Last updated: 2022-02-10

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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
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(reshape2)
library(tidyverse)
library(dplyr)

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")

sce_rna$dysfunction_score <- NULL
sce_rna$dysfunction_density <- NULL
sce_prot$dysfunction_score <- NULL
sce_prot$dysfunction_density <- NULL

Derive Scoring

We divide the images with a high CD8 cell density into two groups: Low Dysfunction (CD8+CXCL13+ < 5%) and High Dysfunction (CD8+CXCL13+ >= 5%). Margin punches from LN are not considered.

# paste density scoring and exhaustion scoring
dysfunction <- data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>% # remove controls
  mutate(MM_location_punch = paste(MM_location, Location, sep = "_")) %>%
  filter(MM_location_punch != "LN_M") %>% # remove LN margin samples
  filter(Tcell_density_score_image %in% c("high")) %>%
  mutate(celltype2 = paste(celltype, CXCL13, sep = "_")) %>% # add CXCL13 info to celltype
  filter(celltype2 %in% c("CD8+ T cell_1", "CD8+ T cell_0")) %>%
  group_by(Description, celltype2) %>%
  summarise(n=n()) %>%
  reshape2::dcast(Description ~ celltype2, value.var = "n", fill = 0) %>%
  reshape2::melt(id.vars = c("Description"), variable.name = "celltype2", value.name = "n") %>%
  group_by(Description) %>%
  mutate(fraction = n / sum(n)) %>%
  filter(celltype2 == "CD8+ T cell_1") %>%
  ungroup() %>%
  mutate(dysfunction_score = ifelse(fraction >= median(fraction), "High Dysfunction", "Low Dysfunction")) %>%
  select(Description, dysfunction_score, fraction)

cur_rna <- data.frame(colData(sce_rna))
cur_rna <- left_join(cur_rna, dysfunction)
sce_rna$dysfunction_score <- cur_rna$dysfunction_score
sce_rna$dysfunction_density <- paste(sce_rna$Tcell_density_score_image, sce_rna$dysfunction_score, sep = " - ")

cur_prot <- data.frame(colData(sce_prot))
cur_prot <- left_join(cur_prot, dysfunction)
sce_prot$dysfunction_score <- cur_prot$dysfunction_score
sce_prot$dysfunction_density <- paste(sce_prot$Tcell_density_score_image, sce_prot$dysfunction_score, sep = " - ")

Number of Patients in different groups

data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL" & is.na(dysfunction_score) == FALSE) %>%
  distinct(PatientID, .keep_all = T) %>%
  group_by(dysfunction_score) %>%
  summarise(patients = n())
# A tibble: 2 × 2
  dysfunction_score patients
  <chr>                <int>
1 High Dysfunction         8
2 Low Dysfunction         14

Fraction of Dysfunctional Cells per Image

ggplot(dysfunction, aes(x=reorder(Description,-fraction), y=fraction)) + 
  geom_bar(stat="identity") +
  geom_hline(yintercept = 0.05) +
  xlab("Description") +
  ylab("Fraction of CXCL13+ CD8+ T cells")

median(dysfunction$fraction)
[1] 0.0581435

save SCE

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

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] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] forcats_0.5.1               stringr_1.4.0              
 [3] purrr_0.3.4                 readr_2.1.2                
 [5] tidyr_1.2.0                 tibble_3.1.6               
 [7] ggplot2_3.3.5               tidyverse_1.3.1            
 [9] reshape2_1.4.4              SingleCellExperiment_1.16.0
[11] SummarizedExperiment_1.24.0 Biobase_2.54.0             
[13] GenomicRanges_1.46.1        GenomeInfoDb_1.30.1        
[15] IRanges_2.28.0              S4Vectors_0.32.3           
[17] BiocGenerics_0.40.0         MatrixGenerics_1.6.0       
[19] matrixStats_0.61.0          dplyr_1.0.7                
[21] workflowr_1.7.0            

loaded via a namespace (and not attached):
 [1] bitops_1.0-7           fs_1.5.2               lubridate_1.8.0       
 [4] httr_1.4.2             rprojroot_2.0.2        tools_4.1.2           
 [7] backports_1.4.1        bslib_0.3.1            utf8_1.2.2            
[10] R6_2.5.1               DBI_1.1.2              colorspace_2.0-2      
[13] withr_2.4.3            tidyselect_1.1.1       processx_3.5.2        
[16] compiler_4.1.2         git2r_0.29.0           cli_3.1.1             
[19] rvest_1.0.2            xml2_1.3.3             DelayedArray_0.20.0   
[22] labeling_0.4.2         sass_0.4.0             scales_1.1.1          
[25] callr_3.7.0            digest_0.6.29          rmarkdown_2.11        
[28] XVector_0.34.0         pkgconfig_2.0.3        htmltools_0.5.2       
[31] highr_0.9              dbplyr_2.1.1           fastmap_1.1.0         
[34] rlang_1.0.0            readxl_1.3.1           rstudioapi_0.13       
[37] farver_2.1.0           jquerylib_0.1.4        generics_0.1.2        
[40] jsonlite_1.7.3         RCurl_1.98-1.5         magrittr_2.0.2        
[43] GenomeInfoDbData_1.2.7 Matrix_1.4-0           Rcpp_1.0.8            
[46] munsell_0.5.0          fansi_1.0.2            lifecycle_1.0.1       
[49] stringi_1.7.6          whisker_0.4            yaml_2.2.2            
[52] zlibbioc_1.40.0        plyr_1.8.6             grid_4.1.2            
[55] promises_1.2.0.1       crayon_1.4.2           lattice_0.20-45       
[58] haven_2.4.3            hms_1.1.1              knitr_1.37            
[61] ps_1.6.0               pillar_1.7.0           reprex_2.0.1          
[64] glue_1.6.1             evaluate_0.14          getPass_0.2-2         
[67] modelr_0.1.8           vctrs_0.3.8            tzdb_0.2.0            
[70] httpuv_1.6.5           cellranger_1.1.0       gtable_0.3.0          
[73] assertthat_0.2.1       xfun_0.29              broom_0.7.12          
[76] later_1.3.0            ellipsis_0.3.2