R: digitize pie plot - how to plot unrelated areas between sectors with minimal overlap

A note on expected result: the goal was to create a graph showing how many common functions are there, ignoring individual functions (1st graph below) or overlapping common functions (e.g. 2nd graph looks like the same functions shared between all groups, which is incorrectly considered by the 1st plot, but the ratio of functions shared between groups is important here).

The code below shows the following two digits (Figure 1 on the left for reference):

All individual functions

Easy count of unique and common features

One of them must meet expectations.

# 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()

      

        
        
        
      

    

Edit: just by adding a link from the deleted comment: see this answer for a nice example of marking!