Last updated: 2021-02-08

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Knit directory: melanoma_publication_old_data/

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    Ignored:    data/layer_1_classification_protein.csv
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File Version Author Date Message
Rmd 20a1458 toobiwankenobi 2021-02-04 adapt figure order
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Rmd 2ac1833 toobiwankenobi 2021-01-08 changes to Figures
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Rmd 1af3353 toobiwankenobi 2020-10-16 add stuff
Rmd a6b51cd toobiwankenobi 2020-10-14 clean scripts, add new subfigures
Rmd 90196fc toobiwankenobi 2020-10-13 Supp Figure 2
Rmd 7affca0 toobiwankenobi 2020-10-13 clean branch and add suppfigure 2

Introduction

This script generates plots for Supplementary Figure 2.

Preparations

Load libraries

sapply(list.files("code/helper_functions", full.names = TRUE), source)
        code/helper_functions/calculateSummary.R
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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(data.table)
library(survival)
library(ggplot2)
library(broom)
library(dplyr)
library(RColorBrewer)
library(ggalluvial)
library(tidyverse)
library(cowplot)
library(ggbeeswarm)
library(gridExtra)
library(SingleCellExperiment)
library(scater)
library(cba)
library(ComplexHeatmap)
library(reshape2)
library(rms)
library(ggrepel)
library(circlize)
library(coxme)
library(rstatix)
library(ggpubr)

Load Data

# clinical data
dat <- read_csv("data/protein/clinical_data_protein.csv")
dat_survival = fread(file = "data/survdat_for_modelling.csv",stringsAsFactors = FALSE)
dat_inflammation = fread(file = "data/manual_infiltration_scoring_BlockID.csv", header = TRUE)

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

targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol

Supp Figure 2B

Clinical features of the cohort

Note: as the cohort is very diverse, we are using the BlockID as the minimal unit since clinical parameters are described per BlockID. However, sometimes we do have patients of which we have multiple FFPE blocks (BlockIDs). Nonetheless, clinical parameters are not given per patient but per patient FFPE block and are therefore considered the minimial unit.

Number of Samples

dat[dat$BlockID %in% unique(sce_prot[,sce_prot$Location == "CTRL"]$BlockID),]$MM_location <- "Control"

# remove control samples
dat <- dat[dat$BlockID %in% unique(sce_prot[,sce_prot$Location != "CTRL"]$BlockID),]

p1 <- unique(dat[,c("BlockID","MM_location")]) %>%
  ggplot()+
  geom_bar(aes(y=MM_location),stat ="count") +
  xlab("BlockIDs per Location") +
  ylab("Metastasis Location") +
  theme_bw()+
  theme(text = element_text(size=16))
  
p2 <- dat %>%
  ggplot()+
  geom_bar(aes(x=BlockID, fill=(MM_location)),stat="count")+
  theme_bw()+
  theme(axis.text.x = element_text(angle = 90,vjust = 0.5)) + 
  ylab("Number of Samples") +
  guides(fill=guide_legend(title="Metasis Location")) +
  theme(text = element_text(size=16),
        axis.text.x = element_text(size=7))

plot_grid(p1,p2,rel_widths = c(1.25,3))  

Supp Figure 2C

Patients per Location

sce_rna$MM_location <- ifelse(sce_rna$MM_location %in% c("skin", "skin_undefine"), "skin_undefined", sce_rna$MM_location)

groups <- data.frame(colData(sce_rna)) %>%
  distinct(ImageNumber, .keep_all = T) %>%
  group_by(MM_location) %>%
  distinct(PatientID, .keep_all = T) %>%
  summarise(n=n()) %>%
  filter(n>=10) %>%
  arrange(-n)

Boxplot/Barplot per Location for every chemokine combination

fractions_per_image <- data.frame(colData(sce_rna)) %>%
  group_by(ImageNumber, MM_location, expressor, celltype) %>%
  summarise(n = n()) %>%
  group_by(ImageNumber) %>%
  mutate(fraction_per_image = n / sum(n)) %>%
  group_by(ImageNumber, expressor) %>%
  mutate(group_fraction = sum(fraction_per_image)) %>%
  ungroup() %>%
  filter(expressor %in% targets & MM_location %in% groups$MM_location)

# fraction of expressor cells per image 
fraction_expressor_per_image <- fractions_per_image %>%
  distinct(ImageNumber, MM_location, expressor, .keep_all = T) %>%
  reshape2::dcast(ImageNumber + MM_location ~ expressor, value.var = "group_fraction", fill = 0) %>%
  reshape2::melt(id.vars = c("ImageNumber", "MM_location"), variable.name = "expressor", 
                 value.name = "fraction_per_image")

# fraction of celltype expressing a certain combi per image
celltype_fractions <- fractions_per_image %>%
  distinct(ImageNumber, celltype, expressor, .keep_all = T) %>%
  reshape2::dcast(ImageNumber + MM_location + expressor ~ celltype, value.var = "fraction_per_image", fill = 0) %>%
  reshape2::melt(id.vars = c("ImageNumber", "MM_location", "expressor"), 
                 variable.name = "celltype", value.name = "fraction_per_image") %>%
  reshape2::dcast(ImageNumber + MM_location + celltype ~ expressor, value.var = "fraction_per_image", fill = 0) %>%
  reshape2::melt(id.vars = c("ImageNumber", "MM_location", "celltype"), 
                 variable.name = "expressor", value.name = "fraction_per_image") %>%
  group_by(MM_location, expressor, celltype) %>%
  summarise(sum_fraction = sum(fraction_per_image)) %>% # sum-up fractions over all images 
  group_by(MM_location, expressor) %>%
  mutate(proportions = sum_fraction / sum(sum_fraction)) # calculate proportions for each expressor

#median_celltype_fraction <- fractions_per_image %>%
  #distinct(ImageNumber, celltype, expressor, .keep_all = T) %>%
  #reshape2::dcast(ImageNumber + MM_location + expressor ~ celltype, value.var = "fraction_per_image", fill = 0) %>%
  #reshape2::melt(id.vars = c("ImageNumber", "MM_location", "expressor"), 
                 #variable.name = "celltype", value.name = "fraction_per_image") %>%
  #reshape2::dcast(ImageNumber + MM_location + celltype ~ expressor, value.var = "fraction_per_image", fill = 0) %>%
  #reshape2::melt(id.vars = c("ImageNumber", "MM_location", "celltype"), 
                 #variable.name = "expressor", value.name = "fraction_per_image") %>%
  #group_by(MM_location, expressor, celltype) %>%
  #summarise(median_fraction = median(fraction_per_image)) %>%
  #group_by(MM_location, expressor) %>%
  #mutate(proportions = median_fraction / sum(median_fraction))

Plot

plot_list <- list()
for(i in groups$MM_location) {
  a <- fraction_expressor_per_image %>%
    filter(MM_location == i) %>%  
    group_by(ImageNumber, expressor) %>%
    ggplot(., aes(y=expressor, x=fraction_per_image)) + 
    geom_boxplot() + 
    geom_point(alpha=0.2) +
    theme_bw() +
    theme(axis.title.y = element_blank(),
          axis.text.y = element_text(hjust=0.5)) +
    #scale_x_log10() +
    #annotation_logticks(sides = "bottom") + 
    xlab("Cell Fraction per Image") + 
    coord_cartesian(xlim = c(0,0.05))
  
  b <- celltype_fractions %>%
    filter(MM_location == i) %>%  
    ggplot(., aes(y=expressor, x=-proportions, fill=celltype)) + 
    geom_bar(stat = "identity") +
    theme_bw() +
    theme(axis.text.y = element_blank(),
          axis.title.y = element_blank(),
          legend.position = "none") +
    xlab("Producing Cell Types") +
          scale_fill_manual(values = unname(metadata(sce_rna)$colour_vectors$celltype),
                        breaks = names(metadata(sce_rna)$colour_vectors$celltype),
                        labels = names(metadata(sce_rna)$colour_vectors$celltype)) +
    scale_x_continuous(breaks=c(-1.00,-0.75,-0.5, -0.25, 0.00),
                     labels=c("100%", "75%", "50%", "25%", "0%"))
  
  grid.arrange(b,a,nrow=1,
               widths = c(.75,1),
               top = i)
}

Supp Figure 2D

Expressor in MM_location

# add control location to sce
sce_rna$MM_location_simplified2 <- sce_rna$MM_location_simplified
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "Skin"]$MM_location_simplified2 <- "control skin"
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "Lymphnode"]$MM_location_simplified2 <- "control LN"
sce_rna[,sce_rna$Location == "CTRL" & sce_rna$TissueType == "PSO"]$MM_location_simplified2 <- "control psoriasis"

frac <- data.frame(colData(sce_rna)) %>%
  filter(MM_location_simplified2 != "control psoriasis") %>%
  group_by(Description, MM_location_simplified2, expressor) %>%
  summarise(n=n()) %>%
  mutate(fraction = n / sum(n)) %>%
  filter(expressor %in% targets) %>%
  reshape2::dcast(Description + MM_location_simplified2 ~ expressor, value.var = "fraction", fill = 0) %>%
  reshape2::melt(id.vars = c("Description", "MM_location_simplified2"), variable.name = "expressor", value.name = "fraction")

ggplot(frac, aes(x=expressor, y = fraction, fill = MM_location_simplified2)) + 
  geom_boxplot(alpha=1, outlier.size = 0.5) +
  #geom_jitter(size = 0.75, alpha=0.6, position = position_jitterdodge(dodge.width = 0.75,jitter.width = 0.05), aes(col=MM_location_simplified2)) + 
  scale_color_discrete(guide=FALSE) +
  theme_bw() +
  theme(text = element_text(size = 15),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) + 
  guides(fill=guide_legend(title="Met Location", override.aes = aes(lwd=0.5))) +
  xlab("") + 
  ylab("Fractions") +
  coord_cartesian(ylim = c(0,0.075))

Supp Figure 2E

Mutation

targets <- metadata(sce_rna)$chemokines_morethan600_withcontrol

# subset sce_rna
group_size <- data.frame(colData(sce_rna)) %>%
  group_by(ImageNumber, Mutation) %>%
  distinct(ImageNumber, .keep_all = T) %>%
  group_by(Mutation) %>%
  summarise(n=n()) %>%
  filter(n>10 & Mutation != "")

sce_rna_sub <- sce_rna[,sce_rna$Mutation %in% group_size$Mutation]

a <- plotCellCounts(sce = sce_rna_sub, 
                    sce_sub = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets)],
                    cellID = "cellID",
                    colour_by = "expressor",
                    split_by = "Mutation",
                    imageID = "ImageNumber",
                    normalize = TRUE,
                    show_n = FALSE,
                    colour_vector = metadata(sce_rna)$colour_vectors$chemokine_combinations) + 
  guides(fill=guide_legend("Chemokine")) +
  theme(text = element_text(size=16))

b <- plotCellCounts(sce = sce_rna_sub[,which(sce_rna_sub$expressor %in% targets)],
                    cellID = "cellID",
                    colour_by = "celltype",
                    split_by = "Mutation",
                    imageID = "ImageNumber",
                    proportion = TRUE,
                    show_n = FALSE,
                    colour_vector = metadata(sce_rna)$colour_vectors$celltype) + 
  guides(fill=guide_legend("Cell Type")) +
  theme(text = element_text(size=16))

# fraction of chemokine-expressing cells per image
t <- data.frame(colData(sce_rna_sub)) %>%
  group_by(ImageNumber, Mutation, chemokine, MM_location_simplified) %>%
  summarise(n=n()) %>%
  group_by(ImageNumber) %>%
  mutate(fraction = n / sum(n)) %>%
  reshape2::dcast(ImageNumber + Mutation + MM_location_simplified ~ chemokine, value.var = "fraction")

median_expression <- median(t$`TRUE`)

stat.test <- t %>%
  group_by(Mutation) %>%
  t_test(data = ., mu = median_expression, `TRUE` ~ 1, alternative = "greater") %>%
  adjust_pvalue(method="BH") %>%
  add_significance() %>%
  add_x_position(x = "Mutation", dodge = 0.8)

c <- ggplot(t, aes(x=Mutation, y=`TRUE`)) + 
  geom_boxplot(alpha=0.2, lwd=1.5) + 
  geom_quasirandom(aes(col=MM_location_simplified), size=2, alpha=.8) +
  geom_hline(yintercept = median_expression, linetype=2, size=2) + 
  stat_pvalue_manual(stat.test, x = "x", label = "p.adj.signif", size = 7, y.position = 0.3) + 
  xlab("") + 
  ylab("Fraction of Chemokine-Expressing Cells") +
  theme_bw() +
  theme(text = element_text(size=16),
        axis.text.x = element_text(angle=45, hjust=1, vjust=1)) +
  guides(col=guide_legend("Location")) +
  coord_cartesian(ylim=c(0,0.3))

grid.arrange(a,b,c,nrow=1,
             widths = c(1,1,1))
# MM_location for Mutations
data.frame(colData(sce_rna_sub)) %>%
  distinct(ImageNumber, .keep_all = T) %>%
  group_by(Mutation, MM_location_simplified) %>%
  summarise(n=n()) %>%
  group_by(Mutation) %>%
  mutate(percentage = n / sum(n) * 100)
# A tibble: 9 x 4
# Groups:   Mutation [3]
  Mutation MM_location_simplified     n percentage
  <chr>    <chr>                  <int>      <dbl>
1 BRAF     LN                        30       42.3
2 BRAF     other                     10       14.1
3 BRAF     skin                      31       43.7
4 NRAS     LN                        16       32  
5 NRAS     other                     10       20  
6 NRAS     skin                      24       48  
7 wt       LN                         6       21.4
8 wt       other                      5       17.9
9 wt       skin                      17       60.7

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] ggpubr_0.4.0                rstatix_0.6.0              
 [3] coxme_2.2-16                bdsmatrix_1.3-4            
 [5] circlize_0.4.12             ggrepel_0.9.0              
 [7] rms_6.1-0                   SparseM_1.78               
 [9] Hmisc_4.4-2                 Formula_1.2-4              
[11] lattice_0.20-41             reshape2_1.4.4             
[13] ComplexHeatmap_2.4.3        cba_0.2-21                 
[15] proxy_0.4-24                scater_1.16.2              
[17] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[19] Biobase_2.50.0              GenomicRanges_1.42.0       
[21] GenomeInfoDb_1.26.2         IRanges_2.24.1             
[23] S4Vectors_0.28.1            BiocGenerics_0.36.0        
[25] MatrixGenerics_1.2.0        matrixStats_0.57.0         
[27] gridExtra_2.3               ggbeeswarm_0.6.0           
[29] cowplot_1.1.1               forcats_0.5.0              
[31] stringr_1.4.0               purrr_0.3.4                
[33] readr_1.4.0                 tidyr_1.1.2                
[35] tibble_3.0.4                tidyverse_1.3.0            
[37] ggalluvial_0.12.3           RColorBrewer_1.1-2         
[39] dplyr_1.0.2                 broom_0.7.3                
[41] ggplot2_3.3.3               survival_3.2-7             
[43] data.table_1.13.6           workflowr_1.6.2            

loaded via a namespace (and not attached):
  [1] readxl_1.3.1              backports_1.2.1          
  [3] plyr_1.8.6                splines_4.0.3            
  [5] BiocParallel_1.22.0       TH.data_1.0-10           
  [7] digest_0.6.27             htmltools_0.5.0          
  [9] viridis_0.5.1             fansi_0.4.1              
 [11] magrittr_2.0.1            checkmate_2.0.0          
 [13] cluster_2.1.0             openxlsx_4.2.3           
 [15] modelr_0.1.8              sandwich_3.0-0           
 [17] jpeg_0.1-8.1              colorspace_2.0-0         
 [19] rvest_0.3.6               haven_2.3.1              
 [21] xfun_0.20                 crayon_1.3.4             
 [23] RCurl_1.98-1.2            jsonlite_1.7.2           
 [25] zoo_1.8-8                 glue_1.4.2               
 [27] gtable_0.3.0              zlibbioc_1.36.0          
 [29] XVector_0.30.0            MatrixModels_0.4-1       
 [31] GetoptLong_1.0.5          DelayedArray_0.16.0      
 [33] car_3.0-10                BiocSingular_1.4.0       
 [35] shape_1.4.5               abind_1.4-5              
 [37] scales_1.1.1              mvtnorm_1.1-1            
 [39] DBI_1.1.0                 Rcpp_1.0.5               
 [41] viridisLite_0.3.0         htmlTable_2.1.0          
 [43] clue_0.3-58               foreign_0.8-81           
 [45] rsvd_1.0.3                htmlwidgets_1.5.3        
 [47] httr_1.4.2                ellipsis_0.3.1           
 [49] farver_2.0.3              pkgconfig_2.0.3          
 [51] nnet_7.3-14               dbplyr_2.0.0             
 [53] utf8_1.1.4                labeling_0.4.2           
 [55] tidyselect_1.1.0          rlang_0.4.10             
 [57] later_1.1.0.1             munsell_0.5.0            
 [59] cellranger_1.1.0          tools_4.0.3              
 [61] cli_2.2.0                 generics_0.1.0           
 [63] evaluate_0.14             yaml_2.2.1               
 [65] knitr_1.30                fs_1.5.0                 
 [67] zip_2.1.1                 nlme_3.1-151             
 [69] whisker_0.4               quantreg_5.82            
 [71] xml2_1.3.2                compiler_4.0.3           
 [73] rstudioapi_0.13           curl_4.3                 
 [75] beeswarm_0.2.3            png_0.1-7                
 [77] ggsignif_0.6.0            reprex_0.3.0             
 [79] stringi_1.5.3             Matrix_1.3-2             
 [81] vctrs_0.3.6               pillar_1.4.7             
 [83] lifecycle_0.2.0           GlobalOptions_0.1.2      
 [85] BiocNeighbors_1.6.0       bitops_1.0-6             
 [87] irlba_2.3.3               conquer_1.0.2            
 [89] httpuv_1.5.4              R6_2.5.0                 
 [91] latticeExtra_0.6-29       promises_1.1.1           
 [93] rio_0.5.16                vipor_0.4.5              
 [95] codetools_0.2-18          polspline_1.1.19         
 [97] MASS_7.3-53               assertthat_0.2.1         
 [99] rprojroot_2.0.2           rjson_0.2.20             
[101] withr_2.3.0               multcomp_1.4-15          
[103] GenomeInfoDbData_1.2.4    hms_0.5.3                
[105] rpart_4.1-15              rmarkdown_2.6            
[107] DelayedMatrixStats_1.10.1 carData_3.0-4            
[109] git2r_0.28.0              lubridate_1.7.9.2        
[111] base64enc_0.1-3