Last updated: 2021-02-18

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Knit directory: melanoma_publication_old_data/

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Introduction

In this script we try to set global thresholds for some key markers (CD3 etc) and then sub-cluster the respective cells with markers that we know are expressed on those cells.

Preparations

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

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
value   ?                                
visible 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                                  
sapply(list.files("/Volumes/server_homes/daniels/Git/imcRtools/R/", full.names = TRUE), source)
named list()
library(LSD)
library(SingleCellExperiment)
library(ggplot2)
library(scater)
library(viridis)
library(Rphenograph)
library(igraph)
library(CATALYST)
library(workflowr)
library(gridExtra)
library(dplyr)

Load data

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

Marker for Clustering

Define Superclasses for subclustering

# store labels from randomForest separately
sce$celltype_rf <- sce$celltype

# assign superclasses
sce[,sce$celltype_rf %in% c("exhausted Tcytotoxic", "Tcytotoxic")]$celltype <- "CD8+ T cell"
sce[,sce$celltype_rf %in% c("CD134+ Tcell", "CD134- Tcell")]$celltype <- "CD8- T cell"

Definition of markers used for subclustering the classified cells

marker_list <- list()

marker_list$Stroma <- c("SMA", "CK5", "Cadherin11", "FAP","pRB","B2M","GLUT1","T5_CCL4","T7_CCL18","CCR2","T1_CXCL8",
                        "T4_CXCL10","T8_CXCL13","T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","CD31",
                        "T10_CCL19","cleavedPARP")

marker_list$Tumor <- c("HLADR", "S100","B2M","GLUT1","PDL1","SOX10","T5_CCL4","T7_CCL18","CCR2","T1_CXCL8","T4_CXCL10","T8_CXCL13",
                       "T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP",
                       "Mart1","pRB")

marker_list$Neutrophil <- c("CD15", "PDL1","GLUT1", "MPO","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
                       "T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")


marker_list$`CD8- T cell` <- c("CD3", "CD8", "PD1", "Lag3", "CCR2","CD38","PDL1", "HLADR", "CD134","T5_CCL4", "B2M", "CD31",
                       "T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13","T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9",
                       "T11_CCL8","T10_CCL19","cleavedPARP", "pRB")

marker_list$`CD8+ T cell` <- c("CD3", "CD8", "PD1", "Lag3", "CCR2","CD38","PDL1", "HLADR", "CD134","T5_CCL4", "B2M", "CD31",
                       "T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13","T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9",
                       "T11_CCL8","T10_CCL19","cleavedPARP", "pRB")


marker_list$Macrophage <- c("CD68", "CD163", "HLADR", "PDL1", "CD3", "CCR2","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
                       "T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")

marker_list$Vasculature <- c("CD31", "SMA","CK5","pRB", "Cadherin11","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
                       "T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")

marker_list$`HLA-DR` <- c("HLADR","CD68","CD3","CCR2","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
                       "T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")

marker_list$CD38 <- c("HLADR","CD38","CD3","CCR2","CD163","CD68","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
                       "T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")

marker_list$unknown <- rownames(sce[rowData(sce)$good_marker,])

# check if the names of the marker list match the names of the classified cells
all(names(marker_list) %in% unique(sce$celltype))
[1] TRUE

Clustering

Sub-cluster the whole dataset with FlowSOM

FlowSOM first because it is faster

## the FlowSOM function from CATALYST needs an another column in the rowData of the sce to work properly:
rowData(sce)$marker_class <- "state"

# vector for clustering
fs_clustering <- vector(length = ncol(sce))

# Macrophage, CD8- T cell, CD8+ T cell, Tumor will be clustered for a total of 6 clustes each
set.seed(12345)

# 6 clusters
for(i in c("CD8- T cell","Macrophage","CD8+ T cell","Tumor")){
  #subset cells for clustering
  cur_sce <- sce[,sce$celltype == i]
  names(assays(cur_sce)) <- c("counts", "exprs","scaled_counts", "scaled_asinh")
  cur_sce <- CATALYST::cluster(cur_sce, features = marker_list[i][[1]], ydim = 2, xdim = 3, maxK = 4)
  fs_clustering[sce$celltype == i] <- cur_sce$cluster_id
}

# 4 clusters
for(i in c("Neutrophil","CD38","Stroma","Vasculature","HLA-DR", "unknown")){
  cur_sce <- sce[,sce$celltype == i]
  names(assays(cur_sce)) <-  c("counts", "exprs","scaled_counts", "scaled_asinh")
  cur_sce <- CATALYST::cluster(cur_sce, features = marker_list[i][[1]], ydim = 2, xdim = 2, maxK = 3)
  fs_clustering[sce$celltype == i] <- cur_sce$cluster_id
}

# Save in SCE object
colData(sce)$celltype_clustered <- as.factor(fs_clustering)

sce$celltype_clustered <- paste0(sce$celltype, "_", sce$celltype_clustered)

Visualization

Visulalize clustering results for FlowSOM

# Aggregate the counts
mean_sce <- calculateSummary(sce, split_by = c("celltype_clustered", "celltype"), 
                             exprs_values = "counts")
# Exclude bad markers
mean_sce <- mean_sce[rowData(sce)$good_marker,]

# Transform and scale
assay(mean_sce, "arcsinh") <- asinh(assay(mean_sce, "meanCounts")) 
assay(mean_sce, "arcsinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))

# Scaled
plotHeatmap(mean_sce, exprs_values = "arcsinh_scaled", 
            colour_columns_by = "celltype", 
            labels_col = mean_sce$celltype_clustered,
            show_colnames = TRUE, annotation_legend = TRUE, borders_color = NA,
            color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
            zlim = c(-4, 4),legend = TRUE)

Numbers of cells per cluster

celltype_counts <- sce$celltype_clustered

table(celltype_counts)
celltype_counts
       CD38_1        CD38_2        CD38_3        CD38_4 CD8- T cell_1 
         1703          2323          3115          4049         24011 
CD8- T cell_2 CD8- T cell_3 CD8- T cell_4 CD8- T cell_5 CD8- T cell_6 
        14807         13230         12678         24222          8924 
CD8+ T cell_1 CD8+ T cell_2 CD8+ T cell_3 CD8+ T cell_4 CD8+ T cell_5 
         6013          7272          5321         10216          5908 
CD8+ T cell_6      HLA-DR_1      HLA-DR_2      HLA-DR_3      HLA-DR_4 
        10474         13176          4636          5941         12700 
 Macrophage_1  Macrophage_2  Macrophage_3  Macrophage_4  Macrophage_5 
         7513         13380         11788         13106          8945 
 Macrophage_6  Neutrophil_1  Neutrophil_2  Neutrophil_3  Neutrophil_4 
        10696           622          1223          1701          1508 
     Stroma_1      Stroma_2      Stroma_3      Stroma_4       Tumor_1 
         2325          3956          7841         10868         72776 
      Tumor_2       Tumor_3       Tumor_4       Tumor_5       Tumor_6 
        94020         69388         89337        108154        119002 
    unknown_1     unknown_2     unknown_3     unknown_4 Vasculature_1 
         1869           549           452           313         10360 
Vasculature_2 Vasculature_3 Vasculature_4 
         5568          4860          1424 

Cluster Names

Assign names to clusters

annotations <- sce$celltype_clustered

# add annotation if wanted
#annotations[annotations == "Tumor_1"] <- "Tumor1_"
#annotations[annotations == "Tumor_2"] <- "Tumor2_"
#annotations[annotations == "Tumor_3"] <- "Tumor3_"
#annotations[annotations == "Tumor_4"] <- "Tumor4_"
#annotations[annotations == "Tumor_5"] <- "Tumor5_"
#annotations[annotations == "Tumor_6"] <- "Tumor6_"

sce$cellAnnotation <- annotations

mean_sce <- calculateSummary(sce, split_by = c("celltype_clustered","celltype"), 
                             exprs_values = "counts")

# Exclude bad markers
mean_sce <- mean_sce[rowData(sce)$good_marker,]

# Transform and scale
assay(mean_sce, "arcsinh") <- asinh(assay(mean_sce, "meanCounts")) 
assay(mean_sce, "arcsinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))

plotHeatmap(mean_sce, exprs_values = "arcsinh_scaled", 
            colour_columns_by = c("celltype"), 
            labels_col = mean_sce$celltype,
            show_colnames = TRUE, annotation_legend = TRUE, borders_color = NA,
            color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
            zlim = c(-4, 4),legend = TRUE)

Clustering Quality Control

Marker expression density of clusters vs. manually gated - CD8+ T cell

# Density of Expression in different CD8+ T cell clusters vs. manually gated Lag3+ cells
cur_sce <- data.frame(colData(sce))
cur_exprs <- data.frame(t(assays(sce)[[2]]))
cur_exprs <- cbind(cur_exprs, cur_sce[,c("celltype", "celltype_rf", "celltype_clustered")])
cur_exprs$cellID <- rownames(cur_exprs)

cur_exprs <- cur_exprs %>%
  reshape2::melt(id.vars = c("cellID", "celltype", "celltype_rf", "celltype_clustered"), 
                 variable.name = "marker", value.name = "asinh")

# clustering
a <- cur_exprs %>%
  filter(celltype == "CD8+ T cell" & marker %in% marker_list$`CD8+ T cell`) %>%
  ggplot(data=., aes(asinh, color = celltype_clustered)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)

# manual gating
b <- cur_exprs %>%
  filter(celltype == "CD8+ T cell" & marker %in% marker_list$`CD8+ T cell`) %>%
  ggplot(data=., aes(asinh, color = celltype_rf)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)

grid.arrange(a, b, ncol = 2)
Warning: Removed 17847 rows containing non-finite values (stat_density).

Warning: Removed 17847 rows containing non-finite values (stat_density).

CD8- T cell

# clustering
a <- cur_exprs %>%
  filter(celltype == "CD8- T cell" & marker %in% marker_list$`CD8- T cell`) %>%
  ggplot(data=., aes(asinh, color = celltype_clustered)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)

# manual gating
b <- cur_exprs %>%
  filter(celltype == "CD8- T cell" & marker %in% marker_list$`CD8- T cell`) %>%
  ggplot(data=., aes(asinh, color = celltype_rf)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)

grid.arrange(a, b, ncol = 2)
Warning: Removed 33619 rows containing non-finite values (stat_density).

Warning: Removed 33619 rows containing non-finite values (stat_density).

Tumor

# clustering
cur_exprs %>%
  filter(celltype == "Tumor" & marker %in% marker_list$Tumor) %>%
  ggplot(data=., aes(asinh, color = celltype_clustered)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)
Warning: Removed 124307 rows containing non-finite values (stat_density).

Neutrophil

# clustering
cur_exprs %>%
  filter(celltype == "Neutrophil" & marker %in% marker_list$Neutrophil) %>%
  ggplot(data=., aes(asinh, color = celltype_clustered)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)
Warning: Removed 1029 rows containing non-finite values (stat_density).

Macrophage

# clustering
cur_exprs %>%
  filter(celltype == "Macrophage" & marker %in% marker_list$Macrophage) %>%
  ggplot(data=., aes(asinh, color = celltype_clustered)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)
Warning: Removed 47900 rows containing non-finite values (stat_density).

CD38

# clustering
cur_exprs %>%
  filter(celltype == "CD38" & marker %in% marker_list$CD38) %>%
  ggplot(data=., aes(asinh, color = celltype_clustered)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)
Warning: Removed 2477 rows containing non-finite values (stat_density).

unknown

# clustering
cur_exprs %>%
  filter(celltype == "unknown" & marker %in% marker_list$unknown) %>%
  ggplot(data=., aes(asinh, color = celltype_clustered)) + 
  geom_density() +
  xlim(0,4) + 
  facet_wrap(~marker)
Warning: Removed 36 rows containing non-finite values (stat_density).

SCE object

Save the single cell object

saveRDS(sce, "data/data_for_analysis/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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] dplyr_1.0.2                 gridExtra_2.3              
 [3] CATALYST_1.12.2             igraph_1.2.6               
 [5] Rphenograph_0.99.1.9003     viridis_0.5.1              
 [7] viridisLite_0.3.0           scater_1.16.2              
 [9] ggplot2_3.3.3               SingleCellExperiment_1.12.0
[11] SummarizedExperiment_1.20.0 Biobase_2.50.0             
[13] GenomicRanges_1.42.0        GenomeInfoDb_1.26.2        
[15] IRanges_2.24.1              S4Vectors_0.28.1           
[17] BiocGenerics_0.36.0         MatrixGenerics_1.2.0       
[19] matrixStats_0.57.0          LSD_4.1-0                  
[21] 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] ConsensusClusterPlus_1.52.0 splines_4.0.3              
  [7] flowCore_2.0.1              BiocParallel_1.22.0        
  [9] TH.data_1.0-10              digest_0.6.27              
 [11] htmltools_0.5.0             magrittr_2.0.1             
 [13] CytoML_2.0.5                cluster_2.1.0              
 [15] openxlsx_4.2.3              ComplexHeatmap_2.4.3       
 [17] RcppParallel_5.0.2          sandwich_3.0-0             
 [19] flowWorkspace_4.0.6         cytolib_2.0.3              
 [21] jpeg_0.1-8.1                colorspace_2.0-0           
 [23] ggrepel_0.9.0               haven_2.3.1                
 [25] xfun_0.20                   crayon_1.3.4               
 [27] RCurl_1.98-1.2              jsonlite_1.7.2             
 [29] hexbin_1.28.2               graph_1.66.0               
 [31] survival_3.2-7              zoo_1.8-8                  
 [33] glue_1.4.2                  gtable_0.3.0               
 [35] nnls_1.4                    zlibbioc_1.36.0            
 [37] XVector_0.30.0              GetoptLong_1.0.5           
 [39] DelayedArray_0.16.0         ggcyto_1.16.0              
 [41] car_3.0-10                  BiocSingular_1.4.0         
 [43] Rgraphviz_2.32.0            shape_1.4.5                
 [45] abind_1.4-5                 scales_1.1.1               
 [47] pheatmap_1.0.12             mvtnorm_1.1-1              
 [49] Rcpp_1.0.5                  plotrix_3.7-8              
 [51] clue_0.3-58                 foreign_0.8-81             
 [53] rsvd_1.0.3                  FlowSOM_1.20.0             
 [55] tsne_0.1-3                  RColorBrewer_1.1-2         
 [57] ellipsis_0.3.1              farver_2.0.3               
 [59] pkgconfig_2.0.3             XML_3.99-0.5               
 [61] labeling_0.4.2              reshape2_1.4.4             
 [63] tidyselect_1.1.0            rlang_0.4.10               
 [65] later_1.1.0.1               munsell_0.5.0              
 [67] cellranger_1.1.0            tools_4.0.3                
 [69] generics_0.1.0              ggridges_0.5.3             
 [71] evaluate_0.14               stringr_1.4.0              
 [73] yaml_2.2.1                  knitr_1.30                 
 [75] fs_1.5.0                    zip_2.1.1                  
 [77] purrr_0.3.4                 RANN_2.6.1                 
 [79] RBGL_1.64.0                 whisker_0.4                
 [81] xml2_1.3.2                  compiler_4.0.3             
 [83] rstudioapi_0.13             beeswarm_0.2.3             
 [85] curl_4.3                    png_0.1-7                  
 [87] tibble_3.0.4                stringi_1.5.3              
 [89] forcats_0.5.0               lattice_0.20-41            
 [91] Matrix_1.3-2                vctrs_0.3.6                
 [93] pillar_1.4.7                lifecycle_0.2.0            
 [95] GlobalOptions_0.1.2         BiocNeighbors_1.6.0        
 [97] data.table_1.13.6           cowplot_1.1.1              
 [99] bitops_1.0-6                irlba_2.3.3                
[101] httpuv_1.5.4                R6_2.5.0                   
[103] latticeExtra_0.6-29         promises_1.1.1             
[105] RProtoBufLib_2.0.0          rio_0.5.16                 
[107] vipor_0.4.5                 codetools_0.2-18           
[109] MASS_7.3-53                 gtools_3.8.2               
[111] rprojroot_2.0.2             rjson_0.2.20               
[113] withr_2.3.0                 multcomp_1.4-15            
[115] GenomeInfoDbData_1.2.4      hms_0.5.3                  
[117] ncdfFlow_2.34.0             grid_4.0.3                 
[119] rmarkdown_2.6               DelayedMatrixStats_1.10.1  
[121] carData_3.0-4               Rtsne_0.15                 
[123] git2r_0.28.0                base64enc_0.1-3            
[125] ggbeeswarm_0.6.0