Last updated: 2021-02-19

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Knit directory: melanoma_publication_old_data/

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Introduction

This script generates plots for Figure 4.

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   ?                                       
visible FALSE                                   
        code/helper_functions/censor_dat.R
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        code/helper_functions/detect_mRNA_expression.R
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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
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visible FALSE                                    
        code/helper_functions/getSpotnumber.R
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visible FALSE                                
        code/helper_functions/plotCellCounts.R
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        code/helper_functions/plotCellFractions.R
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visible FALSE                                    
        code/helper_functions/plotDist.R
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visible FALSE                           
        code/helper_functions/scatter_function.R
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visible FALSE                                   
        code/helper_functions/sceChecks.R
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visible FALSE                            
        code/helper_functions/validityChecks.R
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visible FALSE                                 
library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table) 
library(ggplot2)
library(cba)
library(ComplexHeatmap)
library(colorRamps)
library(circlize)
library(RColorBrewer)
library(ggpubr)
library(ggbeeswarm)
library(gridExtra)
library(tidyr)
library(ggpmisc)
library(circlize)
library(coxme)
library(dittoSeq)
library(scater)
library(cowplot)
library(survminer)
library(ggalluvial)
library(cytomapper)
library(ggrepel)
library(rstatix)

Read the data

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

# meta data
dat_relation = fread(file = "data/data_for_analysis/protein/Object relationships.csv",stringsAsFactors = FALSE)
dat_relation_rna = fread(file = "data/data_for_analysis/RNA/Object relationships.csv",stringsAsFactors = FALSE)

# image
image_mat_prot <- read.csv("data/data_for_analysis/protein/Image.csv")

# surv_dat
dat_survival_prot <- fread(file = "data/data_for_analysis/protein/clinical_data_protein.csv")

Prepare Relation Data Protein

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

# add celltype status to first and second label
celltype_first <- data.frame(colData(sce_prot))[,c("cellID", "celltype", "celltype_clustered")]
colnames(celltype_first) <- c("cellID_first", "celltype_first", "celltype_clust_first")
celltype_second <- data.frame(colData(sce_prot))[,c("cellID", "celltype", "celltype_clustered")]
colnames(celltype_second) <- c("cellID_second", "celltype_second", "celltype_clust_second")

dat_relation <- left_join(dat_relation, celltype_first, by = "cellID_first")
dat_relation <- left_join(dat_relation, celltype_second, by = "cellID_second")

colnames(dat_relation)[5] <- "FirstImageNumber"

Prepare Relation Data RNA

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

# add celltype status to first and second label
celltype_first <- data.frame(colData(sce_rna))[,c("cellID", "celltype_rf", "celltype_clustered")]
colnames(celltype_first) <- c("cellID_first", "celltype_first", "celltype_clust_first")
celltype_second <- data.frame(colData(sce_rna))[,c("cellID", "celltype_rf", "celltype_clustered")]
colnames(celltype_second) <- c("cellID_second", "celltype_second", "celltype_clust_second")

dat_relation_rna <- left_join(dat_relation_rna, celltype_first, by = "cellID_first")
dat_relation_rna <- left_join(dat_relation_rna, celltype_second, by = "cellID_second")

colnames(dat_relation_rna)[5] <- "FirstImageNumber"

Figure 4A

Tumor Marker Profile for different Tcell Scoring Groups

tumor_marker_protein <- c("pS6", "H3K27me3", "HLADR", "PDL1", "IDO1")
tumor_marker_rna <- c("B2M")

# rna data 
dat_rna <- data.frame(t(assay(sce_rna[tumor_marker_rna, sce_rna$celltype == "Tumor"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(dat_rna, data.frame(colData(sce_rna))[,c("cellID", "Tcell_density_score_image", "Description", "MM_location", "Location")])

# filter
dat_rna <- dat_rna %>%
  filter(Location != "CTRL")

# mean per image
dat_rna <- dat_rna %>%
  select(-cellID) %>%
  group_by(Description, Tcell_density_score_image) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE)

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

# protein data
dat_prot <- data.frame(t(assay(sce_prot[tumor_marker_protein,, sce_prot$celltype == "Tumor"], "asinh")))
dat_prot$cellID <- rownames(dat_prot)
dat_prot <- left_join(dat_prot, data.frame(colData(sce_prot))[,c("cellID", "Tcell_density_score_image", "Description", "MM_location", "Location")])

# filter
dat_prot <- dat_prot %>%
  filter(Location != "CTRL")

# mean per image
dat_prot <- dat_prot %>%
  select(-cellID) %>%
  group_by(Description, Tcell_density_score_image) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE)

# melt
dat_prot <- dat_prot %>%
  reshape2::melt(id.vars = c("Description", "Tcell_density_score_image"), variable.name = "channel", value.name = "asinh")

# join both data sets
comb <- rbind(dat_prot, dat_rna)


# adjusted wilcox.test for groups
group_comparison <- list(c("absent", "high"), c("med", "high"))

stat.test <- comb %>%
  group_by(channel) %>%
  wilcox_test(data = ., asinh ~ Tcell_density_score_image) %>%
  adjust_pvalue(method = "BH") %>%
  add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
  add_xy_position(x = "Tcell_density_score_image", dodge = 0.8, comparisons = group_comparison) %>%
  filter(is.na(y.position) == FALSE)

# plot 
p <- ggplot(comb, aes(x=Tcell_density_score_image, y=asinh, 
                      group=Tcell_density_score_image)) + 
  geom_boxplot(alpha=0.2, lwd=1, aes(group=Tcell_density_score_image, fill = Tcell_density_score_image)) +
  facet_wrap(~channel, scales = "free", ncol=3) + 
  stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) + 
  geom_quasirandom(alpha=0.6, size=2, aes(group=Tcell_density_score_image, col = Tcell_density_score_image)) +
  scale_color_discrete(guide = FALSE) +
  theme_bw() +
  theme(text = element_text(size=18),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank()) +
  xlab("") + 
  ylab("Mean Count per Image (asinh)") +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.15))) +
  guides(fill=guide_legend(title="T cell Score", override.aes = c(lwd=0.5, alpha=1)))

leg <- get_legend(p)

grid.arrange(p + theme(legend.position = "none"))
grid.arrange(leg)

Figure 4B

Load masks

all_mask <- loadImages(x = "data/full_data/rna/cpout/",
                       pattern = "ilastik_s2_Probabilities_equalized_cellmask.tiff")

add the ImageNumber to masks

# we load the metadata for the images.
image_mat_rna <- as.data.frame(read.csv(file = "data/data_for_analysis/rna/Image.csv",stringsAsFactors = FALSE))

# we extract only the FileNames of the masks as they are in the all_masks object
cur_df <- data.frame(cellmask = image_mat_rna$FileName_cellmask,
                     ImageNumber = image_mat_rna$ImageNumber,
                     Description = image_mat_rna$Metadata_Description)

# we set the rownames of the extracted data to be equal to the names of all_masks
rownames(cur_df) <- gsub(pattern = ".tiff",replacement = "",image_mat_rna$FileName_cellmask)

# we add the extracted information via mcols in the order of the all_masks object
mcols(all_mask) <- cur_df[names(all_mask),]

scale the masks

all_mask <- scaleImages(all_mask,2^16-1)

Plot two example Images

# subset masks
mask_sub <- all_mask[mcols(all_mask)$Description %in% c("L11", "N3")]
sce_rna_sub <- sce_rna[,sce_rna$Description %in% c("L11","N3")]

plotCells(mask = mask_sub, 
          object = sce_rna_sub,
          img_id = "Description", cell_id = "CellNumber",
          colour_by = c("CD3","CD8", "Mart1", "SOX10", "B2M"),
          colour = list(CD3 = c("black", "green"),
                        CD8 = c("black", "green"),
                        Mart1 = c("black", "blue"),
                        SOX10 = c("black", "blue"),
                        B2M = c("black", "red")),
          display = "single",
          exprs_values = "scaled_asinh",
          scale = TRUE)

Figure 4C

Correlation with mean B2M expression and T cell density

tumor_dat <- data.frame(t(assay(sce_rna["B2M", sce_rna$celltype == "Tumor" & sce_rna$Location != "CTRL"], "asinh")))
tumor_dat$Description <- sce_rna[, sce_rna$celltype == "Tumor" & sce_rna$Location != "CTRL"]$Description

tumor_dat <- tumor_dat %>%
  group_by(Description) %>%
  summarise(mean_B2M = mean(B2M))

cur_df <- data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  group_by(Description, BlockID, celltype) %>%
  summarise(n=n()) %>%
  mutate(fraction = n/sum(n)) %>%
  ungroup() %>%
  complete(Description, celltype, fill = list(fraction = 0)) %>%
  filter(celltype == "CD8+ T cell")

cur_df_chemokine <- data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  group_by(Description, chemokine) %>%
  summarise(n=n()) %>%
  reshape2::dcast(Description ~ chemokine, value.var = "n", fill = 0) %>%
  mutate(fraction_positive = `TRUE` / (`FALSE` + `TRUE`))
  
tumor_dat <- left_join(tumor_dat, cur_df)
tumor_dat_chemokine <- left_join(tumor_dat, cur_df_chemokine)

# remove bad images and controls
tumor_dat <- tumor_dat
tumor_dat_chemokine <- tumor_dat_chemokine

# boxplot
a <- ggplot(tumor_dat, aes(y=mean_B2M, x=log10(fraction))) + 
  geom_point(size=3) +
  geom_smooth(method = "lm", formula = y ~ poly(x,2)) +
  stat_regline_equation(formula = y ~ poly(x,2), 
                aes(label =  ..rr.label..), 
                size=10) +
  ylab("Mean B2M Expression (asinh)") +
  xlab("Cytotoxic T cell Fraction (log10)") +
  theme_bw() + 
  theme(text = element_text(size=18))

b <- ggplot(tumor_dat_chemokine, aes(y=mean_B2M, x=log10(fraction_positive))) + 
  geom_point(size=3) +
  geom_smooth(method = "lm", formula = y~poly(x,2)) +
  stat_poly_eq(formula = y ~ poly(x,2), 
                aes(label =  ..rr.label..), 
                parse = TRUE, size=10) +
  ylab("Mean B2M Expression (asinh)") +
  xlab("Chemokine-Expressing Cell Fraction (log10)") +
  theme_bw() + 
  theme(text = element_text(size=18))

grid.arrange(a,b, nrow=1)
Warning: Removed 6 rows containing non-finite values (stat_smooth).
Warning: Removed 6 rows containing non-finite values (stat_regline_equation).

Figure 4D

Interaction between Dysfunctional CD8 and Tumor Cells

# number of interactions CD8+ T cell and tumor per image
dat_relation_sub <- dat_relation_rna[dat_relation_rna$celltype_first %in% c("exhausted Tcytotoxic", "Tcytotoxic") & 
                                   dat_relation_rna$celltype_second == "Tumor"]

# count number of interactions
count <- dat_relation_sub %>%
  group_by(FirstImageNumber) %>%
  summarise(n=n())
names(count)[1] <- "ImageNumber"

# fraction of exhausted cd8 per image
dysfunction <- data.frame(colData(sce_rna)) %>%
  mutate(celltype2 = paste(celltype, CXCL13, sep = "_")) %>%
  group_by(ImageNumber, celltype2) %>%
  summarise(n=n()) %>%
  reshape2::dcast(ImageNumber ~ celltype2, value.var = "n", fill = 0) %>%
  reshape2::melt(id.vars = c("ImageNumber"), variable.name = "celltype2", value.name = "n") %>%
  group_by(ImageNumber) %>%
  mutate(fraction = n / sum(n)) %>%
  filter(celltype2 == "CD8+ T cell_1") %>%
  ungroup() %>%
  select(ImageNumber, fraction)

# correlation plot
cur_dat <- left_join(count, dysfunction)

ggplot(cur_dat, aes(x=log10(fraction), y=log10(n))) + 
  geom_point(size=3) + 
  geom_smooth(method="lm") +
  stat_cor(method = "pearson",
           aes(label = paste0("atop(", ..r.label..,  ",", ..rr.label.. ,")")),
           size = 8, cor.coef.name = "R", label.sep="\n", label.y = 1, label.x = -2) + 
  theme_bw() +
  theme(text = element_text(size=14)) +
  xlab("Fraction of Dysfunctional Cytotoxic T Cells (log10)") +
  ylab("Number of CD8/Tumor Interactions\n(log10)")
Warning: Removed 59 rows containing non-finite values (stat_smooth).
Warning: Removed 59 rows containing non-finite values (stat_cor).

Figure 4E

Boxplots with interaction counts per Image

# add dysfunction score to dat_relation
ex_score <- data.frame(colData(sce_prot)) %>%
  distinct(ImageNumber, .keep_all = T) %>%
  select(ImageNumber, dysfunction_score, MM_location)

ex_score$FirstImageNumber <- ex_score$ImageNumber

dat_relation <- left_join(dat_relation, ex_score[,c("FirstImageNumber", "dysfunction_score", "MM_location")])

sum <- dat_relation %>%
  filter(celltype_first == "CD8+ T cell" & celltype_second == "Tumor" & !is.na(dysfunction_score)) %>%
  group_by(FirstImageNumber, MM_location, dysfunction_score, celltype_first, celltype_clust_second) %>%
  summarise(n=n()) %>%
  reshape2::dcast(FirstImageNumber + MM_location + dysfunction_score + celltype_first ~ celltype_clust_second, value.var = "n", fill=0) %>%
  reshape2::melt(id.vars = c("FirstImageNumber", "MM_location", "dysfunction_score", "celltype_first"), variable.name = "celltype", value.name = "n")

#sum <- sum[sum$MM_location == "skin_subcutaneous",]

# calculate fractions for every image (makes it more comparable)
sum2 <- sum %>%
  group_by(FirstImageNumber) %>%
  mutate(fraction = n/sum(n)) %>%
  ungroup()

stat.test <- sum2 %>%
  group_by(celltype) %>%
  wilcox_test(data = ., fraction ~ dysfunction_score) %>%
  adjust_pvalue(method = "BH") %>%
  add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
  add_x_position(x = "celltype", dodge = 0.8)

ggplot(sum2, aes(x=celltype, y=fraction)) +
  geom_boxplot(alpha=.2, lwd=1, aes(fill = dysfunction_score)) +
  geom_quasirandom(alpha=.6, dodge.width=.75, size=2, aes(group = dysfunction_score, col=dysfunction_score)) +
  stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7, y.position = 0.9) + 
  scale_color_discrete(guide = FALSE) +
  theme_bw() +
  theme(text = element_text(size = 16),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
  guides(fill=guide_legend(title="Dysfunction Score", override.aes = aes(lwd=0.5))) +
  xlab("") + 
  ylab("Fraction of Interactions") +
  ylim(0,1)

Figure 4F

Tumor Marker Profile for different Scoring Groups per Image

tumor_marker_protein <- c("S100", "MiTF")
tumor_marker_rna <- c("Mart1", "pRB")

# rna data 
dat_rna <- data.frame(t(assay(sce_rna[tumor_marker_rna, sce_rna$celltype == "Tumor"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(dat_rna, data.frame(colData(sce_rna))[,c("cellID", "dysfunction_score", "Description", "MM_location")])

# filter
dat_rna <- dat_rna %>%
  filter(dysfunction_score %in% c("high dysfunction", "low dysfunction"))

# mean per image
dat_rna <- dat_rna %>%
  select(-cellID) %>%
  group_by(Description, dysfunction_score) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE)

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

# protein data
dat_prot <- data.frame(t(assay(sce_prot[tumor_marker_protein,, sce_prot$celltype == "Tumor"], "asinh")))
dat_prot$cellID <- rownames(dat_prot)
dat_prot <- left_join(dat_prot, data.frame(colData(sce_prot))[,c("cellID", "dysfunction_score", "Description", "MM_location")])

# filter
dat_prot <- dat_prot %>%
  filter(dysfunction_score %in% c("high dysfunction", "low dysfunction"))

# mean per image
dat_prot <- dat_prot %>%
  select(-cellID) %>%
  group_by(Description, dysfunction_score) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE)

# melt
dat_prot <- dat_prot %>%
  reshape2::melt(id.vars = c("Description", "dysfunction_score"), variable.name = "channel", value.name = "asinh")

# join both data sets
comb <- rbind(dat_prot, dat_rna)

stat.test <- comb %>%
  group_by(channel) %>%
  wilcox_test(data = ., asinh ~ dysfunction_score) %>%
  adjust_pvalue(method = "BH") %>%
  add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
  add_xy_position(x = "celltype", dodge = 0.8)

# plot 
ggplot(comb, aes(x=dysfunction_score, y=asinh)) + 
  geom_boxplot(alpha=0.2, lwd=1, aes(fill=dysfunction_score)) +
  geom_quasirandom(alpha=0.6, size=3, aes(col=dysfunction_score)) +
  scale_color_discrete(guide = FALSE) +
  theme_bw() +
  theme(text = element_text(size=16),
        legend.position = "none") +
  facet_wrap(~channel, scales = "free") + 
  stat_pvalue_manual(stat.test, label = "p.adj.signif", size = 7) + 
  ylab("Mean Expression (asinh)") + 
  xlab("") +
  scale_y_continuous(expand = expansion(mult = c(0.05, 0.2)))

Figure 4G

S100 and T Cell Dysfunction

# fraction of exhausted cd8 per image
dysfunction <- data.frame(colData(sce_rna)) %>%
  mutate(celltype2 = paste(celltype, CXCL13, sep = "_")) %>%
  group_by(ImageNumber, celltype2) %>%
  summarise(n=n()) %>%
  reshape2::dcast(ImageNumber ~ celltype2, value.var = "n", fill = 0) %>%
  reshape2::melt(id.vars = c("ImageNumber"), variable.name = "celltype2", value.name = "n") %>%
  group_by(ImageNumber) %>%
  mutate(fraction = n / sum(n)) %>%
  filter(celltype2 == "CD8+ T cell_1") %>%
  ungroup() %>%
  select(ImageNumber, fraction)

# rna data 
dat_rna <- data.frame(t(assay(sce_rna["S100", sce_rna$celltype == "Tumor"], "asinh")))
dat_rna$cellID <- rownames(dat_rna)
dat_rna <- left_join(dat_rna, data.frame(colData(sce_rna))[,c("cellID", "ImageNumber")])

# mean per image
dat_rna <- dat_rna %>%
  select(-cellID) %>%
  group_by(ImageNumber) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE)

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

# correlation plot
cur_dat <- left_join(dysfunction, dat_rna)

# high density images
cur_dat <- cur_dat[cur_dat$ImageNumber %in% unique(sce_rna[,colData(sce_rna)$dysfunction_score %in% c("high dysfunction", "low dysfunction")]$ImageNumber),]

ggplot(cur_dat, aes(x=asinh, y=log10(fraction))) + 
  geom_point(size=3) + 
  geom_smooth(method="lm") +
  stat_cor(method = "pearson",
           aes(label = paste(..r.label.., ..rr.label.., sep = "~`,`~")),
           size = 8, cor.coef.name = "R", label.sep="\n", label.y.npc = "top", label.x.npc = "left") + 
  theme_bw() +
  theme(text = element_text(size=15)) +
  xlab("Mean S100 (asinh)") +
  ylab("Fraction of Dysfunctional T cells\n(log10)")
Warning: Removed 1 rows containing non-finite values (stat_smooth).
Warning: Removed 1 rows containing non-finite values (stat_cor).

Figure 4H

Response / Survival Analysis

# select blocks in ICI subgroup that are treatment-naive and non-adjuvant
blocks <- unique(sce_prot[,sce_prot$treatment_status_before_surgery == "naive" & 
                            sce_prot$treatment_group_after_surgery == "ICI" &
                            sce_prot$Adjuvant == "n"]$BlockID)

# subset survival data (only data points that contain info for 3m response)
dat_survival_prot_sub <- dat_survival_prot[dat_survival_prot$BlockID %in% blocks & 
                                             is.na(dat_survival_prot$Status_at_3m) == FALSE, ]

# remove LN margin samples
dat_survival_prot_sub <- dat_survival_prot_sub %>%
  mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
  filter(mmLocationPunch != "LN_M")

Celltypes Clustered in Responder vs. Non-Responder

im_size <- as.data.frame(cbind(image_mat_prot$Metadata_Description, (image_mat_prot$Height_cellmask * image_mat_prot$Width_cellmask)/1000000))
names(im_size) <- c("Description", "mm2")
im_size$mm2 <- as.numeric(im_size$mm2)
im_size[im_size$Description %in% c("G1", "G1 - split"), ]$mm2 <- mean(im_size[im_size$Description %in% c("G1", "G1 - split"), ]$mm2)
im_size <- im_size[im_size != "G1 - split",]

cur_dat <- data.frame(colData(sce_prot[,sce_prot$BlockID %in% unique(dat_survival_prot_sub$BlockID)])) %>%
  mutate(mmLocationPunch = paste(MM_location, Location, sep = "_")) %>%
  #filter(mmLocationPunch != "LN_M") %>% # remove LN margin images 
  #filter(celltype != "Tumor") %>%
  group_by(PatientID,BlockID, Description, bcell_patch_score, celltype_clustered) %>%
  summarise(n=n()) %>% 
  reshape2::dcast(PatientID + BlockID + Description + bcell_patch_score ~ celltype_clustered, value.var = "n", fill=0) %>%
  reshape2::melt(id.vars = c("PatientID","BlockID", "Description", "bcell_patch_score"), 
                 variable.name = "celltype", value.name = "n") %>%
  group_by(Description) %>%
  mutate(fraction = n/sum(n)) %>%
  mutate(total_cells = sum(n)) %>%
  ungroup() %>%
  filter(celltype %in% c("Tumor_1", "Tumor_2", "Tumor_3", "Tumor_4", "Tumor_5", "Tumor_6", "Tumor_7", "Tumor_8", "Tumor_9"))

cur_dat <- left_join(cur_dat, dat_survival_prot_sub[,c("BlockID", "Status_at_3m", "Primary_melanoma_type")]) %>%
  distinct(Description, celltype, .keep_all = T)

cur_dat <- left_join(cur_dat, im_size[im_size$Description %in% unique(cur_dat$Description),])
cur_dat$n_per_mm2 <- cur_dat$n / cur_dat$mm2

stat.test <- cur_dat %>%
  group_by(celltype) %>%
  mutate(y_log10 = log10(n_per_mm2+1)) %>%
  wilcox_test(data = ., y_log10 ~ Status_at_3m) %>%
  adjust_pvalue(method = "BH") %>%
  add_significance("p.adj",cutpoints = c(0, 1e-04, 0.001, 0.01, 0.1, 1)) %>%
  add_x_position(x = "celltype", dodge = 0.8)

#stat.test$p.adj.format <- ifelse(stat.test$p.adj < 0.05, paste("p = ", round(stat.test$p.adj,2), sep = ""), "n.s.")

ggplot(cur_dat, aes(x=celltype, y=log10(n_per_mm2+1))) + 
  geom_boxplot(alpha=.5, outlier.shape = NA, aes(fill=Status_at_3m)) +
  geom_quasirandom(size=1, dodge.width = 0.75, aes(group = Status_at_3m, col=Status_at_3m)) +
  stat_pvalue_manual(stat.test, x = "celltype", label = "p.adj.signif", size = 7, y.position = 4) + 
  theme_bw() +
  theme(text = element_text(size = 16),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + 
  ylab("Cells per mm2 (log10)") +
  xlab("") +
  scale_color_manual(guide = FALSE, values=c("blue", "orange")) +
  scale_fill_manual(values=c("blue", "orange")) + 
  guides(fill=guide_legend(title="Response 3m", override.aes = c(lwd=0.5, size=1))) +
  scale_y_continuous(expand = c(0.1, 0))
Warning: Duplicated aesthetics after name standardisation: size
# number of patients
dat_survival_prot_sub %>%
  distinct(PatientID, .keep_all = T) %>%
  group_by(Status_at_3m) %>%
  summarise(n=n())
# A tibble: 2 x 2
  Status_at_3m     n
  <chr>        <int>
1 NR               7
2 R                4
# number of images
dat_survival_prot_sub %>%
  distinct(Description, .keep_all = T) %>%
  group_by(Status_at_3m) %>%
  summarise(n=n())
# A tibble: 2 x 2
  Status_at_3m     n
  <chr>        <int>
1 NR              16
2 R                8

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               ggrepel_0.9.0              
 [3] cytomapper_1.3.1            EBImage_4.32.0             
 [5] ggalluvial_0.12.3           survminer_0.4.8            
 [7] cowplot_1.1.1               scater_1.16.2              
 [9] dittoSeq_1.0.2              coxme_2.2-16               
[11] bdsmatrix_1.3-4             survival_3.2-7             
[13] ggpmisc_0.3.7               gridExtra_2.3              
[15] ggbeeswarm_0.6.0            ggpubr_0.4.0               
[17] RColorBrewer_1.1-2          circlize_0.4.12            
[19] colorRamps_2.3              ComplexHeatmap_2.4.3       
[21] cba_0.2-21                  proxy_0.4-24               
[23] data.table_1.13.6           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] utf8_1.1.4                shinydashboard_0.7.1     
  [3] tidyselect_1.1.0          htmlwidgets_1.5.3        
  [5] BiocParallel_1.22.0       munsell_0.5.0            
  [7] codetools_0.2-18          withr_2.3.0              
  [9] colorspace_2.0-0          knitr_1.30               
 [11] rstudioapi_0.13           ggsignif_0.6.0           
 [13] labeling_0.4.2            git2r_0.28.0             
 [15] GenomeInfoDbData_1.2.4    KMsurv_0.1-5             
 [17] farver_2.0.3              pheatmap_1.0.12          
 [19] rprojroot_2.0.2           vctrs_0.3.6              
 [21] generics_0.1.0            xfun_0.20                
 [23] R6_2.5.0                  clue_0.3-58              
 [25] rsvd_1.0.3                locfit_1.5-9.4           
 [27] bitops_1.0-6              DelayedArray_0.16.0      
 [29] assertthat_0.2.1          promises_1.1.1           
 [31] scales_1.1.1              beeswarm_0.2.3           
 [33] gtable_0.3.0              rlang_0.4.10             
 [35] systemfonts_0.3.2         GlobalOptions_0.1.2      
 [37] splines_4.0.3             broom_0.7.3              
 [39] yaml_2.2.1                abind_1.4-5              
 [41] modelr_0.1.8              backports_1.2.1          
 [43] httpuv_1.5.4              tools_4.0.3              
 [45] ellipsis_0.3.1            raster_3.4-5             
 [47] polynom_1.4-0             ggridges_0.5.3           
 [49] Rcpp_1.0.5                plyr_1.8.6               
 [51] zlibbioc_1.36.0           RCurl_1.98-1.2           
 [53] GetoptLong_1.0.5          viridis_0.5.1            
 [55] zoo_1.8-8                 haven_2.3.1              
 [57] cluster_2.1.0             fs_1.5.0                 
 [59] magrittr_2.0.1            openxlsx_4.2.3           
 [61] reprex_0.3.0              whisker_0.4              
 [63] hms_0.5.3                 mime_0.9                 
 [65] evaluate_0.14             fftwtools_0.9-9          
 [67] xtable_1.8-4              rio_0.5.16               
 [69] jpeg_0.1-8.1              readxl_1.3.1             
 [71] shape_1.4.5               compiler_4.0.3           
 [73] crayon_1.3.4              htmltools_0.5.0          
 [75] mgcv_1.8-33               later_1.1.0.1            
 [77] tiff_0.1-6                lubridate_1.7.9.2        
 [79] DBI_1.1.0                 dbplyr_2.0.0             
 [81] Matrix_1.3-2              car_3.0-10               
 [83] cli_2.2.0                 pkgconfig_2.0.3          
 [85] km.ci_0.5-2               foreign_0.8-81           
 [87] sp_1.4-5                  xml2_1.3.2               
 [89] svglite_1.2.3.2           vipor_0.4.5              
 [91] XVector_0.30.0            rvest_0.3.6              
 [93] digest_0.6.27             rmarkdown_2.6            
 [95] cellranger_1.1.0          survMisc_0.5.5           
 [97] edgeR_3.30.3              DelayedMatrixStats_1.10.1
 [99] gdtools_0.2.3             curl_4.3                 
[101] shiny_1.5.0               rjson_0.2.20             
[103] lifecycle_0.2.0           nlme_3.1-151             
[105] jsonlite_1.7.2            carData_3.0-4            
[107] BiocNeighbors_1.6.0       viridisLite_0.3.0        
[109] limma_3.44.3              fansi_0.4.1              
[111] pillar_1.4.7              lattice_0.20-41          
[113] fastmap_1.0.1             httr_1.4.2               
[115] glue_1.4.2                zip_2.1.1                
[117] png_0.1-7                 svgPanZoom_0.3.4         
[119] stringi_1.5.3             BiocSingular_1.4.0       
[121] irlba_2.3.3