Last updated: 2021-02-04

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Knit directory: melanoma_publication_old_data/

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Introduction

Here, we define chemokine-expressing cells

Preparations

knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())

Load libraries

source("code/helper_functions/detect_mRNA_expression.R")
source("code/helper_functions/validityChecks.R")
library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(scater)
library(CATALYST)
library(reshape2)
library(LSD)
library(data.table)
library(ComplexHeatmap)
library(corrplot)
library(pheatmap)
library(grid)
library(gridExtra)
library(tidyr)
library(colorRamps)

Load the single cell experiment object and the image metadata

sce <- readRDS(file = "data/sce_RNA.rds")

Detect Chemokine Expressing Cells

Detection of chemokine expressing cells

for the detection of chemokine expressing cells we make use of the fact that we also measured a negative control (DapB).

# get the names of the chemokine channels without the negative control channel
chemokine_channels = rownames(sce[which(grepl("T\\d+_",rownames(sce)) & ! grepl("DapB",rownames(sce))),])

# run function to define chemokine expressing cells 
output_list <- compute_difference(sce, 
                          cellID = "cellID", 
                          assay_name = "asinh", 
                          threshold = 0.01, 
                          mRNA_channels = chemokine_channels, 
                          negative_control = "T6_DapB", 
                          return_calc_metrics = TRUE)

# overwrite SCE object
sce <- output_list$output_sce

Plot results from chemokine detection

# check difference between DapB and signal (histogram)
for(i in chemokine_channels){
  
  # subset whole data set for visualization purposes
  diff_chemo <- output_list[[i]]
  diff_chemo_sub <- diff_chemo[sample(nrow(diff_chemo), nrow(diff_chemo)*0.1), ]
  
  a = ggplot(data = diff_chemo_sub, aes(x=diff)) + 
  geom_histogram(binwidth = 0.01) + 
  xlab(paste("DapB mRNA signal subtracted from", i, sep = " ")) + ggtitle("Raw distribution") + 
    theme(plot.title = element_text(hjust = 0.5, size = 35), 
          axis.title = element_text(size = 25), 
          axis.text =  element_text(size = 15))

  b = ggplot(data = diff_chemo_sub, aes(x=scaled_diff)) + 
  geom_histogram(binwidth = 0.1, aes(fill = 
                                       ifelse(padj <= 0.01 & scaled_diff > 0, 'p<0.01', 'n.s.'))) + 
  xlab(paste("DapB mRNA signal subtracted from", i, sep = " ")) + ggtitle("Scaled distribution") + 
    labs(fill = "legend") +
    xlim(-5,7) +
    scale_fill_manual(values = c("black", "deepskyblue1")) +
        theme(plot.title = element_text(hjust = 0.5, size = 35), 
          axis.title = element_text(size = 25), 
          legend.text = element_text(size = 20), 
          legend.title = element_text(size=20),
          axis.text =  element_text(size = 15))
  
  # significant cells defined by subtraction
  c = ggplot(data=diff_chemo_sub, aes(x=mean_negative_control, y=mean_chemokine)) + 
    geom_point(alpha=0.2, aes(col = 
                                ifelse(padj <= 0.01 & scaled_diff > 0, 'p<0.01', 'n.s.'))) + 
    scale_color_manual(values = c("black", "deepskyblue1")) +
    xlim(0,5.5) + ylim(0,5.5) +
    ylab(paste("Mean expression of", i, sep=" ")) +
    xlab("Mean DapB mRNAexpression") +
    ggtitle(paste("DapB mRNA vs.", i, sep = " ")) + 
    theme(plot.title = element_text(hjust = 0.5, size = 35), 
          axis.title = element_text(size = 25), 
          legend.position = "none",
          axis.text =  element_text(size = 15))
  #png(file = paste("~/Daniel_volume/Rout_RNA/chemokine_detection_method_comparison/",i,"difference_distribution_BH_0.01.png", sep="_"), height = 1000, width = 1600)
  grid.arrange(a,b,c, nrow = 3, ncol=1)
  #dev.off()
}
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Basic Stats

Basic numbers on the chemokine expressing cells

chemokines <- data.frame(colData(sce))
chemokines <- chemokines[, chemokine_channels]

# calculate the amount of cells that are positive for 1, 2 and multiple combinations. exclude column containing the ids (12)
single_combinations = chemokines[rowSums(chemokines[,-1]) == 1,-1]

# number of single chemokines positive cells 
nrow(single_combinations)
[1] 45168
double_combinations = chemokines[rowSums(chemokines[,-1]) == 2,-1]
# number of single chemokines positive cells 
nrow(double_combinations)
[1] 8656
multiple_combinations = chemokines[rowSums(chemokines[,-1]) >= 3,-1]
# number of cells that express 3 or more chemokines
nrow(multiple_combinations)
[1] 2427
# number of double positives per chemokine
double_counts <- colSums(double_combinations)

# frequency matrix and corrplot for frequency matrix
double_combinations[double_combinations == 0] <- NA
count_matrix = psych::pairwiseCount(x=double_combinations)

# normalize the frequency matrix by the amount of double combinations that occur for each chemokine
frequency_matrix <- count_matrix
for (i in colnames(count_matrix)){
  frequency_matrix[,i] <- frequency_matrix[,i]/double_counts[i]
}

Frequency double-positive cells

The next plot shows the frequencies of all double positive cell occurences. e.g. of all T4_CXCL10 expressing cells that also express another chemokine more than 50% express T9_CXCL9.

corrplot(frequency_matrix, is.corr = FALSE, tl.col = 'black', method = 'pie', type = 'full', 
         tl.srt = 45, tl.cex = 0.8, tl.offset = 0.5, cl.length = 2, cl.cex = 1, cl.align.text = "l", cl.ratio = 0.3,
         diag=TRUE, order = "hclust")

Corrplot of Frequency matrix

Now we normalize the numbers of double positives by the numbers of all respective positive chemokines. this shows that usually between 20-40 percent of chemokine expressing cells are double positive expressors.

single_counts <- colSums(single_combinations)
frequency_matrix <- count_matrix
for (i in colnames(count_matrix)){
  frequency_matrix[,i] <- frequency_matrix[,i]/single_counts[i]
}

corrplot(frequency_matrix, is.corr = FALSE, tl.col = 'black', method = 'pie', type = 'full', 
         tl.srt = 45, tl.cex = 0.8, tl.offset = 0.5, cl.length = 2, cl.cex = 1, cl.align.text = "l", cl.ratio = 0.3,
         cl.lim = c(0,1), diag=TRUE, order = "hclust")

SCE object

Add data to SCE object

# general chemokine producer tag for every cell (logical binary)
sce$chemokine <- ifelse(rowSums(data.frame(colData(sce)[,chemokine_channels])) > 0, TRUE, FALSE)

# rename colData entry names
idx <- match(chemokine_channels, colnames(colData(sce)))
for(i in idx){
  colnames(colData(sce))[i] <- strsplit(colnames(colData(sce))[i], split = "_")[[1]][2]
}

Add expressor info and colour_vector (with control samples)

cur_df <- colData(sce)
cur_df <- as_tibble(cur_df)
cur_df <- cur_df[,grepl("CCL|CXCL|Dap",colnames(cur_df))]

for(i in colnames(cur_df)){
  cur_df[[i]] <- ifelse(cur_df[[i]]== 1,i,"NA")
}

cur_df <- cur_df %>%
  unite(expressor,sep = "_",na.rm =TRUE,remove=FALSE)

cur_df$expressor <- gsub("NA_","",cur_df$expressor)
cur_df$expressor <- gsub("_NA","",cur_df$expressor)

# summary table of all combinations
summary_cur_df <- table(cur_df$expressor)

# order table according to abundance of combinations
summary_cur_df<- summary_cur_df[order(-as.numeric(summary_cur_df))]

# combinations with more than 600 occurrences
targets <- names(which(summary_cur_df > 600))
targets <- targets[!targets == "NA"]

# add expressor info to sce object
sce$expressor <- cur_df$expressor

# add target names to metadata
metadata(sce)$chemokines_morethan600_withcontrol <- targets

Save SCE object

saveRDS(object = sce,file = "data/sce_RNA.rds")

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] colorRamps_2.3              tidyr_1.1.2                
 [3] gridExtra_2.3               pheatmap_1.0.12            
 [5] corrplot_0.84               ComplexHeatmap_2.4.3       
 [7] data.table_1.13.6           LSD_4.1-0                  
 [9] reshape2_1.4.4              CATALYST_1.12.2            
[11] scater_1.16.2               ggplot2_3.3.3              
[13] dplyr_1.0.2                 SingleCellExperiment_1.12.0
[15] SummarizedExperiment_1.20.0 Biobase_2.50.0             
[17] GenomicRanges_1.42.0        GenomeInfoDb_1.26.2        
[19] IRanges_2.24.1              S4Vectors_0.28.1           
[21] BiocGenerics_0.36.0         MatrixGenerics_1.2.0       
[23] matrixStats_0.57.0          workflowr_1.6.2            

loaded via a namespace (and not attached):
  [1] readxl_1.3.1                circlize_0.4.12            
  [3] drc_3.0-1                   plyr_1.8.6                 
  [5] igraph_1.2.6                ConsensusClusterPlus_1.52.0
  [7] splines_4.0.3               flowCore_2.0.1             
  [9] BiocParallel_1.22.0         TH.data_1.0-10             
 [11] digest_0.6.27               htmltools_0.5.0            
 [13] viridis_0.5.1               magrittr_2.0.1             
 [15] CytoML_2.0.5                cluster_2.1.0              
 [17] openxlsx_4.2.3              RcppParallel_5.0.2         
 [19] sandwich_3.0-0              flowWorkspace_4.0.6        
 [21] cytolib_2.0.3               jpeg_0.1-8.1               
 [23] colorspace_2.0-0            ggrepel_0.9.0              
 [25] haven_2.3.1                 xfun_0.20                  
 [27] crayon_1.3.4                RCurl_1.98-1.2             
 [29] jsonlite_1.7.2              hexbin_1.28.2              
 [31] graph_1.66.0                survival_3.2-7             
 [33] zoo_1.8-8                   glue_1.4.2                 
 [35] gtable_0.3.0                nnls_1.4                   
 [37] zlibbioc_1.36.0             XVector_0.30.0             
 [39] GetoptLong_1.0.5            DelayedArray_0.16.0        
 [41] ggcyto_1.16.0               car_3.0-10                 
 [43] BiocSingular_1.4.0          Rgraphviz_2.32.0           
 [45] shape_1.4.5                 abind_1.4-5                
 [47] scales_1.1.1                mvtnorm_1.1-1              
 [49] Rcpp_1.0.5                  plotrix_3.7-8              
 [51] viridisLite_0.3.0           tmvnsim_1.0-2              
 [53] clue_0.3-58                 foreign_0.8-81             
 [55] rsvd_1.0.3                  FlowSOM_1.20.0             
 [57] tsne_0.1-3                  RColorBrewer_1.1-2         
 [59] ellipsis_0.3.1              farver_2.0.3               
 [61] pkgconfig_2.0.3             XML_3.99-0.5               
 [63] labeling_0.4.2              tidyselect_1.1.0           
 [65] rlang_0.4.10                later_1.1.0.1              
 [67] munsell_0.5.0               cellranger_1.1.0           
 [69] tools_4.0.3                 generics_0.1.0             
 [71] ggridges_0.5.3              evaluate_0.14              
 [73] stringr_1.4.0               yaml_2.2.1                 
 [75] knitr_1.30                  fs_1.5.0                   
 [77] zip_2.1.1                   purrr_0.3.4                
 [79] nlme_3.1-151                RBGL_1.64.0                
 [81] whisker_0.4                 xml2_1.3.2                 
 [83] compiler_4.0.3              rstudioapi_0.13            
 [85] beeswarm_0.2.3              curl_4.3                   
 [87] png_0.1-7                   tibble_3.0.4               
 [89] stringi_1.5.3               forcats_0.5.0              
 [91] lattice_0.20-41             Matrix_1.3-2               
 [93] psych_2.0.12                vctrs_0.3.6                
 [95] pillar_1.4.7                lifecycle_0.2.0            
 [97] GlobalOptions_0.1.2         BiocNeighbors_1.6.0        
 [99] cowplot_1.1.1               bitops_1.0-6               
[101] irlba_2.3.3                 httpuv_1.5.4               
[103] R6_2.5.0                    latticeExtra_0.6-29        
[105] promises_1.1.1              RProtoBufLib_2.0.0         
[107] rio_0.5.16                  vipor_0.4.5                
[109] codetools_0.2-18            MASS_7.3-53                
[111] gtools_3.8.2                rprojroot_2.0.2            
[113] rjson_0.2.20                withr_2.3.0                
[115] mnormt_2.0.2                multcomp_1.4-15            
[117] GenomeInfoDbData_1.2.4      hms_0.5.3                  
[119] ncdfFlow_2.34.0             rmarkdown_2.6              
[121] DelayedMatrixStats_1.10.1   carData_3.0-4              
[123] Rtsne_0.15                  git2r_0.28.0               
[125] base64enc_0.1-3             ggbeeswarm_0.6.0