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CyTOF_TMA_analysis.Rmd
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---
title: "CyTOF_TMA_analysis"
author: "Z.Loncova"
params:
datadir: "./imaging/Single_cell_data_markers" # step 2 in the manuscript: define the data (input) and output folders
resultsdir: "./results/figures"
---
Composition of immune cells
```{r setup, include=FALSE}
library(rlang) # step 3 in the manuscript: load the necessary R libraries
library(dplyr)
library(reshape)
library(ggplot2)
library(cowplot)
if (!dir.exists(params$resultsdir)){ dir.create(params$resultsdir, recursive = T) } # step 4 in the manuscript: check if the folder exists and create it if not
name.list.samples <- c( # step 5 in the manuscript: create the name list of all regions of interest
"17_10053_ROI001",
"17_10053_ROI002",
"17_10053_ROI003",
"17_10053_ROI004",
"17_10053_ROI005",
"17_10053_ROI006",
"17_10053_ROI007",
"17_10053_ROI008",
"17_8146_ROI003",
"17_8146_ROI004",
"17_8146_ROI005",
"17_8146_ROI006",
"17_8146_ROI007",
"17_8146_ROI008",
"17_8146_ROI009",
"17_8146_ROI010",
"18_02076_ROI001",
"18_02076_ROI002",
"18_02076_ROI003",
"18_02076_ROI004",
"18_02076_ROI005",
"18_02076_ROI006",
"18_02076_ROI007",
"18_02076_ROI008",
"18_12804_ROI002",
"18_12804_ROI003",
"18_12804_ROI005",
"18_12804_ROI006",
"18_12804_ROI007",
"18_12804_ROI008",
"18_12804_ROI010",
"18_12816_ROI001",
"18_12816_ROI002",
"18_12816_ROI003",
"18_12816_ROI004",
"18_12816_ROI005",
"18_12816_ROI006",
"18_12816_ROI007",
"18_12816_ROI008",
"ROI001_17_9065_TB1",
"ROI002_17_9065_TB2",
"ROI003_17_9065_TB3",
"ROI004_17_9065_IM1",
"ROI005_17_9065_IM2"
)
pheno <- NULL # step 6 in the manuscript: search for unique category names in all .csv files
for(j in 1:length(name.list.samples)){
tab01 <- read.csv(paste0(params$datadir, "/", name.list.samples[j], "_with_all_phenotypes_all_cells_18caths.csv"), stringsAsFactors = FALSE, encoding = "UTF-8")
pheno2<- unique(tab01$category)
pheno <- c(pheno2,pheno)
}
uniq.pheno <- sort(unique(pheno))
dens.all <- matrix(0,1,length(name.list.samples)) # step 7 in the manuscript: count the number of cells for each of the 44 ROIs
rownames(dens.all) <- "Total cell density"
colnames(dens.all) <- name.list.samples
dens.pheno <- matrix(0,length(uniq.pheno),length(name.list.samples))
rownames (dens.pheno) <- uniq.pheno
colnames(dens.pheno) <- name.list.samples
for(i in 1:length(name.list.samples)){
tab01 <- read.csv(paste0(params$datadir, "/", name.list.samples[i], "_with_all_phenotypes_all_cells_18caths.csv"), stringsAsFactors = FALSE, encoding = "UTF-8")
dens.all[i] <- nrow(tab01) # counts of all cells per sqmm
for (r in 1:length(uniq.pheno)) {
tab02 <- tab01[tab01$category == paste0(uniq.pheno[r]),]
dens.pheno[r,i] <-nrow(tab02)
}
}
dens.pheno.final <-as.data.frame(cbind(rowMeans(dens.pheno[,9:16]),rowMeans(dens.pheno[,40:44]),rowMeans(dens.pheno[,1:8]), # step 8 in the manuscript: sum up the couts for ROIS belonging to the one patient
rowMeans(dens.pheno[,17:24]),rowMeans(dens.pheno[,25:31]),rowMeans(dens.pheno[,32:39])))
colnames(dens.pheno.final) <- c('CRC02','CRC03','CRC04','CRC13','CRC26','CRC26LM')
dens.all.final <- as.data.frame(cbind(sum(dens.all[,9:16]),sum(dens.all[,40:44]),sum(dens.all[,1:8]),
sum(dens.all[,17:24]),sum(dens.all[,25:31]),sum(dens.all[,32:39])))
colnames(dens.all.final) <- c('CRC02','CRC03','CRC04','CRC13','CRC26','CRC26LM')
organoids <- c('CRC02','CRC03','CRC04','CRC13','CRC26','CRC26LM')
write.csv(dens.pheno.final, file.path(params$resultsdir,"density.18.categories.csv"), row.names = TRUE)
colours.key.18 <- c(
'#FB1313',
'#53F328',
'#299C0A',
'#0F590D',
'#5E3DAF',
'#73D5F1',
'#46FBC3',
'#94FF33',
'#043A02',
'#0ACC55',
'#53D786',
'#53AF77',
'#FF5004',
'#37FB03',
'#1F820A',
'#BD2207',
'#3E3D3D',
'#B831F7'
)
data.df <- reshape::melt(as.matrix(dens.pheno.final))
colnames(data.df) <- c ("X1", "X2", "value")
data.df2 <- transform(data.df, X2=factor(X2, levels = c('CRC26LM','CRC26','CRC13','CRC04','CRC03','CRC02')))
png(file.path(params$resultsdir, "cell_densities_per_18_categories.png"), height=600, width=900)
ggplot(data=data.df2, aes(x=X1, y=value, fill=value)) +
geom_col(position = "dodge", aes(fill=factor(X1))) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 30)) +
theme(axis.text.x = element_text(size = 12)) +
theme(axis.text.y = element_text(size = 12)) +
theme(plot.title = element_text(hjust = 0.5, size = 22)) +
labs(title = "Cell densities [cells/mm²] for 18 categories", x = "", y = "") +
facet_wrap(~X2, ncol = 1, strip.position = "left") +
theme(strip.placement = "outside",strip.text.y.left = element_text(size = 18, angle = 0), #12
strip.background = element_rect(fill = "white")) +
theme(legend.position = "bottom", legend.title = element_blank()) +
scale_fill_manual(values= colours.key.18) +
ylim(0,1500) +
guides(fill=guide_legend(ncol=4)) +
guides(shape = guide_legend(override.aes = list(size = 2))) +
guides(color = guide_legend(override.aes = list(size = 2))) +
theme(legend.position = "none")
dev.off()
```
Cell (phenotype) density
```{r}
im.area <- (1000*1000)/1000000
pheno <- NULL # step 9 in the manuscript: search for unique phenotype names in all .csv files
for(j in 1:length(name.list.samples)){
tab01 <- read.csv(paste0(params$datadir, "/", name.list.samples[j], "_with_all_phenotypes_all_cells_18caths.csv"), stringsAsFactors = FALSE,
encoding = "UTF-8")
pheno2<- unique(tab01$phenotype)
pheno <- c(pheno2,pheno)
}
uniq.pheno <- unique(pheno)
ordering <- c ( 'Tcells_undefined',
'Prol_Tcells',
'CD8+_Tcells',
'Intraepithelial_CD8+_Tcells',
'Intraepithelial_CD4+_PD1+_Tcells',
'CD4+_Tcells',
'CD8-_Tcells',
'Tregs',
'ICOS+_Tregs',
'CD57+_cells',
'CD45RO_undefined',
'CD11c+_cells',
'CD38+_cells',
'VISTA+_CD31+_CD38+_cells',
'ILC', # innate lymphoid cells
'CD163+_HLA-DR+_macrophages',
'HLA-DR+_macrophages',
'CD11c+_HLA-DR+_macrophages',
'Macrophages_undefined',
'HLA-DR+_monocytes',
'Monocytes',
'Granulocytes',
'Fibroblasts',
'HLA-DR+_fibroblasts',
'D2-40_fibroblasts',
'D2-40+_TGFb+_fibroblasts',
'TGFb+_tum',
'Bcat+_FOXP3+_PDL1+_tum',
'Bcat+_CD15+_tum',
'Bcat+_Prol_tum',
'Bcat+_HLA-DR+_Prol_tum',
'Bcat+_HLA-DR+_tum',
'Bcat+_TGFb+_tum',
'Bcat+_tum',
'Bcat+_HLA-DR+_TGFb+_tum',
'Bcat+_Prol_TGFb+_tum',
'Apoptotic_Bcat+_Prol_tum',
'Apoptotic_Bcat+_tum',
'Apoptotic_Bcat+_HLA-DR+_tum',
'Vessels',
'D2-40+_vessels',
'TGFb+_vessels',
'VISTA+_vessels',
'Unknown'
)
dens.all <- matrix(0,1,length(name.list.samples))
rownames(dens.all) <- "Total cell density"
colnames(dens.all) <- name.list.samples
dens.pheno <- matrix(0,length(ordering),length(name.list.samples))
rownames (dens.pheno) <- ordering
colnames(dens.pheno) <- name.list.samples
for(i in 1:length(name.list.samples)){ # step 10: count the number of cells for each of the 44 samples
tab01 <- read.csv(paste0(params$datadir, "/", name.list.samples[i], "_with_all_phenotypes_all_cells_18caths.csv"), stringsAsFactors = FALSE,
encoding = "UTF-8")
dens.all[i] <- nrow(tab01)/im.area # all cells per sqmm
for (r in 1:length(ordering)) {
tab02 <- tab01[tab01$phenotype == paste0(ordering[r]),]
dens.pheno[r,i] <-nrow(tab02)/im.area
}
}
dens.pheno.final43 <- as.data.frame(cbind(rowMeans(dens.pheno[,9:16]),rowMeans(dens.pheno[,40:44]),rowMeans(dens.pheno[,1:8]), # step 11: group the samples together that belong to one tumor/patient
rowMeans(dens.pheno[,17:24]),rowMeans(dens.pheno[,25:31]),rowMeans(dens.pheno[,32:39])))
colnames(dens.pheno.final43) <- c('CRC02','CRC03','CRC04','CRC13','CRC26','CRC26LM')
dens.all.final <- as.data.frame(cbind(sum(dens.all[,9:16]),sum(dens.all[,40:44]),sum(dens.all[,1:8]),
sum(dens.all[,17:24]),sum(dens.all[,25:31]),sum(dens.all[,32:39])))
colnames(dens.all.final) <- c('CRC02','CRC03','CRC04','CRC13','CRC26','CRC26LM')
write.csv(dens.pheno.final43, file.path(params$resultsdir,"density.43.phenotypes.csv"), row.names = TRUE)
colours.key <- c(
'#53F328',
'#62D243',
'#37FB03',
'#299C0A',
'#71BD5D',
'#1D590D',
'#0F590D',
'#478A44',
'#043A02',
'#94FF33',
'#6AAB2F',
'#5AA615',
'#4BD82E',
'#1F820A',
'#04F90E',
'#0ACC55',
'#13A14A',
'#53D786',
'#53AF77',
'#46FBC3',
'#73D5F1',
'#2896E5',
'#9873F1',
'#754CDA',
'#5E3DAF',
'#062697',
'#B61818',
'#FB1313',
'#BD2207',
'#FF5004',
'#F3842C',
'#F99E0B',
'#BB4B0B',
'#F77B13',
'#F7A413',
'#F7C613',
'#C6A839',
'#C67939',
'#F39E0B',
'#C6398C',
'#F70795',
'#9F0560',
'#B831F7',
'#3E3D3D'
)
data.counts <- as.matrix(dens.pheno.final43)
data.countss <- rbind(data.counts[1:9,], data.counts[11:12,], data.counts[14:44,])
rownames(data.countss) <- c(
'01 - Tcells undefined',
'02 - Proliferating Tcells',
'03 - CD8+ Tcells',
'04 - Intraepithelial CD8+ Tcells',
'05 - Intraepithelial CD4+PD1+ Tcells',
'06 - CD4+ Tcells',
'07 - CD8- Tcells',
'08 - Tregs',
'09 - ICOS+ Tregs',
'10 - CD45RO undefined',
'11 - CD11c+ cells',
'12 - VISTA+ CD31+CD38+ cells',
'13 - ILC',
'14 - CD163+HLA-DR+ macrophages',
'15 - HLA-DR+ macrophages',
'16 - CD11c+HLA-DR+ macrophages',
'17 - Macrophages undefined',
'18 - HLA-DR+ monocytes',
'19 - Monocytes',
'20 - Granulocytes',
'21 - Fibroblasts',
'22 - HLA-DR+ fibroblasts',
'23 - D2-40+ fibroblasts',
'24 - D2-40+TGFb+ fibroblasts',
'25 - TGFb+ tumor cells',
'26 - Bcat+FOXP3+PDL1+ tumor cells',
'27 - Bcat+CD15+ tumor cells',
'28 - Bcat+ proliferating tumor cells',
'29 - Bcat+HLA-DR+ proliferating tumor cells',
'30 - Bcat+HLA-DR+ tumor cells',
'31 - Bcat+TGFb+ tumor cells',
'32 - Bcat+ tumor cells',
'33 - Bcat+HLA-DR+TGFb+ tumor cells',
'34 - Bcat+TGFb+ proliferating tumor cells',
'35 - Apoptotic Bcat+ proliferating tumor cells',
'36 - Apoptotic Bcat+ tumor cells',
'37 - Apoptotic Bcat+HLA-DR+ tumor cells',
'38 - Vessels',
'39 - D2-40+ vessels',
'40 - TGFb+ vessels',
'41 - VISTA+ vessels',
'42 - Undefined cells'
)
data.counts <- as.matrix(data.countss[1:41,])
melted.counts <- reshape2::melt(data.counts)
colnames(melted.counts) <- c("X1","X2","value")
#pdf(file.path(params$resultsdir, "cell_densities_per_41_phenotype.pdf"), height=5, width=15)
png(file.path(params$resultsdir, "cell_densities_per_41_phenotype.png"), height=600, width=1500) # step 12: plot the data
ggplot(data=melted.counts, aes(x=X1, y=value, fill=value)) +
geom_col(position = "dodge", aes(fill=factor(X1))) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
scale_x_discrete(labels=c("01","02","03","04","05","06","07","08","09","10",
"11","12","13","14","15","16","17","18","19","20",
"21","22","23","24","25","26","27","28","29","30",
"31","32","33","34","35","36","37","38","39","40",
"41")) +
theme(axis.text.x = element_text(size = 12)) +
theme(axis.text.y = element_text(size = 12)) +
theme(plot.title = element_text(hjust = 0.5, size = 22)) +
labs(title = "Cell densities [cells/mm²]", x = "", y = "") +
facet_wrap(~X2, ncol = 1, strip.position = "left") +
theme(strip.placement = "outside",strip.text.y.left = element_text(size = 18, angle = 0),
strip.background = element_rect(fill = "white")) +
theme(legend.position = "bottom", legend.title = element_blank()) +
scale_fill_manual(values= colours.key) +
ylim(0,1500) +
guides(fill=guide_legend(ncol=4)) +
guides(shape = guide_legend(override.aes = list(size = 2))) +
guides(color = guide_legend(override.aes = list(size = 2)))
dev.off()
```
6 markers immune vs tumor cells
```{r}
im.area <- 1
dens.tum.pdl1 <- matrix(0,1,length(name.list.samples)) # step 13: create empty matrices in order to count the tumor and immune cells positive for the particular markers
rownames(dens.tum.pdl1) <- "PDL1+ tumor cells"
colnames(dens.tum.pdl1) <- name.list.samples
dens.imu.pdl1 <- matrix(0,1,length(name.list.samples))
rownames(dens.imu.pdl1) <- "PDL1+ immune cells"
colnames(dens.imu.pdl1) <- name.list.samples
dens.tum.pd1 <- matrix(0,1,length(name.list.samples))
rownames(dens.tum.pd1) <- "PD1+ tumor cells"
colnames(dens.tum.pd1) <- name.list.samples
dens.imu.pd1 <- matrix(0,1,length(name.list.samples))
rownames(dens.imu.pd1) <- "PD1+ immune cells"
colnames(dens.imu.pd1) <- name.list.samples
dens.tum.tim3 <- matrix(0,1,length(name.list.samples))
rownames(dens.tum.tim3) <- "TIM3+ tumor cells"
colnames(dens.tum.tim3) <- name.list.samples
dens.imu.tim3 <- matrix(0,1,length(name.list.samples))
rownames(dens.imu.tim3) <- "TIM3+ immune cells"
colnames(dens.imu.tim3) <- name.list.samples
dens.tum.lag3 <- matrix(0,1,length(name.list.samples))
rownames(dens.tum.lag3) <- "LAG3+ tumor cells"
colnames(dens.tum.lag3) <- name.list.samples
dens.imu.lag3 <- matrix(0,1,length(name.list.samples))
rownames(dens.imu.lag3) <- "LAG3+ immune cells"
colnames(dens.imu.lag3) <- name.list.samples
dens.tum.icos <- matrix(0,1,length(name.list.samples))
rownames(dens.tum.icos) <- "ICOS+ tumor cells"
colnames(dens.tum.icos) <- name.list.samples
dens.imu.icos <- matrix(0,1,length(name.list.samples))
rownames(dens.imu.icos) <- "ICOS+ immune cells"
colnames(dens.imu.icos) <- name.list.samples
dens.tum.ido <- matrix(0,1,length(name.list.samples))
rownames(dens.tum.ido) <- "IDO+ tumor cells"
colnames(dens.tum.ido) <- name.list.samples
dens.imu.ido <- matrix(0,1,length(name.list.samples))
rownames(dens.imu.ido) <- "IDO+ immune cells"
colnames(dens.imu.ido) <- name.list.samples
# crate character vectors for both immune and tumor phenotypes
tumor.phenotypes <- c("Bcat+_FOXP3+_PDL1+_tum", "TGFb+_tum", "Bcat+_CD15+_tum", "Bcat+_Prol_tum", "Bcat+_HLA-DR+_Prol_tum",
"Bcat+_HLA-DR+_tum", "Bcat+_TGFb+_tum", "Bcat+_tum", "Bcat+_Prol_TGFb+_tum", "Bcat+_HLA-DR+_TGFb+_tum",
"Apoptotic_Bcat+_Prol_tum", "Apoptotic_Bcat+_tum", "Apoptotic_Bcat+_HLA-DR+_tum")
immune.phenotypes <- c("Tcells_undefined", "Prol_Tcells", "CD8+_Tcells", "Intraepithelial_CD8+_Tcells", "Intraepithelial_CD4+_PD1+_Tcells",
"CD4+_Tcells", "CD8-_Tcells", "Tregs", "ICOS+_Tregs", "CD45RO_undefined", "CD11c+_cells", "ILC",
"CD163+_HLA-DR+_macrophages", "HLA-DR+_macrophages", "CD11c+_HLA-DR+_macrophages", "Macrophages_undefined",
"HLA-DR+_monocytes", "Monocytes", "Granulocytes")
for(i in 1:length(name.list.samples)){ # step 14: search for the lines that contain a particular phenotype name, either form the tumor cell group or immune cell group
tab01 <- read.csv(paste0(params$datadir, "/", name.list.samples[i], "_with_all_phenotypes_all_cells_18caths.csv"),
stringsAsFactors =FALSE, encoding = "UTF-8")
a <-0
b <-0
c <-0
d <-0
e <-0
f <-0
g <-0
h <-0
m <-0
n <-0
k <-0
l <-0
df <- tab01
df2 <- subset(df, df$phenotype %in% tumor.phenotypes)
df3 <- subset(df2, df2$PD.L1>0.1)
a <- nrow(df3)
df2 <- subset(df, df$phenotype %in% immune.phenotypes)
df3 <- subset(df2, df2$PD.L1>0.1)
b <- nrow(df3)
df2 <- subset(df, df$phenotype %in% tumor.phenotypes)
df3 <- subset(df2, df2$PD.1>0.1)
c <- nrow(df3)
df2 <- subset(df, df$phenotype %in% immune.phenotypes)
df3 <- subset(df2, df2$PD.1>0.1)
d <- nrow(df3)
df2 <- subset(df, df$phenotype %in% tumor.phenotypes)
df3 <- subset(df2, df2$TIM.3>0.1)
e <- nrow(df3)
df2 <- subset(df, df$phenotype %in% immune.phenotypes)
df3 <- subset(df2, df2$TIM.3>0.1)
f <- nrow(df3)
df2 <- subset(df, df$phenotype %in% tumor.phenotypes)
df3 <- subset(df2, df2$LAG.3>0.1)
g <- nrow(df3)
df2 <- subset(df, df$phenotype %in% immune.phenotypes)
df3 <- subset(df2, df2$LAG.3>0.1)
h <- nrow(df3)
df2 <- subset(df, df$phenotype %in% tumor.phenotypes)
df3 <- subset(df2, df2$ICOS>0.1)
m <- nrow(df3)
df2 <- subset(df, df$phenotype %in% immune.phenotypes)
df3 <- subset(df2, df2$ICOS>0.1)
n <- nrow(df3)
df2 <- subset(df, df$phenotype %in% tumor.phenotypes)
df3 <- subset(df2, df2$IDO>0.1)
k <- nrow(df3)
df2 <- subset(df, df$phenotype %in% immune.phenotypes)
df3 <- subset(df2, df2$IDO>0.1)
l <- nrow(df3)
dens.tum.pdl1[i] <- a/im.area
dens.imu.pdl1 [i] <- b/im.area
dens.tum.pd1[i] <- c/im.area
dens.imu.pd1 [i] <- d/im.area
dens.tum.tim3[i] <- e/im.area
dens.imu.tim3 [i] <- f/im.area
dens.tum.lag3[i] <- g/im.area
dens.imu.lag3 [i] <- h/im.area
dens.tum.icos[i] <- m/im.area
dens.imu.icos [i] <- n/im.area
dens.tum.ido[i] <- k/im.area
dens.imu.ido [i] <- l/im.area
}
dens.double2 <- rbind( dens.tum.pdl1, dens.imu.pdl1,
dens.tum.pd1, dens.imu.pd1,
dens.tum.tim3, dens.imu.tim3,
dens.tum.lag3, dens.imu.lag3,
dens.tum.icos, dens.imu.icos,
dens.tum.ido, dens.imu.ido);
dens.double.final2 <-as.data.frame(cbind(rowMeans(dens.double2[,9:16]),rowMeans(dens.double2[,40:44]),rowMeans(dens.double2[,1:8]), # step 15: group samples together that belong to one tumor/patient
rowMeans(dens.double2[,17:24]),rowMeans(dens.double2[,25:31]),rowMeans(dens.double2[,32:39])))
colnames(dens.double.final2) <- c('CRC02','CRC03','CRC04','CRC13','CRC26','CRC26LM')
transp2 <- as.matrix(dens.double.final2[5:12,])
colnames(transp2) <- organoids
data.m5 <- reshape::melt(transp2)
marker <- c(rep(c( "TIM3+", "TIM3+","LAG3+", "LAG3+", "ICOS+", "ICOS+","IDO+", "IDO+"),6))
tissue <- c(rep(c("tumor cells", "immune cells"),24))
data.m6 <- cbind(data.m5,marker, tissue)
colnames(data.m6) <- c("X1","X2","value","marker", "tissue")
data.m6 <- select(data.m6, X2:tissue)
colnames(data.m6) <- c("organoid","count", "marker","tissue")
#pdf(file.path(params$resultsdir, "cell_densities_4_markers_tum_imu.pdf"), height=5, width=15)
png(file.path(params$resultsdir, "cell_densities_4_markers_tum_imu.png"), height=500, width=1500)
ggplot(data=data.m6, aes(x=organoid, y=count, fill=tissue)) +
geom_col(position = "dodge", width = 0.5) +
scale_fill_manual(values = c("#F38722","blue")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 26)) +
theme(axis.text.y = element_text(size = 24)) +
theme(axis.title.y = element_text(size = 24),strip.background = element_rect(fill = "white")) +
theme(legend.title = element_blank()) +
theme(legend.text = element_text(size = 16)) +
theme(legend.position = c(1,1), legend.justification = c(1,1)) +
theme(strip.text.x = element_text(size = 24)) +
theme(title = element_text(size = 24))+
theme(plot.title = element_text(hjust = 0.5)) +
labs(title = "", x = "", y = "Cell densities [cells/mm²]") +
facet_wrap(~marker, nrow = 1)
dev.off()
transp3 <- as.matrix(dens.double.final2[1:4,])
data.m7 <- reshape::melt(transp3)
marker <- c(rep(c("PDL1+","PDL1+", "PD1+","PD1+"),6))
tissue <- c(rep(c("tumor cells", "immune cells"),12))
data.m8 <- cbind(data.m7,marker, tissue)
colnames(data.m8) <- c("X1","X2","value","marker", "tissue")
data.m8 <- select(data.m8, X2:tissue)
colnames(data.m8) <- c("organoid","count", "marker","tissue")
#pdf(file.path(params$resultsdir, "density_pd1_pdl1.pdf"), height = 8, width = 12)
png(file.path(params$resultsdir, "density_pd1_pdl1.png"), height = 800, width = 1200) # step 16: plot the counts as barplots
ggplot(data=data.m8, aes(x=organoid, y=count, fill=tissue)) +
geom_col(position = "dodge", width = 0.5) +
scale_fill_manual(values = c("#F38722","blue")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle = 60, hjust = 1, size = 26)) +
theme(axis.text.y = element_text(size = 28)) +
theme(axis.title.y = element_text(size = 28),strip.background = element_rect(fill = "white")) +
theme(legend.title = element_blank()) +
theme(legend.text = element_text(size = 20)) +
theme(legend.position = c(1,1), legend.justification = c(1,1)) +
theme(strip.text.x = element_text(size = 28)) +
theme(title = element_text(size = 32))+
theme(plot.title = element_text(hjust = 0.5)) +
labs(title = "", x = "", y = "Cell densities [cells/mm²]") +
facet_wrap(~marker)
dev.off()
```