------------------------------------------
fun <- function(x) unlist(tstrsplit(x, split = '||', fixed = TRUE))
nonshared %>% setDT()
sharedcells %>% setDT()
sharedpats %>% setDT()
sharedall %>% setDT()
nonshared <- nonshared[, .(group = paste(patient, cell.type, sep = '-'), feature)][, feature := paste0('a', str_pad(str_extract(feature, '[0-9]+'), 2, 'left', '0'))]
sharedcells <- sharedcells[, lapply(.SD, fun), by = 1:nrow(sharedcells)][, .(group = paste(patient, cell.types, sep = '-'), feature = features)][, feature := paste0('b', str_pad(str_extract(feature, '[0-9]+'), 2, 'left', '0'))]
sharedpats <- sharedpats[, lapply(.SD, fun), by = 1:nrow(sharedpats)][, .(group = paste(patients, cell.type, sep = '-'), feature = features)][, feature := paste0('c', str_pad(str_extract(feature, '[0-9]+'), 2, 'left', '0'))]
sharedall <- sharedall[, lapply(.SD, fun), by = 1:nrow(sharedall)][, .(group = both, feature = features)][, feature := paste0('d', str_pad(str_extract(feature, '[0-9]+'), 2, 'left', '0'))]
dt_split <- rbindlist(
list(
nonshared,
sharedcells,
sharedpats,
sharedall
)
)
# Set key and self join to find shared features ---------------------------
setkey(dt_split, feature)
dt_join <- dt_split[dt_split, .(group, i.group, feature), allow.cartesian = TRUE] %>%
.[group != i.group, ]
# Create a "sorted key" ---------------------------------------------------
# key := paste(sort(.SD)...
# To leave only unique combinations of groups and features
dt_join <-
dt_join[,
key := paste(sort(.SD), collapse = '|'),
by = 1:nrow(dt_join),
.SDcols = c('group', 'i.group')
] %>%
setorder(feature, key) %>%
unique(by = c('key', 'feature')) %>%
.[, .(
group_from = i.group,
group_to = group,
feature = feature)]
# Rename and key ----------------------------------------------------------
dt_split %>% setnames(old = 'group', new = 'group_from') %>% setkey(group_from, feature)
dt_join %>% setkey(group_from, feature)
# Individual features -----------------------------------------------------
# Features without connections --------------------------------------------
dt_singles <- dt_split[, .(group_from, group_to = group_from, feature)] %>%
.[, N := .N, by = feature] %>%
.[!(N > 1 & group_from == group_to), !c('N')]
# Bind all, add some columns etc. -----------------------------------------
dt_bind <- rbind(dt_singles, dt_join) %>% setorder(group_from, feature, group_to)
dt_bind[, ':='(
group_from_f = paste(group_from, feature, sep = '.'),
group_to_f = paste(group_to, feature, sep = '.'))]
dt_bind[, feature := NULL] # feature can be removed
# Colour
dt_bind[, colour := ifelse(group_from_f == group_to_f, "#FFFFFF00", '#00000050')] # Change first to #FF0000FF to show red blobs
# Prep. sectors -----------------------------------------------------------
sectors_f <- union(dt_bind[, group_from_f], dt_bind[, group_to_f]) %>% sort()
colour_lookup <-
union(dt_bind[, group_from], dt_bind[, group_to]) %>% sort() %>%
structure(seq_along(.) + 1, names = .)
sector_colours <- str_replace_all(sectors_f, '.[a-d][0-9]+', '') %>%
colour_lookup[.]
# Gaps between sectors ----------------------------------------------------
gap_sizes <- c(0.0, 1.0)
gap_degree <-
sapply(table(names(sector_colours)), function(i) c(rep(gap_sizes[1], i-1), gap_sizes[2])) %>%
unlist() %>% unname()
# gap_degree <- rep(0, length(sectors_f)) # Or no gap
# Plot! -------------------------------------------------------------------
# Each "sector" is a separate patient/cell/feature combination
circos.par(gap.degree = gap_degree)
circos.initialize(sectors_f, xlim = c(0, 1))
circos.trackPlotRegion(ylim = c(0, 1), track.height = 0.05, bg.col = sector_colours, bg.border = NA)
for(i in 1:nrow(dt_bind)) {
row_i <- dt_bind[i, ]
circos.link(
row_i[['group_from_f']], c(0, 1),
row_i[['group_to_f']], c(0, 1),
border = NA, col = row_i[['colour']]
)
}
# "Feature" labels
circos.trackPlotRegion(track.index = 2, ylim = c(0, 1), panel.fun = function(x, y) {
sector.index = get.cell.meta.data("sector.index")
circos.text(0.5, 0.25, sector.index, col = "white", cex = 0.6, facing = "clockwise", niceFacing = TRUE)
}, bg.border = NA)
# "Patient/cell" labels
for(s in names(colour_lookup)) {
sectors <- sectors_f %>% { .[str_detect(., s)] }
highlight.sector(
sector.index = sectors, track.index = 1, col = colour_lookup[s],
text = s, text.vjust = -1, niceFacing = TRUE)
}
circos.clear()
# counts of unique and shared features ------------------------------------
xlims <- dt_split[, .N, by = group_from][, .(x_from = 0, x_to = N)] %>% as.matrix()
links <- dt_join[, .N, by = .(group_from, group_to)]
colours <- dt_split[, unique(group_from)] %>% structure(seq_along(.) + 1, names = .)
library(circlize)
sectors = names(colours)
circos.par(cell.padding = c(0, 0, 0, 0))
circos.initialize(sectors, xlim = xlims)
circos.trackPlotRegion(ylim = c(0, 1), track.height = 0.05, bg.col = colours, bg.border = NA)
for(i in 1:nrow(links)) {
link <- links[i, ]
circos.link(link[[1]], c(0, link[[3]]), link[[2]], c(0, link[[3]]), col = '#00000025', border = NA)
}
# "Patient/cell" labels
for(s in sectors) {
highlight.sector(
sector.index = s, track.index = 1, col = colours[s],
text = s, text.vjust = -1, niceFacing = TRUE)
}
circos.clear()
編輯:只需添加刪除評論中的鏈接:請參閱此答案以獲取標簽的一個很好的示例!
@ m-dz提供了正確的方向。 我可以提供有關模擬數據的更多詳細信息。
讓我們從這里開始:
patients <- c(rep("patient1",20), rep("patient2",10))
cell.types <- c(rep("cell1",12), rep("cell2",8),rep("cell1",6), rep("cell2",4))
features <- c(paste("feature",1:12,sep="_"), paste("feature",9:16,sep="_"), paste("feature",c(1,2,9,10,17,18),sep="_"), paste("feature",c(1,18,19,20),sep="_"))
dat <- data.frame(patient=patients, cell.type=cell.types, feature=features)
dat <- with(dat, table(paste(patient,cell.type,sep='|'), feature))
as.data.frame
將dat
轉換為三列數據框(即一個鄰接列表,其中鏈接從第一列開始,指向第二列)
dat = as.data.frame(dat, stringsAsFactors = FALSE)
為患者/細胞和特征生成顏色。
features = unique(dat[[2]])
features_col = structure(rand_color(length(features)), names = features)
patients_col = structure(2:5, names = unique(dat[[1]]))
如果一個特征僅存在於一個患者/細胞組合中,您不想顯示它但仍希望保持其在圖中的位置,您可以將#FFFFFF00
設置為其顏色(白色,具有完全透明度,以便它不會涵蓋其他鏈接)。 在這里,我們希望鏈接顏色與特征扇區相同。
col = ifelse(dat[[3]], features_col[dat[[2]]], "#FFFFFF00")
col = gsub("FF$", "80", col) # half transparent
features_count = tapply(dat[[3]], dat[[2]], sum)
# set color to white if it only exists in one patient/cell
col[features_count[dat[[2]]] == 1] = "#FFFFFF00"
最后的和弦圖:
chordDiagram(dat, col = col, grid.col = c(features_col, patients_col))
您可以在特征扇區中看到至少有兩個指向患者/細胞的鏈接。
准備好數據
library(circlize)
patients <- c(rep("patient1",20), rep("patient2",10))
cell.types <- c(rep("cell1",12), rep("cell2",8),rep("cell1",6), rep("cell2",4))
features <- c(paste("feature",1:12,sep="_"), paste("feature",9:16,sep="_"), paste("feature",c(1,2,9,10,17,18),sep="_"), paste("feature",c(1,18,19,20),sep="_"))
dat <- data.frame(patient=patients, cell.type=cell.types, feature=features)
dat <- with(dat, table(paste(patient,cell.type,sep='|'), feature))
dat<-as.data.frame(dat,stringsAsFactors = FALSE)
獲得患者和細胞類型的所有組合
df=NULL
for(i in levels(as.factor(dat$feature))){
temp<-as.data.frame(matrix(combn(dat[which(dat$feature==i),1],2),byrow = TRUE,ncol=2),stringsAsFactors = FALSE)
temp$feature=i
temp$Freq=1
Freq_0<-subset(dat$Var1,dat$feature==i & dat$Freq==0)
for(j in Freq_0){
temp$Freq[temp$V1==j | temp$V2==j]=0
}
df<-rbind(df,temp)
}
添加顏色
df$color=rainbow(dim(df)[1])
df[which(df$Freq==0),5]="white"
df$Freq=1
chordDiagram(df[,c(-3,-5)], transparency = 0.5,col = df$color)
不同的鏈接意味着不同的特征,鏈接顏色為白色,其中'Freq'為0
如果你想留下'feature'屬性......讓我們先准備好數據
library(circlize)
patients <- c(rep("patient1",20), rep("patient2",10))
cell.types <- c(rep("cell1",12), rep("cell2",8),rep("cell1",6), rep("cell2",4))
features <- c(paste("feature",1:12,sep="_"), paste("feature",9:16,sep="_"), paste("feature",c(1,2,9,10,17,18),sep="_"), paste("feature",c(1,18,19,20),sep="_"))
dat <- data.frame(patient=patients, cell.type=cell.types, feature=features)
dat <- with(dat, table(paste(patient,cell.type,sep='|'), feature))
dat<-as.data.frame(dat,stringsAsFactors = FALSE)
df=NULL
for(i in levels(as.factor(dat$feature))){
temp<-as.data.frame(matrix(combn(dat[which(dat$feature==i),1],2),byrow = TRUE,ncol=2),stringsAsFactors = FALSE)
temp$feature=i
temp$Freq=1
Freq_0<-subset(dat$Var1,dat$feature==i & dat$Freq==0)
for(j in Freq_0){
temp$Freq[temp$V1==j | temp$V2==j]=0
}
df<-rbind(df,temp)
}
處理過它
library(dplyr)
df1<-subset(df,df$Freq==1)
df0<-subset(df,df$Freq==0)
df1_mod<-summarise(group_by(df1,V1,V2),Freq=n())
df0_mod<-summarise(group_by(df0,V1,V2),Freq=n())
添加顏色
df1_mod$color<-rainbow(5)
df0_mod$color<-"white"
df_res<-rbind(df0_mod,df1_mod)
畫出來
chordDiagram(df_res, transparency = 0.5,col = df_res$color)
這些圖片顯示'Freq'中有很多零。
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