Last updated: 2021-02-10

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Knit directory: melanoma_publication_old_data/

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Introduction

This script generates plots for Figure 3.

Preparations

knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())

Load libraries

First, we will load the libraries needed for this part of the analysis.

sapply(list.files("code/helper_functions", full.names = TRUE), source)
        code/helper_functions/calculateSummary.R
value   ?                                       
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        code/helper_functions/censor_dat.R
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        code/helper_functions/detect_mRNA_expression.R
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        code/helper_functions/DistanceToClusterCenter.R
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        code/helper_functions/findClusters.R
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        code/helper_functions/findCommunity.R
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        code/helper_functions/getCellCount.R
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        code/helper_functions/getInfoFromString.R
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        code/helper_functions/getSpotnumber.R
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        code/helper_functions/plotBarFracCluster.R
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        code/helper_functions/plotCellCounts.R
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        code/helper_functions/plotCellFrac.R
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        code/helper_functions/plotCellFracGroups.R
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        code/helper_functions/plotCellFracGroupsSubset.R
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        code/helper_functions/plotCellFractions.R
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        code/helper_functions/plotDist.R
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        code/helper_functions/scatter_function.R
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        code/helper_functions/sceChecks.R
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        code/helper_functions/validityChecks.R
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library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(umap)
library(data.table) 
library(fpc)
library(ggplot2)
library(cba)
library(ComplexHeatmap)
library(colorRamps)
library(circlize)
library(RColorBrewer)
library(ggbeeswarm)
library(destiny)
library(scater)
library(dittoSeq)
library(gridExtra)
library(ggpmisc)
library(neighbouRhood)
library(cowplot)
library(viridis)
library(ggpubr)
library(rstatix)

Read the data

sce_rna = readRDS(file = "data/sce_RNA.rds")
sce_prot = readRDS(file = "data/sce_protein.rds")

# meta data
dat_relation = fread(file = "data/rna/Object relationships.csv",stringsAsFactors = FALSE)

Prepare Relation Table

# prepare data 
dat_relation$cellID_first <- paste("RNA", paste(dat_relation$`First Image Number`, dat_relation$`First Object Number`, sep = "_"), sep = "_")
dat_relation$cellID_second <- paste("RNA", paste(dat_relation$`Second Image Number`, dat_relation$`Second Object Number`, sep = "_"), sep = "_")

Figure 3B

CP output

# Load and prepare
dat_cells = fread(file = "data/rna/cell.csv",stringsAsFactors = FALSE)
dat_relation = fread(file = "data/rna/Object relationships.csv",stringsAsFactors = FALSE)

# Number of cores used for multicore:
if(detectCores() >= 12){
  ncores = round(detectCores()/1.25,0)
}
if(detectCores() > 1 & detectCores() < 12){
  ncores = round(detectCores()/2,0)
}
if(detectCores() == 1){
  ncores = 1
}

n_perm = 100 

Start the analysis

start = Sys.time()
cur_sce <- as.data.frame(colData(sce_rna))

# add same cellID to dat_cells as in sce object
dat_cells$cellID <- paste("RNA_", paste(dat_cells$ImageNumber, dat_cells$ObjectNumber, sep = "_"), sep = "")

image_df <- data.frame()
for(size in c(2,3,4,5,6)) {
  images <- data.frame()
  for(i in colnames(cur_sce[,grepl("CCL|CXCL",colnames(cur_sce))])){
    # add chemokine info to celltype
    sce_info <- cur_sce[,c("cellID", i , "Description")]
    
    # add celltype information
    dat_cells_tmp <- left_join(as.data.frame(dat_cells), sce_info, by = "cellID")
    
    #assign labels and groups
    dat_cells_tmp$label <- dat_cells_tmp[,i]
    dat_cells_tmp$group <- dat_cells_tmp$Description
    dat_cells_tmp <- as.data.table(dat_cells_tmp)
    
    # subset dat_relation and dat_cells
    dat_cells_sub <- dat_cells_tmp#[dat_cells$Description == "P3",]
    dat_relation_sub <- dat_relation#[dat_relation$`First Image Number` == unique(sce_rna[,sce_rna$Description == "P3"]$ImageNumber),]
    
    # Prepare the data
    d = neighbouRhood::prepare_tables(dat_cells_sub, dat_relation_sub)
    
    # Calculate the baseline statistics
    dat_baseline = neighbouRhood::apply_labels(d[[1]], d[[2]]) %>%
      neighbouRhood::aggregate_classic_patch(., patch_size = size)
    
    # Calculate the permutation statistics
    # This will run the test using parallel computing. The name of the idcol does actually not matter.
    
    set.seed(12312)
    dat_perm = rbindlist(mclapply(1:n_perm, function(x){
      dat_labels = neighbouRhood::shuffle_labels(d[[1]])
      neighbouRhood::apply_labels(dat_labels, d[[2]]) %>%
        neighbouRhood::aggregate_classic_patch(., patch_size = size)
    },mc.cores = ncores
    ), idcol = 'run')
    
    # calc p values
    dat_p <- neighbouRhood::calc_p_vals(dat_baseline, dat_perm, n_perm = n_perm, p_tresh = 0.01) 
    
    # select interactions between chemokine+ cells
    dat_p$interaction <- paste(dat_p$FirstLabel, dat_p$SecondLabel, sep = "_")
    
    dat_p_wide <- dat_p %>%
      reshape2::dcast(group ~ interaction, value.var = "sigval", fill = 0) %>%
      select(group, `1_1`)
    
    summary <- as.data.frame(dat_p_wide) %>%
      group_by(`1_1`) %>%
      summarise(n=n(),.groups = 'drop') %>%
      ungroup() %>%
      mutate(percentage_sig = (n/sum(n)) * 100)
    
    images <- rbind(images, cbind(summary[1,], i))
  }
  
  # calculate percentage of images with significant patches
  images$percentage_sig <- 100 - images$percentage_sig
  images$patch_size <- size
  images <- select(images, percentage_sig, i, patch_size)
  colnames(images) <- c("significant_images", "chemokine", "patch_size")
  
  # add to data.frame
  image_df <- rbind(image_df, images)
}
end = Sys.time()

print(end-start)
Time difference of 21.94326 mins

Visualize

dat <- image_df %>%
  reshape2::dcast(chemokine ~ patch_size, value.var = "significant_images", fill = 0)

rownames(dat) <- dat$chemokine
dat$chemokine <- NULL

m <- t(as.matrix(dat))

col_fun = viridis::inferno(100)

Heatmap(m,
        cluster_rows = FALSE,
        col = col_fun,
        column_title = "Self-Interaction",
        column_title_side = "bottom",
        show_row_names = TRUE,
        cell_fun = function(j, i, x, y, width, height, fill) {
          grid.text(sprintf("%.1f", m[i, j]), x, y, gp = gpar(fontsize = 15, col = "grey"))
          },
        heatmap_legend_param = list(
          title = "% Significant\nImages", at = c(0, 10, 20, 30, 40, 50),
          labels = c("0%", "10%", "20%", "30%","40%", "50%")),
        row_title = "Patch Size",
        row_names_side = "left",
        width = unit(15, "cm"),
        height = unit(8, "cm"))

Calculate p-value for Figure 3B

start = Sys.time()
cur_sce <- as.data.frame(colData(sce_rna))

image_df <- data.frame()
for(size in c(2,3,4,5,6,7,8,9,10)) {
  images <- data.frame()
  for(i in c("CXCL10")){
    # add chemokine info to celltype
    sce_info <- cur_sce[,c("cellID", i , "Description")]
    
    # add celltype information
    dat_cells_tmp <- left_join(as.data.frame(dat_cells), sce_info, by = "cellID")
    
    #assign labels and groups
    dat_cells_tmp$label <- dat_cells_tmp[,i]
    dat_cells_tmp$group <- dat_cells_tmp$Description
    dat_cells_tmp <- as.data.table(dat_cells_tmp)
    
    # subset dat_relation and dat_cells
    dat_cells_sub <- dat_cells_tmp[Description == "P3",]
    dat_relation_sub <- dat_relation[dat_relation$`First Image Number` == unique(sce_rna[,sce_rna$Description == "P3"]$ImageNumber),]
    
    # Prepare the data
    d = neighbouRhood::prepare_tables(dat_cells_sub, dat_relation_sub)
    
    # Calculate the baseline statistics
    dat_baseline = neighbouRhood::apply_labels(d[[1]], d[[2]]) %>%
      neighbouRhood::aggregate_classic_patch(., patch_size = size)
    
    # Calculate the permutation statistics
    # This will run the test using parallel computing. The name of the idcol does actually not matter.
    
    set.seed(12312)
    dat_perm = rbindlist(mclapply(1:n_perm, function(x){
      dat_labels = neighbouRhood::shuffle_labels(d[[1]])
      neighbouRhood::apply_labels(dat_labels, d[[2]]) %>%
        neighbouRhood::aggregate_classic_patch(., patch_size = size)
    },mc.cores = ncores
    ), idcol = 'run')
    
    # calc p values
    dat_p <- neighbouRhood::calc_p_vals(dat_baseline, dat_perm, n_perm = n_perm, p_tresh = 0.01) 
    
    # select interactions between chemokine+ cells
    dat_p$interaction <- paste(dat_p$FirstLabel, dat_p$SecondLabel, sep = "_")
    images <- rbind(images,dat_p)
  }
  
  # calculate percentage of images with significant patches
  images$patch_size <- size
  
  # add to data.frame
  image_df <- rbind(image_df, images)
}

end = Sys.time()
print(end-start)
Time difference of 1.056387 mins
# Significant Self-Interatcions
image_df[image_df$interaction == "1_1",]
   group FirstLabel SecondLabel       p_gt p_lt direction          p   sig
1:    58          1           1 0.00990099    1      TRUE 0.00990099  TRUE
2:    58          1           1 0.00990099    1      TRUE 0.00990099  TRUE
3:    58          1           1 0.00990099    1      TRUE 0.00990099  TRUE
4:    58          1           1 0.00990099    1      TRUE 0.00990099  TRUE
5:    58          1           1 0.00990099    1      TRUE 0.00990099  TRUE
6:    58          1           1 0.00990099    1      TRUE 0.00990099  TRUE
7:    58          1           1 0.00990099    1      TRUE 0.00990099  TRUE
8:    58          1           1 0.00990099    1      TRUE 0.00990099  TRUE
9:    58          1           1 1.00000000    1     FALSE 1.00000000 FALSE
   sigval interaction patch_size
1:      1         1_1          2
2:      1         1_1          3
3:      1         1_1          4
4:      1         1_1          5
5:      1         1_1          6
6:      1         1_1          7
7:      1         1_1          8
8:      1         1_1          9
9:      0         1_1         10

Figure 3C

Example of CXCL10 Cluster and corresponding Community

example <- findClusters(sce_rna[,sce_rna$ImageNumber == 58], sce_rna[,sce_rna$CXCL10 == 1]$cellID, 
                    'cellID', 
                    'Center_X', 'Center_Y', 
                    'ImageNumber', 
                    distance = 20, 
                    min_clust_size = 10,
                    output_colname = "example_cluster")
Time difference of 1.139391 secs
[1] "clusters successfully added to sce object"
example <- findCommunity(example, 
              'cellID', 
              'Center_X', 'Center_Y', 
              'ImageNumber', 
              'example_cluster', 
              distance = 25,
              output_colname = "chemokine_community_i",
              plot = TRUE)
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Time difference of 3.615983 secs
[1] "communities successfully added to sce object"

Zoom-in plot for patch/milieu

example <- findClusters(sce_rna[,sce_rna$ImageNumber == 58], sce_rna[,sce_rna$CXCL10 == 1]$cellID, 
                    'cellID', 
                    'Center_X', 'Center_Y', 
                    'ImageNumber', 
                    distance = 20, 
                    min_clust_size = 10,
                    output_colname = "example_cluster")
Time difference of 0.845577 secs
[1] "clusters successfully added to sce object"
example <- findCommunity(example, 
              'cellID', 
              'Center_X', 'Center_Y', 
              'ImageNumber', 
              'example_cluster', 
              distance = 25,
              output_colname = "chemokine_community_i",
              plot = TRUE,
              xlim = c(725,850),
              ylim = c(500,675),
              point_size = 14)
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Warning: Removed 533 rows containing missing values (geom_point).
Time difference of 3.229455 secs
[1] "communities successfully added to sce object"

Figure 3D

Chemokine-producing cells in Patches

# define fractions of chemokines present in community
cur_dt <- data.frame(colData(sce_rna))

plot_list <- list()

for(i in names(cur_dt[,grepl(glob2rx("*pure"),names(cur_dt))])) {
  chemokine_name <- toupper(str_split(i, "_")[[1]][1])
  
  # select all chemokine-producing cells in a community
  unique_comms <- unique(cur_dt[cur_dt[,i] > 0,i])
  cur_dt_sub <- cur_dt[cur_dt[,i] %in% unique_comms,]
  
  cur_dt_sub <- cbind(cur_dt_sub[,i],
                      cur_dt_sub[,grepl(glob2rx("C*L*"),names(cur_dt_sub))])
  
  colnames(cur_dt_sub)[1] <- i
  
  # add celltype and MM_location_simplified
  cur_dt_sub$cellID <- rownames(cur_dt_sub)
  cur_dt_sub <- left_join(cur_dt_sub, cur_dt[,c("cellID", "celltype", "MM_location_simplified")])
  
  # melt the table
  cur_dt_sub <- cur_dt_sub %>%
    reshape2::melt(id.vars = c("cellID", "celltype", "MM_location_simplified", i), variable.name = "chemokine", value.name = "status")
  
  non_producer <- cur_dt_sub %>%
    group_by(cellID) %>%
    summarise(sum = sum(status)) %>%
    filter(sum == 0) %>%
    select(cellID)
    
  producer <- cur_dt_sub %>%
    group_by(cellID) %>%
    summarise(sum = sum(status)) %>%
    filter(sum > 0) %>%
    select(cellID)
  
  # select non-producing cells and count
  non_producer <- cur_dt_sub[cur_dt_sub$cellID %in% non_producer$cellID,] %>%
    distinct(cellID, .keep_all = TRUE) %>%
    group_by(celltype, MM_location_simplified, chemokine) %>%
    summarise(n=n())
  
  non_producer$chemokine <- "no chemokine"
  
  # select producing cells and count chemokines
  producer <- cur_dt_sub[cur_dt_sub$cellID %in% producer$cellID,] %>%
    filter(status == 1) %>%
    group_by(celltype, MM_location_simplified, chemokine) %>%
    summarise(n=n())
  
  summary <- rbind(producer, non_producer)
  
  summary_celltypes <- summary %>%
    group_by(celltype) %>%
    summarise(n=sum(n)) %>%
    mutate(percentage = n / sum(n) * 100) #%>%
    #filter(percentage > 5)
  
  summary_chemokines <- summary %>%
    group_by(celltype, chemokine) %>%
    summarise(n=sum(n)) #%>%
    #filter(celltype %in% summary_celltypes$celltype)
  
  # color_vector
  col_vector_cells <- metadata(sce_rna)$colour_vector$celltype
  col_vector_chemokines <- metadata(sce_rna)$colour_vectors$chemokine_single
  
  col_vector <- c(col_vector_cells, col_vector_chemokines)
  
  # add "no chemokine" to col_vector
  col_vector <- c(col_vector, "white")
  names(col_vector) <- c(names(col_vector[-length(col_vector)]), "no chemokine")
  
  # create labels for middle of sunburst plot
  
  # Number of detected Patches
  numberOfPatches <- paste(length(unique_comms), ifelse(length(unique_comms)>1," Milieus", " Milieu"), sep = "")
  
  # Median Number of Chemokine XY Producing Cells in a Patch
  medianCells <- cur_dt[cur_dt[,i] > 0 & cur_dt[,chemokine_name] == 1,] %>%
    group_by_at(i) %>%
    summarise(n=n()) %>%
    mutate(median = median(n))
  medianCells <- paste(round(unique(medianCells$median)), " Cells", sep = "")
    
  # Percentage of Chemokine produced in a Patch 
  percentageInPatches <- cur_dt[cur_dt[,chemokine_name] == 1,] %>%
    mutate(in_patch = ifelse(.[,i] > 0, 1, 0)) %>%
    group_by(in_patch) %>%
    summarise(n=n()) %>%
    mutate(percentage = n / sum(n) * 100) %>%
    filter(in_patch == 1)
  percentageInPatches <- paste(round(unique(percentageInPatches$percentage)), "%", sep = "")
  
  label <- paste(paste(numberOfPatches, medianCells, sep = "\n"), percentageInPatches, sep = "\n")
  
  # sunburst plot
  plt <- ggplot() + 
    geom_text(aes(x=0,y=0, label = label, size=1)) +
    geom_col(aes(x = 2, y = n, fill = celltype), 
           data = summary_celltypes, 
           color = "white",
           lwd = 1) + 
    geom_col(aes(x = 3, y = n, group = celltype, fill = chemokine), 
           data = summary_chemokines) +
    xlim(0, 3.5) + labs(x = NULL, y = NULL) + 
    scale_fill_manual(values = unname(col_vector),
                        breaks = names(col_vector),
                        labels = names(col_vector)) + 
    coord_polar(theta = "y") + 
    ggtitle(chemokine_name) +
    theme_void() +
    theme(axis.ticks=element_blank(),
          plot.margin = unit(c(0,0,0,0), "cm"),
          axis.text=element_blank(),
          axis.title=element_blank(),
          legend.position = "none",
          text = element_text(size = 18),
          plot.title = element_text(hjust = 0.5))
  
  # add to list
  plot_list[[i]] <- plot_grid(plt)

}

# plot sunburst plots (without CCL4, CCL22, CCL8 - low abundance communities)
plot_grid(plot_list$cxcl8_pure, plot_list$ccl2_pure, 
          plot_list$cxcl10_pure, plot_list$cxcl9_pure,
          plot_list$ccl18_pure, plot_list$ccl19_pure,
          plot_list$cxcl12_pure, plot_list$cxcl13_pure, 
          plot_list$ccl4_pure, plot_list$ccl22_pure,
          plot_list$ccl8_pure, 
          ncol = 3, aligh = "hv")
Warning in as_grob.default(plot): Cannot convert object of class character into
a grob.

Legend for Plot

# create legend for chemokines
lgd1 = Legend(labels = names(col_vector_chemokines), title = "Outer Circle\nChemokine", legend_gp = gpar(fill = unname(col_vector_chemokines)))

# create legend for celltypes
lgd2 = Legend(labels = names(col_vector_cells), title = "Inner Circle\nCell Type ", legend_gp = gpar(fill = unname(col_vector_cells)))

draw(packLegend(lgd2, lgd1, direction = "horizontal"))

Figure 3E

Expression levels in CD8 cells in milieus

milieus <- data.frame(colData(sce_rna)) %>%
  filter(celltype == "Tcytotoxic") %>%
  select(cellID, contains("pure")) %>%
  mutate_if(is.numeric, ~1 * (. > 0))

milieus$number_of_milieus <- rowSums(milieus[,-1])

# keep Tcytotoxics that are part of at least one milieu
milieus <- milieus %>%
  filter(number_of_milieus > 0) %>%
  select(-number_of_milieus) %>%
  reshape2::melt(id.vars = "cellID", variable.name = "milieu", value.name = "is_part") %>%
  filter(is_part > 0) %>%
  select(cellID, milieu)

Marker Profile for different Milieus (for CD8+)

marker_rna <- c("Lag3", "T8_CXCL13", "T5_CCL4")

# rna data 
dat_rna <- data.frame(t(assay(sce_rna[marker_rna, sce_rna$celltype == "Tcytotoxic"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(milieus, dat_rna)

# melt
dat_rna <- dat_rna %>%
  reshape2::melt(id.vars = c("cellID", "milieu"), variable.name = "channel", value.name = "asinh")

# remove CCL4/CCL8/CXCL8 milieus due to too few data points
dat_rna <- dat_rna %>%
  filter(!(milieu %in% c("ccl4_pure", "ccl8_pure", "cxcl8_pure")))

# rename milieus
dat_rna <- dat_rna %>%
  mutate(milieu_short = toupper(str_split(milieu, "_",  n = 2, simplify = TRUE)[,1]))

col_vector_chemokines <- metadata(sce_rna)$colour_vectors$chemokine_single

# add channel medium
dat_rna <- dat_rna %>%
  group_by(channel) %>%
  mutate(channel_median = median(asinh))

# one-sample t test
stat.test <- data.frame()

# loop through all channels (each has a different µ)
for(j in unique(dat_rna$channel)){
  cur.mu <- unique(dat_rna[dat_rna$channel == j, ]$channel_median)
  
  # calculate p-value for different milieus in one channel and adjust pvalue 
  cur.test <- dat_rna[dat_rna$channel == j, ] %>%
    group_by(channel, milieu_short) %>%
    t_test(asinh ~ 1, mu = cur.mu, detailed = TRUE) %>%
    adjust_pvalue()
      
  stat.test <- rbind(stat.test, cur.test)
}

# adjust again for testing across different channels
stat.test <- stat.test %>%
    adjust_pvalue() %>%
    add_significance("p.adj")

# plot
plot_list <- list()
ylim_list <- list("Lag3" = c(0,0.9), "T8_CXCL13" = c(0,2.3), "T5_CCL4" = c(0,1))
for(i in unique(dat_rna$channel)){
  cur.stat.test <- stat.test[stat.test$channel == i, ]
  
  plot_list[[i]] <- ggplot(dat_rna[dat_rna$channel == i,], aes(x=milieu_short, y=asinh, fill=milieu_short)) + 
    geom_boxplot(alpha=.6, lwd=0.5, outlier.shape = NA, position = position_dodge(1.1)) +
    theme_bw() +
    theme(text = element_text(size=18),
          axis.title.x=element_blank(),
          axis.text.x=element_blank(),
          axis.ticks.x=element_blank()) +
    guides(fill=guide_legend("Milieu", override.aes = list(alpha=1)), col="none") +
    stat_pvalue_manual(
      cur.stat.test, x = "milieu_short", y.position = ylim_list[[i]][2]-0.05,
      label = "p.adj.signif",
      position = position_dodge(0.8), 
      size=4) + 
    ylab("Mean Expression (asinh)") + 
    xlab("") + 
    geom_hline(aes(yintercept = channel_median, group = channel), colour = 'black', linetype = 2, size=1) + 
    scale_fill_manual(values = unname(col_vector_chemokines),
                      breaks = names(col_vector_chemokines),
                      labels = names(col_vector_chemokines)) + 
    coord_cartesian(ylim=ylim_list[[i]]) +
    facet_wrap(~channel)
}

leg_c <- cowplot::get_legend(plot_list[[1]])

grid.arrange(plot_list[[1]] + theme(legend.position = "none"),
             plot_list[[2]] + theme(legend.position = "none") + ylab(""),
             plot_list[[3]] + theme(legend.position = "none") + ylab(""),
             ncol=2)

Legend

grid.arrange(leg_c)

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] grid      parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] rstatix_0.6.0               ggpubr_0.4.0               
 [3] viridis_0.5.1               viridisLite_0.3.0          
 [5] cowplot_1.1.1               neighbouRhood_0.4          
 [7] magrittr_2.0.1              dtplyr_1.0.1               
 [9] ggpmisc_0.3.7               gridExtra_2.3              
[11] dittoSeq_1.0.2              scater_1.16.2              
[13] destiny_3.2.0               ggbeeswarm_0.6.0           
[15] RColorBrewer_1.1-2          circlize_0.4.12            
[17] colorRamps_2.3              ComplexHeatmap_2.4.3       
[19] cba_0.2-21                  proxy_0.4-24               
[21] fpc_2.2-9                   data.table_1.13.6          
[23] umap_0.2.7.0                forcats_0.5.0              
[25] stringr_1.4.0               dplyr_1.0.2                
[27] purrr_0.3.4                 readr_1.4.0                
[29] tidyr_1.1.2                 tibble_3.0.4               
[31] ggplot2_3.3.3               tidyverse_1.3.0            
[33] reshape2_1.4.4              SingleCellExperiment_1.12.0
[35] SummarizedExperiment_1.20.0 Biobase_2.50.0             
[37] GenomicRanges_1.42.0        GenomeInfoDb_1.26.2        
[39] IRanges_2.24.1              S4Vectors_0.28.1           
[41] BiocGenerics_0.36.0         MatrixGenerics_1.2.0       
[43] matrixStats_0.57.0          workflowr_1.6.2            

loaded via a namespace (and not attached):
  [1] reticulate_1.18           tidyselect_1.1.0         
  [3] ranger_0.12.1             BiocParallel_1.22.0      
  [5] munsell_0.5.0             units_0.6-7              
  [7] codetools_0.2-18          withr_2.3.0              
  [9] colorspace_2.0-0          knitr_1.30               
 [11] rstudioapi_0.13           robustbase_0.93-7        
 [13] ggsignif_0.6.0            vcd_1.4-8                
 [15] VIM_6.0.0                 TTR_0.24.2               
 [17] labeling_0.4.2            git2r_0.28.0             
 [19] GenomeInfoDbData_1.2.4    farver_2.0.3             
 [21] pheatmap_1.0.12           rprojroot_2.0.2          
 [23] vctrs_0.3.6               generics_0.1.0           
 [25] xfun_0.20                 ggthemes_4.2.0           
 [27] diptest_0.75-7            R6_2.5.0                 
 [29] clue_0.3-58               rsvd_1.0.3               
 [31] RcppEigen_0.3.3.9.1       locfit_1.5-9.4           
 [33] concaveman_1.1.0          flexmix_2.3-17           
 [35] bitops_1.0-6              DelayedArray_0.16.0      
 [37] assertthat_0.2.1          promises_1.1.1           
 [39] scales_1.1.1              nnet_7.3-14              
 [41] beeswarm_0.2.3            gtable_0.3.0             
 [43] rlang_0.4.10              scatterplot3d_0.3-41     
 [45] GlobalOptions_0.1.2       hexbin_1.28.2            
 [47] broom_0.7.3               yaml_2.2.1               
 [49] abind_1.4-5               modelr_0.1.8             
 [51] backports_1.2.1           httpuv_1.5.4             
 [53] tools_4.0.3               ellipsis_0.3.1           
 [55] ggridges_0.5.3            Rcpp_1.0.5               
 [57] plyr_1.8.6                zlibbioc_1.36.0          
 [59] classInt_0.4-3            RCurl_1.98-1.2           
 [61] openssl_1.4.3             GetoptLong_1.0.5         
 [63] zoo_1.8-8                 haven_2.3.1              
 [65] ggrepel_0.9.0             cluster_2.1.0            
 [67] fs_1.5.0                  RSpectra_0.16-0          
 [69] openxlsx_4.2.3            RANN_2.6.1               
 [71] lmtest_0.9-38             reprex_0.3.0             
 [73] pcaMethods_1.80.0         whisker_0.4              
 [75] hms_0.5.3                 evaluate_0.14            
 [77] smoother_1.1              rio_0.5.16               
 [79] mclust_5.4.7              readxl_1.3.1             
 [81] shape_1.4.5               compiler_4.0.3           
 [83] V8_3.4.0                  KernSmooth_2.23-18       
 [85] crayon_1.3.4              htmltools_0.5.0          
 [87] later_1.1.0.1             lubridate_1.7.9.2        
 [89] DBI_1.1.0                 dbplyr_2.0.0             
 [91] MASS_7.3-53               sf_0.9-7                 
 [93] boot_1.3-25               Matrix_1.3-2             
 [95] car_3.0-10                cli_2.2.0                
 [97] pkgconfig_2.0.3           foreign_0.8-81           
 [99] laeken_0.5.1              sp_1.4-5                 
[101] xml2_1.3.2                vipor_0.4.5              
[103] XVector_0.30.0            rvest_0.3.6              
[105] digest_0.6.27             rmarkdown_2.6            
[107] cellranger_1.1.0          edgeR_3.30.3             
[109] DelayedMatrixStats_1.10.1 curl_4.3                 
[111] kernlab_0.9-29            modeltools_0.2-23        
[113] ggplot.multistats_1.0.0   rjson_0.2.20             
[115] lifecycle_0.2.0           jsonlite_1.7.2           
[117] carData_3.0-4             BiocNeighbors_1.6.0      
[119] askpass_1.1               limma_3.44.3             
[121] fansi_0.4.1               pillar_1.4.7             
[123] lattice_0.20-41           httr_1.4.2               
[125] DEoptimR_1.0-8            glue_1.4.2               
[127] xts_0.12.1                zip_2.1.1                
[129] png_0.1-7                 prabclus_2.3-2           
[131] class_7.3-17              stringi_1.5.3            
[133] RcppHNSW_0.3.0            BiocSingular_1.4.0       
[135] irlba_2.3.3               e1071_1.7-4