Last updated: 2022-02-22

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Rmd fa0f601 toobiwankenobi 2022-02-06 clean Supp Fig code
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Rmd fc55711 toobiwankenobi 2021-07-07 figure changes
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Rmd 3203891 toobiwankenobi 2021-02-19 change celltype names
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Rmd f9bb33a toobiwankenobi 2021-02-04 new Figure 5 and minor changes in figure order
Rmd 9442cb9 toobiwankenobi 2020-12-22 add all new files
Rmd 77466b7 Tobias Hoch 2020-10-22 work on subfigures
Rmd f643fb2 toobiwankenobi 2020-10-19 add tumor analysis
Rmd 58c40e5 toobiwankenobi 2020-10-19 correct files that don’t work
Rmd 1af3353 toobiwankenobi 2020-10-16 add stuff

Introduction

This script generates plots for Supplementary Figure 5.

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/findMilieu.R code/helper_functions/findPatch.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/plotCellCounts.R
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        code/helper_functions/plotCellFractions.R
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        code/helper_functions/plotDist.R code/helper_functions/read_Data.R
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        code/helper_functions/scatter_function.R
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        code/helper_functions/sceChecks.R
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        code/helper_functions/validityChecks.R
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library(SingleCellExperiment)
library(reshape2)
library(tidyverse)
library(dplyr)
library(data.table) 
library(ggplot2)
library(ComplexHeatmap)
library(rms)
library(ggrepel)
library(ggbeeswarm)
library(circlize)
library(ggpubr)
library(ggridges)
library(gridExtra)
library(rstatix)
library(cowplot)
library(ggrastr)

Read the data

# clinical data
dat <- read_csv("data/data_for_analysis/protein/clinical_data_protein.csv")
Rows: 167 Columns: 38
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (31): BlockID, Description, TissueType, Location, PatientID, IHC_T_score...
dbl  (7): SpotNr, ImageNumber, Nr_treatments_before_surgery, Time_to_death_o...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Protein data

sce_prot <- readRDS("data/data_for_analysis/sce_protein.rds")
sce_prot <- sce_prot[,sce_prot$Location != "CTRL"]

# take panel_meta because there are the channel names from the shiny output (and only there, not in the SCE)
panel_meta_prot <- read.csv(file = "data/data_for_analysis/protein/melanoma_1.06_protein.csv", 
                       sep= ",",  stringsAsFactors = FALSE )


# RNA data

sce_rna <- readRDS("data/data_for_analysis/sce_rna.rds")
sce_rna <- sce_rna[,sce_rna$Location != "CTRL"]

# take panel_meta because there are the channel names from the shiny output (and only there, not in the SCE)
panel_meta_rna <- read.csv(file = "data/data_for_analysis/rna/panel_mat.csv", stringsAsFactors = FALSE )

Supp Figure 5A

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("Biopsy Blocks 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_blank(),
        axis.ticks.x=element_blank(),
        text = element_text(size=16)) + 
  ylab("Number of Samples") +
  xlab("Biopsy Blocks") +
  scale_y_continuous(limits = c(0,4), expand = c(0, 0)) +
  guides(fill=guide_legend(title="Metasis Location"))

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

Version Author Date
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Supp Figure 5B

Select one random file per celltype and load file

# load all subsetted sce object from hierarchichal gating and combine the
label.files <- list.files("data/data_for_analysis/protein/celltype_classifier", full.names = TRUE)
file_names <- data.frame(path = label.files)
file_names$fileName <- sub(".*/", "", label.files)
file_names$celltype <- sub("\\_.*", "", file_names$fileName)

# select one random file per celltype
file_names <- file_names %>%
  group_by(celltype) %>%
  sample_n(1)

# Read in SCE objects
cur_sces <- lapply(file_names$path, readRDS)

Print gating scheme - Protein

for(element in cur_sces){
  # current labelled sce object
  sce_label <- element
  cur_celltype <- unique(sce_label$cytomapper_CellLabel)
  # select all gates from metadata
  gates <- metadata(sce_label)[grepl("cytomapper_gate_", names(metadata(sce_label)))]
  cur_img <- gates[[1]]$img_id
  # subset sce to current image 
  cur_sce <- sce_prot[,sce_prot$ImageNumber == cur_img]
  plots <- list()
  
  for(i in names(gates)) {
    plots[[i]] <- local({
      # select gates and imageID
      cur_gate <- as.data.frame(gates[[i]]$gate)
      cur_img <- gates[[i]]$img_id
      
      # names of channels (complicated because shiny has different channel names than SCE)
      x_gate <- rownames(cur_gate)[1]
      y_gate <- rownames(cur_gate)[2]
      x_metal <- panel_meta_prot[panel_meta_prot$clean_target == x_gate, "Metal.Tag"]
      y_metal <- panel_meta_prot[panel_meta_prot$clean_target == y_gate, "Metal.Tag"]
      x_original <- rownames(rowData(cur_sce)[rowData(cur_sce)[,"Metal.Tag"] == x_metal,])
      y_original <- rownames(rowData(cur_sce)[rowData(cur_sce)[,"Metal.Tag"] == y_metal,])
      
      # select current channels
      cur_counts <- as.data.frame(t(assay(cur_sce, "asinh")[c(x_original, y_original),]))
      
      # check if cells are in gate
      cur_counts$in_gate <- ifelse(cur_counts[,1] >= cur_gate[1,1] &
                                     cur_counts[,1] <= cur_gate[1,2] &
                                     cur_counts[,2] >= cur_gate[2,1] &
                                     cur_counts[,2] <= cur_gate[2,2], TRUE, FALSE)
      
      # cellIDs of cells in gate (for next gate)
      cur_cells <- rownames(cur_counts[cur_counts$in_gate == TRUE,])
      
      # update cur_sce
      cur_sce <<- cur_sce[,cur_sce$cellID %in% cur_cells] ####### !!!!
      
      # plot
      p <- ggplot() +
        geom_point_rast(data=cur_counts, aes(x=cur_counts[,1], y=cur_counts[,2], 
                                        color=ifelse(cur_counts[,"in_gate"] == TRUE, "red", "black")),
                   alpha=ifelse(cur_counts[,"in_gate"] == TRUE, 0.5,0.1)) +
        geom_rect(data = cur_gate, aes(xmin=cur_gate[1,1], xmax=cur_gate[1,2], ymin=cur_gate[2,1],
                                       ymax=cur_gate[2,2]),
                  color="black", alpha=0.2) +
        xlab(x_gate) +
        ylab(y_gate) +
        scale_color_identity() +
        theme_bw() +
        theme(legend.position = "none") +
        coord_cartesian(xlim = c(0,6.5), ylim = c(0,6.5))
      p
    })
  }
  
  n <- length(plots)
  nCol <- floor(sqrt(n))
  do.call("grid.arrange", c(plots, ncol=3,nrow=4, top=cur_celltype))
}

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Supp Figure 5C

Select one random file per celltype and load file

# load all subsetted sce object from hierarchichal gating and combine the
label.files <- list.files("data/data_for_analysis/rna/celltype_classifier", full.names = TRUE)
file_names <- data.frame(path = label.files)
file_names$fileName <- sub(".*/", "", label.files)
file_names$celltype <- sub("\\_.*", "", file_names$fileName)

# select one random file per celltype
file_names <- file_names %>%
  group_by(celltype) %>%
  sample_n(1)

# Read in SCE objects
cur_sces <- lapply(file_names$path, readRDS)