Last updated: 2022-02-22

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Introduction

This script gives an overview over the clinical data and generates statistics used in the manuscript.

Preparations

Load libraries

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(data.table)
library(dplyr)
library(SingleCellExperiment)
library(readr)

Load Data

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

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

Statistics

Number of Samples and Patients

# Number of Patients
data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL") %>%
  distinct(PatientID) %>%
  summarise(n=n())

   n
1 69

# Number of Samples without control samples
data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL") %>%
  distinct(Description) %>%
  summarise(n=n())

    n
1 159

# Number of Samples - Mutation
data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL") %>%
  distinct(Description, .keep_all = T) %>%
  group_by(Mutation) %>%
  summarise(n=n()) %>%
  mutate(percentage = round(n/sum(n) *100,0))

# A tibble: 6 × 3
  Mutation     n percentage
  <chr>    <int>      <dbl>
1 BRAF        71         45
2 GNAQ         2          1
3 KRAS         1          1
4 NRAS        50         31
5 unknown      7          4
6 wt          28         18

# Number of Samples - Location
data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL") %>%
  distinct(Description, .keep_all = T) %>%
  group_by(MM_location_simplified) %>%
  summarise(n=n()) %>%
  mutate(percentage = round(n/sum(n) *100,0))

# A tibble: 3 × 3
  MM_location_simplified     n percentage
  <chr>                  <int>      <dbl>
1 LN                        54         34
2 other                     29         18
3 skin                      76         48

Number of Samples per Patient

# Number of Patients
data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL") %>%
  distinct(Description, .keep_all = T) %>%
  group_by(PatientID) %>%
  summarise(n=n()) %>%
  group_by(n) %>%
  summarise(group_sizes = n())

# A tibble: 7 × 2
      n group_sizes
  <int>       <int>
1     1          14
2     2          39
3     3           7
4     4           4
5     5           1
6     6           3
7     7           1

Samples per Location

# Number Controls
data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL") %>%
  distinct(Description, .keep_all = T) %>%
  group_by(MM_location) %>%
  summarise(n=n())

# A tibble: 11 × 2
   MM_location           n
   <chr>             <int>
 1 brain                 7
 2 LN                   49
 3 LN or soft tissue     3
 4 mucosal               4
 5 parotis or LN         2
 6 skin                  4
 7 skin_cutaneous       10
 8 skin_subcutaneous    52
 9 skin_undefine        10
10 soft tissue           9
11 visceral              9

# Number Controls
data.frame(colData(sce_prot)) %>%
  filter(Location == "CTRL") %>%
  distinct(Description, .keep_all = T) %>%
  group_by(TissueType) %>%
  summarise(n=n())

# A tibble: 3 × 2
  TissueType     n
  <chr>      <int>
1 Lymphnode      2
2 PSO            2
3 Skin           3

Number of Cells in Data Sets

ncol(sce_rna)

[1] 864263

ncol(sce_prot)

[1] 989404

Number of Cells and Ablated Area

# diff in ablated area
area_prot <- data.frame(image_area = image_mat_prot$Height_FullStack * image_mat_prot$Width_FullStack)
area_prot$Description <- image_mat_prot$Metadata_Description
area_prot$data_set <- "Protein"

area_rna <- data.frame(image_area = image_mat_rna$Height_FullStack * image_mat_rna$Width_FullStack)
area_rna$Description <- image_mat_rna$Metadata_Description
area_rna$data_set <- "RNA"

image_area <- rbind(area_rna, area_prot)

# Ratio #Cells Protein/RNA
round(ncol(sce_prot) / ncol(sce_rna),2)

[1] 1.14

area <- image_area %>%
  group_by(data_set) %>%
  summarise(area_sum = sum(image_area))

# Ratio Area Protein/RNA
round(area[area$data_set == "Protein", ]$area_sum / area[area$data_set == "RNA", ]$area_sum,2)

[1] 1.07

Number of Celltypes

# Protein data
data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL") %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage=round(n/sum(n)*100,1))

# A tibble: 11 × 3
   celltype           n percentage
   <chr>          <int>      <dbl>
 1 B cell         39632        4.1
 2 BnT cell       25288        2.6
 3 CD4+ T cell    54188        5.6
 4 CD8+ T cell    52622        5.5
 5 FOXP3+ T cell   9286        1  
 6 Macrophage     62120        6.5
 7 Neutrophil     11081        1.2
 8 pDC             6693        0.7
 9 Stroma         59455        6.2
10 Tumor         622146       64.7
11 unknown        18839        2

# RNA data
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage=round(n/sum(n)*100,1))

# A tibble: 10 × 3
   celltype         n percentage
   <chr>        <int>      <dbl>
 1 CD38         10931        1.3
 2 CD8- T cell  87858       10.5
 3 CD8+ T cell  44630        5.3
 4 HLA-DR       29557        3.5
 5 Macrophage   64405        7.7
 6 Neutrophil    5018        0.6
 7 Stroma       24574        2.9
 8 Tumor       549153       65.3
 9 unknown       2972        0.4
10 Vasculature  21297        2.5

Number of Chemokine Expressing Cells

# Percentage of Chemokine Producing Cells
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  group_by(chemokine) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 2 × 3
  chemokine      n percentage
  <lgl>      <int>      <dbl>
1 FALSE     782417       93.1
2 TRUE       57978        6.9

# Chemokines by Celltype
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(chemokine == 1) %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 10 × 3
   celltype        n percentage
   <chr>       <int>      <dbl>
 1 CD38          920        1.6
 2 CD8- T cell 12179       21  
 3 CD8+ T cell  9428       16.3
 4 HLA-DR       3202        5.5
 5 Macrophage  12675       21.9
 6 Neutrophil   1378        2.4
 7 Stroma       3913        6.7
 8 Tumor       12965       22.4
 9 unknown        45        0.1
10 Vasculature  1273        2.2

# Sum of major expressing cell types
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(chemokine == 1) %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1)) %>%
  filter(percentage > 15) %>%
  summarise(sum <- sum(percentage))

# A tibble: 1 × 1
  `sum <- sum(percentage)`
                     <dbl>
1                     81.6

# CXCL13 experssion by Tcells
# Chemokines by Celltype
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(expressor == "CXCL13") %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1)) %>%
  filter(percentage > 30) %>%
  summarise(sum <- sum(percentage))

# A tibble: 1 × 1
  `sum <- sum(percentage)`
                     <dbl>
1                     71.1

Multi Expressors

data.frame(colData(sce_rna)) %>%
  rowwise() %>%
  mutate(number_of_chemokines = length(strsplit(expressor, "_")[[1]])) %>%
  filter(Location != "CTRL") %>%
  filter(chemokine == 1) %>%
  group_by(number_of_chemokines) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 8 × 3
  number_of_chemokines     n percentage
                 <int> <int>      <dbl>
1                    1 45687       78.8
2                    2  9479       16.3
3                    3  2152        3.7
4                    4   518        0.9
5                    5   109        0.2
6                    6    22        0  
7                    7     8        0  
8                    8     3        0

CCL8 Expression

data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(CCL8 == 1) %>%
  summarise(n=n())

    n
1 862

Chemokine Expression in Tumor Cells

# Percentage of Tumor Cells that Express a Chemokine
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(celltype == "Tumor") %>%
  group_by(chemokine) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 2 × 3
  chemokine      n percentage
  <lgl>      <int>      <dbl>
1 FALSE     536188       97.6
2 TRUE       12965        2.4

# CXCL10 in Tumor cells
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(CXCL10 == 1) %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 10 × 3
   celltype        n percentage
   <chr>       <int>      <dbl>
 1 CD38           71        0.6
 2 CD8- T cell  1275       10.7
 3 CD8+ T cell  1364       11.4
 4 HLA-DR        332        2.8
 5 Macrophage   3624       30.3
 6 Neutrophil    166        1.4
 7 Stroma        212        1.8
 8 Tumor        4778       40  
 9 unknown         3        0  
10 Vasculature   117        1

data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(celltype == "Tumor") %>%
  group_by(CXCL10) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 2 × 3
  CXCL10      n percentage
   <dbl>  <int>      <dbl>
1      0 544375       99.1
2      1   4778        0.9

# CCL2 in Tumor cells
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(CCL2 == 1) %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 10 × 3
   celltype        n percentage
   <chr>       <int>      <dbl>
 1 CD38          270        2.7
 2 CD8- T cell  1198       11.9
 3 CD8+ T cell   768        7.7
 4 HLA-DR        327        3.3
 5 Macrophage   3041       30.3
 6 Neutrophil     89        0.9
 7 Stroma       1182       11.8
 8 Tumor        2872       28.6
 9 unknown         7        0.1
10 Vasculature   282        2.8

data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(celltype == "Tumor") %>%
  group_by(CCL2) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 2 × 3
   CCL2      n percentage
  <dbl>  <int>      <dbl>
1     0 546281       99.5
2     1   2872        0.5

# CXCL8 in Tumor cells
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(CXCL8 == 1) %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 10 × 3
   celltype        n percentage
   <chr>       <int>      <dbl>
 1 CD38           18        0.4
 2 CD8- T cell   133        3.1
 3 CD8+ T cell    95        2.2
 4 HLA-DR         53        1.2
 5 Macrophage    641       14.8
 6 Neutrophil   1058       24.4
 7 Stroma         69        1.6
 8 Tumor        2214       51  
 9 unknown        13        0.3
10 Vasculature    46        1.1

data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(celltype == "Tumor") %>%
  group_by(CXCL8) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 2 × 3
  CXCL8      n percentage
  <dbl>  <int>      <dbl>
1     0 546939       99.6
2     1   2214        0.4

# CXCL12 in Tumor cells
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(CXCL12 == 1) %>%
  group_by(celltype) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 10 × 3
   celltype        n percentage
   <chr>       <int>      <dbl>
 1 CD38          340        4.3
 2 CD8- T cell  1445       18.3
 3 CD8+ T cell   681        8.6
 4 HLA-DR        255        3.2
 5 Macrophage   1287       16.3
 6 Neutrophil     14        0.2
 7 Stroma       2373       30  
 8 Tumor        1060       13.4
 9 unknown         3        0  
10 Vasculature   453        5.7

data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  filter(celltype == "Tumor") %>%
  group_by(CXCL12) %>%
  summarise(n=n()) %>%
  mutate(percentage = round((n / sum(n)) *100,1))

# A tibble: 2 × 3
  CXCL12      n percentage
   <dbl>  <int>      <dbl>
1      0 548093       99.8
2      1   1060        0.2

Chemokine Expression in Locations

# overall chemokine expression
data.frame(colData(sce_rna)) %>%
  filter(Location == "CTRL") %>%
  group_by(chemokine, TissueType) %>%
  summarise(n=n()) %>%
  reshape2::dcast(TissueType ~ chemokine, value.var = "n", fill = 0) %>%
  mutate(percentage_expressing = round(`TRUE` / (`FALSE`+`TRUE`) * 100,1))

  TissueType FALSE TRUE percentage_expressing
1  Lymphnode 16949 1001                   5.6
2        PSO  2524  264                   9.5
3       Skin  3097   33                   1.1

# CXCL13 expression in control samples
data.frame(colData(sce_rna)) %>%
  filter(Location == "CTRL") %>%
  group_by(CXCL13, TissueType) %>%
  summarise(n=n()) %>%
  reshape2::dcast(TissueType ~ CXCL13, value.var = "n", fill = 0) %>%
  mutate(percentage_expressing = round(`1` / (`0`+`1`) * 100,1))

  TissueType     0   1 percentage_expressing
1  Lymphnode 17316 634                   3.5
2        PSO  2787   1                   0.0
3       Skin  3130   0                   0.0

# CXCL13 expression in tumor samples
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  group_by(CXCL13, MM_location) %>%
  summarise(n=n()) %>%
  reshape2::dcast(MM_location ~ CXCL13, value.var = "n", fill = 0) %>%
  mutate(percentage_expressing = round(`1` / (`0`+`1`) * 100,1))

         MM_location      0    1 percentage_expressing
1              brain  28923  922                   3.1
2                 LN 288382 5157                   1.8
3  LN or soft tissue  21060  148                   0.7
4            mucosal  20428   34                   0.2
5      parotis or LN  12698   23                   0.2
6               skin   9656   72                   0.7
7     skin_cutaneous  64397   74                   0.1
8  skin_subcutaneous 268021 1582                   0.6
9      skin_undefine  38163  508                   1.3
10       soft tissue  38483  159                   0.4
11          visceral  41479   26                   0.1

# CXCL10 expression in control samples
data.frame(colData(sce_rna)) %>%
  filter(Location == "CTRL") %>%
  group_by(CXCL10, TissueType) %>%
  summarise(n=n()) %>%
  reshape2::dcast(TissueType ~ CXCL10, value.var = "n", fill = 0) %>%
  mutate(percentage_expressing = round(`1` / (`0`+`1`) * 100,1))

  TissueType     0  1 percentage_expressing
1  Lymphnode 17916 34                   0.2
2        PSO  2782  6                   0.2
3       Skin  3130  0                   0.0

# CXCL10 expression in tumor samples
data.frame(colData(sce_rna)) %>%
  filter(Location != "CTRL") %>%
  group_by(CXCL10, MM_location) %>%
  summarise(n=n()) %>%
  reshape2::dcast(MM_location ~ CXCL10, value.var = "n", fill = 0) %>%
  mutate(percentage_expressing = round(`1` / (`0`+`1`) * 100,1))

         MM_location      0    1 percentage_expressing
1              brain  29336  509                   1.7
2                 LN 289761 3778                   1.3
3  LN or soft tissue  20929  279                   1.3
4            mucosal  20405   57                   0.3
5      parotis or LN  12714    7                   0.1
6               skin   9681   47                   0.5
7     skin_cutaneous  64227  244                   0.4
8  skin_subcutaneous 264588 5015                   1.9
9      skin_undefine  36915 1756                   4.5
10       soft tissue  38399  243                   0.6
11          visceral  41498    7                   0.0

Fisher Test for Bcell patches and Dysfunction

# no control samples
cont_table <- data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL") %>%
  distinct(Description,.keep_all = T) %>%
  filter(is.na(dysfunction_score) == FALSE) %>%
  group_by(bcell_patch_score, dysfunction_score) %>%
  summarise(n=n()) %>%
  filter(bcell_patch_score %in% c("Small B cell Patches", "B cell Follicles")) %>%
  reshape2::dcast(bcell_patch_score ~ dysfunction_score, value.var = "n", fill = 0)

fisher.test(cont_table[,-1])


    Fisher's Exact Test for Count Data

data:  cont_table[, -1]
p-value = 0.01522
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
    1.310455 1242.440939
sample estimates:
odds ratio 
  18.80249

# no LN samples
cont_table <- data.frame(colData(sce_prot)) %>%
  filter(Location != "CTRL" & MM_location_simplified != "LN") %>%
  distinct(Description,.keep_all = T) %>%
  filter(is.na(dysfunction_score) == FALSE) %>%
  group_by(bcell_patch_score, dysfunction_score) %>%
  summarise(n=n()) %>%
  filter(bcell_patch_score %in% c("Small B cell Patches", "B cell Follicles")) %>%
  reshape2::dcast(bcell_patch_score ~ dysfunction_score, value.var = "n", fill = 0)

fisher.test(cont_table[,-1])


    Fisher's Exact Test for Count Data

data:  cont_table[, -1]
p-value = 0.1667
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
 0.2538029       Inf
sample estimates:
odds ratio 
       Inf

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] readr_2.1.2                 SingleCellExperiment_1.16.0
 [3] SummarizedExperiment_1.24.0 Biobase_2.54.0             
 [5] GenomicRanges_1.46.1        GenomeInfoDb_1.30.1        
 [7] IRanges_2.28.0              S4Vectors_0.32.3           
 [9] BiocGenerics_0.40.0         MatrixGenerics_1.6.0       
[11] matrixStats_0.61.0          data.table_1.14.2          
[13] dplyr_1.0.7                 workflowr_1.7.0            

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8             lattice_0.20-45        getPass_0.2-2         
 [4] ps_1.6.0               assertthat_0.2.1       rprojroot_2.0.2       
 [7] digest_0.6.29          utf8_1.2.2             plyr_1.8.6            
[10] R6_2.5.1               evaluate_0.14          httr_1.4.2            
[13] pillar_1.7.0           zlibbioc_1.40.0        rlang_1.0.0           
[16] rstudioapi_0.13        whisker_0.4            callr_3.7.0           
[19] jquerylib_0.1.4        Matrix_1.4-0           rmarkdown_2.11        
[22] stringr_1.4.0          RCurl_1.98-1.5         DelayedArray_0.20.0   
[25] compiler_4.1.2         httpuv_1.6.5           xfun_0.29             
[28] pkgconfig_2.0.3        htmltools_0.5.2        tidyselect_1.1.1      
[31] tibble_3.1.6           GenomeInfoDbData_1.2.7 fansi_1.0.2           
[34] tzdb_0.2.0             crayon_1.4.2           later_1.3.0           
[37] bitops_1.0-7           grid_4.1.2             jsonlite_1.7.3        
[40] lifecycle_1.0.1        DBI_1.1.2              git2r_0.29.0          
[43] magrittr_2.0.2         cli_3.1.1              stringi_1.7.6         
[46] reshape2_1.4.4         XVector_0.34.0         fs_1.5.2              
[49] promises_1.2.0.1       bslib_0.3.1            ellipsis_0.3.2        
[52] generics_0.1.2         vctrs_0.3.8            tools_4.1.2           
[55] glue_1.6.1             purrr_0.3.4            hms_1.1.1             
[58] processx_3.5.2         fastmap_1.1.0          yaml_2.2.2            
[61] knitr_1.37             sass_0.4.0

Summary Statistics for hte Manuscript

toobiwankenobi

2020-10-26