Last updated: 2022-02-10

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Knit directory: MelanomaIMC/

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File Version Author Date Message
Rmd f9a3a83 toobiwankenobi 2022-02-08 clean repo for release
Rmd 588dbb1 toobiwankenobi 2022-02-06 Figure Order
Rmd 3da15db toobiwankenobi 2021-11-24 changes for revision
Rmd 434eee4 toobiwankenobi 2021-09-23 Figure adaptions and new Supp Figure with gates
Rmd c4e2793 toobiwankenobi 2021-08-04 rearrange figure order to match pre-print
html 4109ff1 toobiwankenobi 2021-07-07 delete html files and adapt gitignore
Rmd 4affda4 toobiwankenobi 2021-04-14 figure adaptations
html 4affda4 toobiwankenobi 2021-04-14 figure adaptations
Rmd 3203891 toobiwankenobi 2021-02-19 change celltype names
html 3203891 toobiwankenobi 2021-02-19 change celltype names
Rmd ee1595d toobiwankenobi 2021-02-12 clean repo and adapt files
html ee1595d toobiwankenobi 2021-02-12 clean repo and adapt files
html 3f5af3f toobiwankenobi 2021-02-09 add .html files
Rmd afa7957 toobiwankenobi 2021-02-08 minor changes on figures and figure order
Rmd 20a1458 toobiwankenobi 2021-02-04 adapt figure order
Rmd f9bb33a toobiwankenobi 2021-02-04 new Figure 5 and minor changes in figure order
Rmd 2ac1833 toobiwankenobi 2021-01-08 changes to Figures
Rmd 9442cb9 toobiwankenobi 2020-12-22 add all new files
Rmd 1af3353 toobiwankenobi 2020-10-16 add stuff
Rmd a6b51cd toobiwankenobi 2020-10-14 clean scripts, add new subfigures
Rmd d8819f2 toobiwankenobi 2020-10-08 read new data (nuclei expansion) and adapt scripts
Rmd a21c858 toobiwankenobi 2020-08-06 adapt pipeline
Rmd 2c11d5c toobiwankenobi 2020-08-05 add new scripts

Introduction

This script generates plots for Figure 2. Panel A was created manually.

Preparation

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

Load libraries

sapply(list.files("code/helper_functions", full.names = TRUE), source)
        code/helper_functions/calculateSummary.R
value   ?                                       
visible FALSE                                   
        code/helper_functions/censor_dat.R
value   ?                                 
visible FALSE                             
        code/helper_functions/detect_mRNA_expression.R
value   ?                                             
visible FALSE                                         
        code/helper_functions/DistanceToClusterCenter.R
value   ?                                              
visible FALSE                                          
        code/helper_functions/findMilieu.R code/helper_functions/findPatch.R
value   ?                                  ?                                
visible FALSE                              FALSE                            
        code/helper_functions/getInfoFromString.R
value   ?                                        
visible FALSE                                    
        code/helper_functions/getSpotnumber.R
value   ?                                    
visible FALSE                                
        code/helper_functions/plotCellCounts.R
value   ?                                     
visible FALSE                                 
        code/helper_functions/plotCellFractions.R
value   ?                                        
visible FALSE                                    
        code/helper_functions/plotDist.R code/helper_functions/read_Data.R
value   ?                                ?                                
visible FALSE                            FALSE                            
        code/helper_functions/scatter_function.R
value   ?                                       
visible FALSE                                   
        code/helper_functions/sceChecks.R
value   ?                                
visible FALSE                            
        code/helper_functions/validityChecks.R
value   ?                                     
visible FALSE                                 
library(SingleCellExperiment)
library(ComplexHeatmap)
library(data.table)
library(dplyr)
library(tidyr)
library(ggpmisc)
library(cowplot)
library(corrplot)
library(gridExtra)
library(ggbeeswarm)
library(ggpubr)
library(RColorBrewer)
library(colorRamps)
library(circlize)
library(forcats)
library(viridis)
library(psych)
library(ggpmisc)

Load data

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

sce_rna <- sce_rna[,sce_rna$Location != "CTRL"]
sce_prot <- sce_prot[,sce_prot$Location != "CTRL"]

Figure 2B

UpSet plot with ComplexHeatmap - including CellType Annotation and Met Location

cur_dt <- as.data.table(colData(sce_rna))

# combinations with more than 600 occurrences
targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol
targets_no_control <- replace(targets, match(c("CXCL8", "CCL18"),targets), c("CCL18", "CXCL8"))

# remove control samples
cur_dt <- cur_dt[MM_location_simplified != "control",]

# extract chemokine columns
chemokines <- cbind(cur_dt[,c("ImageNumber", "MM_location_simplified", "celltype")], cur_dt[,grepl(glob2rx("C*L*"),names(cur_dt)), with = F])

# create combination matrix
m <- make_comb_mat(chemokines, top_n_sets = 11)

# filter based on combination size and combination degree
m <- m[comb_size(m) >= 600 & comb_degree(m) > 0]

# sort according to abundance
m <- m[order(-comb_size(m))]

# extract comb names
comb_names <- comb_name(m)

# count celltypes for each combination
celltypes <- data.table()
location <- data.table()

# summarize statistics for each combination (celltype fractions, location)
for (i in comb_names){
  # subset
  set <- chemokines[extract_comb(m, i)]
  
  # chemokines celltypes
  set1 <- set %>%
    group_by(celltype) %>%
    summarise(n=n()) %>%
    reshape2::dcast(.,i ~ celltype, value.var = "n")
  
  # chemokines by location
  set2 <- set %>%
    group_by(ImageNumber, MM_location_simplified) %>%
    summarise(n=n())
  
  # add images with no combinations to not distort median
  set2_add <- distinct(cur_dt[,c("ImageNumber", "MM_location_simplified")], ImageNumber, .keep_all = T)
  set2_add$n <- 0
  
  # subset to only contain images which are not already part of set2
  set2_add <- set2_add[!(ImageNumber %in% set2$ImageNumber),]
  
  set2 <- set2 %>%
    rbind(., set2_add) %>%
    group_by(MM_location_simplified) %>%
    mutate(median = median(n)) %>%
    distinct(MM_location_simplified, median) %>%
    reshape2::dcast(.,i ~ MM_location_simplified, value.var = "median")
  
  # add to data.frame
  celltypes <- rbind(celltypes, set1, fill = TRUE)
  location <- rbind(location, set2, fill = TRUE)
}

# replace NA
celltypes[is.na(celltypes)] <- 0
location[is.na(location)] <- 0

# properties of combination matrix
ss = set_size(m)
cs = comb_size(m)

# calculate fraction of chemokine combinations per patient
fraction_patient <- cur_dt %>%
  select(PatientID, expressor) %>%
  filter(expressor != "NA") %>%
  group_by(PatientID, expressor) %>%
  summarise(n=n()) %>%
  group_by(expressor) %>%
  mutate(fraction = n / sum(n)) %>%
  reshape2::dcast(expressor ~ PatientID, value.var = "fraction", fill = 0) %>%
  filter(expressor %in% targets) %>%
  reshape2::melt(id.vars=c("expressor"), variable.name="PatientID", value.name="fraction")

# how many chemokine combis are found in top10 patients?
fraction_patient <- fraction_patient %>%
  group_by(expressor) %>%
  slice_max(fraction, n=10) %>%
  summarise(top10=round(sum(fraction)*100,0))

# change row-order to match targets and remove expressor column (only keep fractions)
fraction_patient <- fraction_patient[match(targets,fraction_patient$expressor),-1]

# max fraction coming from one image per expressor
fraction_patient$top10 <- paste0(fraction_patient$top10, "%")

# create plot
ht = UpSet(m, 
           set_order = order(ss),
           comb_order = order(cs, decreasing = T),
           top_annotation = HeatmapAnnotation(
             "Number of\nExpressing Cells" = anno_barplot(celltypes[,-1], 
                                                          ylim = c(0, max(cs)*1.1),
                                                          border = FALSE, 
                                                          gp = gpar(fill = metadata(sce_rna)$colour_vectors$celltype[colnames(celltypes[,-1])]), 
                                                          axis_param = list(gp = gpar(fontsize=14)),
                                                          height = unit(7.5, "cm")), 
             annotation_name_side = "left", 
             annotation_name_rot = 0,
             annotation_name_gp = gpar(fontsize=14)),
           left_annotation = rowAnnotation(
             "Total Number of\nExpressing Cells" = anno_barplot(-ss, 
                                                                baseline = 0,
                                                                axis_param = list(
                                                                  at = c(0, -5000, -10000, -15000),
                                                                  labels = c(0, 5000, 10000, 15000),
                                                                  labels_rot = 0,
                                                                  gp = gpar(fontsize = 12)),
                                                                border = FALSE, 
                                                                gp = gpar(fill = "black"), 
                                                                width = unit(3, "cm"),
             ),
             set_name = anno_text(set_name(m), 
                                  location = 0.5, 
                                  gp = gpar(fontsize=14),
                                  just = "center",
                                  width = max_text_width(set_name(m)) + unit(4, "mm")),
             annotation_name_gp = gpar(fontsize=20)),
           right_annotation = NULL,
           show_row_names = FALSE,
           pt_size = unit(3, "mm"),
           lwd = 2,
           width = unit(8, "cm"),
           height = unit(8, "cm"),
           bottom_annotation = HeatmapAnnotation(Top10 = anno_text(t(fraction_patient),
                                                                 just = "center", location = 0.5,
                                                                 rot = 90,
                                                                 gp = gpar(fontsize=13))),
)

# draw heatmap
ht = draw(ht)

# add absolute numbers on top of barplot
od = column_order(ht)
row_od = row_order(ht)
decorate_annotation("Number of\nExpressing Cells", {
  grid.text(cs[od], x = seq_along(cs), y = unit(cs[od], "native") + unit(2, "pt"), 
            default.units = "native", just = c("left", "bottom"), 
            gp = gpar(fontsize = 13, col = "#404040"), rot = 45)
})
decorate_annotation("Total Number of\nExpressing Cells", {
  grid.text(ss[row_od], 
            x = unit(-ss[row_od], "native") + unit(-0.75, "cm"), 
            y = rev(seq_len(length(-ss))), 
            default.units = "native", rot = 0,
            gp = gpar(fontsize = 11))
})

Legend for Figure

# legend for celltypes
lgd1 = Legend(labels = colnames(celltypes[,-1]), 
              title = "Celltypes", 
              legend_gp = gpar(fill = metadata(sce_rna)$colour_vectors$celltype[colnames(celltypes[,-1])],
                               fontsize = 18),
              nrow = 5)

draw(packLegend(lgd1,column_gap = unit(0.5, "cm"),
                max_height = unit(7, "cm")))

Figure 2C

Heatmap Marker Expression

# add marker expression to cells
marker_expression <- data.frame(t(assay(sce_rna[rowData(sce_rna)$good_marker,], "asinh")))
marker_expression$cellID <- rownames(marker_expression)

# chemokine info
chemo <- data.frame(colData(sce_rna))[,c("cellID", "expressor", "celltype")] 

dat <- left_join(chemo, marker_expression, by = "cellID")
dat$cellID <- NULL

# aggregate data
dat_aggr <- dat %>%
  filter(expressor %in% colnames(colData(sce_rna))[grepl("CXCL|CCL", colnames(colData(sce_rna)))]) %>%
  group_by(celltype, expressor) %>%
  summarise_all(funs(mean))
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
# prepare matrix for heatmap
dat_aggr <- dat_aggr %>%
  arrange(celltype, expressor)

stats <- dat %>%
  filter(expressor %in% colnames(colData(sce_rna))[grepl("CXCL|CCL", colnames(colData(sce_rna)))]) %>%
  group_by(celltype, expressor) %>%
  summarise(n=n()) %>%
  filter(n>1000) %>%
  arrange(celltype, expressor)

dat_aggr <- dat_aggr %>%
  filter(paste0(celltype,expressor) %in% paste0(stats$celltype,stats$expressor))

# factorize expressor for column sorting in heatmap
dat_aggr$expressor <- factor(dat_aggr$expressor, levels = c("CCL4", "CCL18", "CCL22", "CXCL8", 
                                                            "CCL8", "CXCL9", "CXCL10", "CXCL13", "CCL2", "CXCL12", "CCL19"))
stats$expressor <- factor(stats$expressor, levels = c("CCL4", "CCL18", "CCL22", "CXCL8", 
                                                      "CCL8", "CXCL9", "CXCL10", "CXCL13", "CCL2", "CXCL12", "CCL19"))

dat_aggr <- dat_aggr %>%
  arrange(celltype, expressor)

stats <- stats %>%
  arrange(celltype, expressor)

# create and scale scale matrix
m <- as.matrix(t(dat_aggr[,-c(1:2)]))
m <- t(scale(t(m)))
colnames(m) <- dat_aggr$celltype

# create top annotations
ha <- HeatmapAnnotation("Chemokine" = dat_aggr$expressor,
                        "Cells" = anno_barplot(stats[,3],
                                               height = unit(1.5,"cm"),
                                               axis_param = list(gp = gpar(fontsize=14))),
                        "Cell Numbers" = anno_text(t(stats[,3]), 
                                                   which = "column", 
                                                   rot = 90, 
                                                   just = "center", 
                                                   location = 0.5,
                                                   gp = gpar(fontsize=14)),
                        col = list("Chemokine" = metadata(sce_rna)$colour_vectors$chemokine_single),
                        show_legend = FALSE,
                        annotation_name_gp = gpar(fontsize = 16))


# row_split for markers
rowData(sce_rna)$heatmap_relevance <- ""
rowData(sce_rna[rowData(sce_rna)$good_marker,])$heatmap_relevance <- "Lineage"
rowData(sce_rna[grepl("CXCL|CCL|DapB", rownames(sce_rna)),])$heatmap_relevance <- "Chemokine"
rowData(sce_rna[grepl("B2M|GLUT1|CD134|Lag3|CD163|cleavedPARP|pRB", rownames(sce_rna)),])$heatmap_relevance <- "Other"

# create heatmap
h <- Heatmap(m, name = "Scaled\nExpression",
             row_split = rowData(sce_rna[rowData(sce_rna)$good_marker,])$heatmap_relevance,
             cluster_columns = FALSE,
             show_column_names = FALSE,
             top_annotation = ha,
             show_heatmap_legend = FALSE,
             column_split = colnames(m),
             column_title_rot = 90,
             cluster_column_slices = TRUE,
             row_names_gp = gpar(fontsize = 13),
             column_title_gp = gpar(fontsize = 16),
             row_title_gp = gpar(fontsize = 16),
             col = colorRamp2(c(-3, 0, 3), c("blue", "white", "red")),
             height = unit(15, "cm"),
             width = unit(11,"cm"))

# draw heatmap
draw(h)

Legend heatmap

lgd1 = color_mapping_legend(h@matrix_color_mapping, plot = FALSE, legend_direction = "horizontal", legend_width=unit(3,"cm"), at = c(-3:3))
lgd2 = color_mapping_legend(ha@anno_list$Chemokine@color_mapping, plot = FALSE, legend_direction = "horizontal", nrow = 4)

lgd_list = packLegend(lgd1,lgd2,direction = "horizontal", gap = unit(1,"cm"))
draw(lgd_list)

Figure 2D

Correlation of Chemokines with Celltypes (based on fractions)

# define chemokines
targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol

# top abundant chemokines
cur_rna <- data.frame(colData(sce_rna))

# protein data
cur_prot <- data.frame(colData(sce_prot))

# sum
rna_sum <- cur_rna %>%
  group_by(Description) %>%
  mutate(total_cells=n()) %>%
  ungroup() %>%
  group_by(Description, total_cells, expressor) %>%
  summarise(n=n()) %>%
  mutate(fraction=n/total_cells) %>%
  reshape2::dcast(Description ~ expressor, value.var = "fraction", fill = 0) 

# only keep highly abundant chemokines
rna_sum <- rna_sum[,c("Description", targets)]

prot_sum <- cur_prot %>%
  group_by(Description, celltype) %>%
  summarise(n = n()) %>%
  group_by(Description) %>%
  mutate(fraction = n/sum(n)) %>%
  reshape2::dcast(Description ~ celltype, value.var = "fraction", fill = 0)

# equal images
all(rna_sum$Description == prot_sum$Description)
[1] TRUE
# correlation
cor <- psych::corr.test(rna_sum[,-1], prot_sum[,-1], method = "pearson",adjust = "BH")

cur_dat <- as.data.frame(cor$r)
cur_dat$variable <- rownames(cur_dat)
dat_long <- reshape2::melt(cur_dat,id.vars="variable")
colnames(dat_long) <- c("chemokines","celltypes","correlation")

p_dat <- as_tibble(cor$p)
p_dat$variable <- rownames(cur_dat)
pdat_long <- reshape2::melt(p_dat,id.vars="variable")
colnames(pdat_long) <- c("chemokines","celltypes","p_adj")

dat_long$p_adj <- pdat_long$p_adj

dat_long <- dat_long %>%
  mutate(sig = ifelse(p_adj <= 0.001 & p_adj > 0.0001,0.001,p_adj))

dat_long <- dat_long %>%
  mutate(sig = case_when(p_adj <= 0.0001 ~ "< 0.0001",
                         p_adj <= 0.001 & p_adj > 0.0001 ~ "< 0.001",
                         p_adj <= 0.01 & p_adj > 0.001 ~ "< 0.01",
                         p_adj <= 0.1 & p_adj > 0.01 ~ "< 0.1",
                         p_adj >0.1 ~ "ns"))

# plot
a <- ggplot()+
  geom_tile(data = dat_long, aes(x = chemokines,y = celltypes,fill=sig),color = "gray",size = 0.1, alpha = 0.5)+
  scale_fill_manual(values = c("< 0.0001" = "darkgreen", "< 0.001" = "green3", "< 0.01"="green", "< 0.1" = "lightgray","ns" = "white"), name = "adj p-value" )+
  theme_classic()+
  theme(axis.text.x = element_text(angle = 90,hjust = 1,vjust = 0.5),
        text = element_text(size=18)) +
  geom_point(data = dat_long, aes(x=chemokines,y = celltypes),size=5.5, show.legend = FALSE) +
  geom_point(data = dat_long, aes(x=chemokines,y = celltypes, color=correlation),size= 5, shape=19) +
  scale_color_gradient2(low="blue",mid= "white", high="red", name = "Pearson correlation") + 
  xlab("Chemokines") +
  ylab("Cell Types")

a + theme(legend.position="none")

Legend

legend <- get_legend(a)
as_ggplot(legend)

sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 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=en_US.UTF-8   
 [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      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] psych_2.1.9                 viridis_0.6.2              
 [3] viridisLite_0.4.0           forcats_0.5.1              
 [5] circlize_0.4.13             colorRamps_2.3             
 [7] RColorBrewer_1.1-2          ggpubr_0.4.0               
 [9] ggbeeswarm_0.6.0            gridExtra_2.3              
[11] corrplot_0.92               cowplot_1.1.1              
[13] ggpmisc_0.4.5               ggpp_0.4.3                 
[15] ggplot2_3.3.5               tidyr_1.2.0                
[17] data.table_1.14.2           ComplexHeatmap_2.10.0      
[19] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[21] Biobase_2.54.0              GenomicRanges_1.46.1       
[23] GenomeInfoDb_1.30.1         IRanges_2.28.0             
[25] S4Vectors_0.32.3            BiocGenerics_0.40.0        
[27] MatrixGenerics_1.6.0        matrixStats_0.61.0         
[29] dplyr_1.0.7                 workflowr_1.7.0            

loaded via a namespace (and not attached):
 [1] colorspace_2.0-2       ggsignif_0.6.3         rjson_0.2.21          
 [4] ellipsis_0.3.2         rprojroot_2.0.2        XVector_0.34.0        
 [7] GlobalOptions_0.1.2    fs_1.5.2               clue_0.3-60           
[10] rstudioapi_0.13        farver_2.1.0           MatrixModels_0.5-0    
[13] fansi_1.0.2            codetools_0.2-18       mnormt_2.0.2          
[16] doParallel_1.0.16      knitr_1.37             jsonlite_1.7.3        
[19] broom_0.7.12           cluster_2.1.2          png_0.1-7             
[22] compiler_4.1.2         httr_1.4.2             backports_1.4.1       
[25] assertthat_0.2.1       Matrix_1.4-0           fastmap_1.1.0         
[28] cli_3.1.1              later_1.3.0            htmltools_0.5.2       
[31] quantreg_5.87          tools_4.1.2            gtable_0.3.0          
[34] glue_1.6.1             GenomeInfoDbData_1.2.7 reshape2_1.4.4        
[37] Rcpp_1.0.8             carData_3.0-5          jquerylib_0.1.4       
[40] vctrs_0.3.8            nlme_3.1-155           iterators_1.0.13      
[43] xfun_0.29              stringr_1.4.0          ps_1.6.0              
[46] lifecycle_1.0.1        rstatix_0.7.0          getPass_0.2-2         
[49] zlibbioc_1.40.0        scales_1.1.1           promises_1.2.0.1      
[52] parallel_4.1.2         SparseM_1.81           yaml_2.2.2            
[55] sass_0.4.0             stringi_1.7.6          highr_0.9             
[58] foreach_1.5.2          shape_1.4.6            rlang_1.0.0           
[61] pkgconfig_2.0.3        bitops_1.0-7           evaluate_0.14         
[64] lattice_0.20-45        purrr_0.3.4            labeling_0.4.2        
[67] processx_3.5.2         tidyselect_1.1.1       plyr_1.8.6            
[70] magrittr_2.0.2         R6_2.5.1               magick_2.7.3          
[73] generics_0.1.2         DelayedArray_0.20.0    DBI_1.1.2             
[76] pillar_1.7.0           whisker_0.4            withr_2.4.3           
[79] abind_1.4-5            RCurl_1.98-1.5         tibble_3.1.6          
[82] crayon_1.4.2           car_3.0-12             utf8_1.2.2            
[85] tmvnsim_1.0-2          rmarkdown_2.11         GetoptLong_1.0.5      
[88] callr_3.7.0            git2r_0.29.0           digest_0.6.29         
[91] httpuv_1.6.5           munsell_0.5.0          beeswarm_0.4.0        
[94] vipor_0.4.5            bslib_0.3.1