Last updated: 2022-02-10

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Introduction

This script detects interaction networks of a given celltype (here: B cells) and defines these networks as clusters. Once a cluster is defined, an algorithm screens the neighbourhood of those clusters to identify cells within/surrounding a cluster. These cells are defined as the community of a cluster.

Load packages and helper functions

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(ggplot2)
library(scater)
library(viridis)
library(igraph)
library(CATALYST)
library(reshape2)
library(cowplot)
library(ggridges)
library(tidyverse)
library(viridis)
library(dplyr)
library(cytomapper)
library(concaveman)
library(data.table)
library(sf)
library(ggbeeswarm)
library(RANN)

Load data

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

Find patches of B cells and their milieus

A B cell cluster is defined by at least 20 adjacent B cells (Bcell and BnTcell, max distance of 15µm between them). A milieu is defined by all cells within a cluster and the proximity (enlarging distance = 15µm)

sce_prot$bcell_patch <- NULL
sce_prot$bcell_milieu <- NULL
sce_prot$bcell_patch_score <- NULL

start = Sys.time()

# quantiles of cell radius
quantile(sqrt(sce_prot[,sce_prot$celltype %in% c("B cell")]$Area/pi))
      0%      25%      50%      75%     100% 
1.128379 2.820948 3.385138 3.908820 9.097284 
# find B cell clusters
sce_prot <- findPatch(sce_prot, sce_prot[,colData(sce_prot)$celltype %in% c("B cell", "BnT cell")]$cellID, 
                    'cellID', 'Center_X', 'Center_Y', 'ImageNumber', 
                    distance = 15, min_clust_size = 10, output_colname = "bcell_patch")
Time difference of 10.30734 mins
[1] "patches successfully added to sce object"
# number of B cell clusters
length(unique(sce_prot$bcell_patch))
[1] 375
# define cells within/surrounding a cluster of B cells
sce_prot <- findMilieu(sce_prot, 
                     'cellID', 'Center_X', 'Center_Y', 'ImageNumber', 'bcell_patch', 
                     distance = 30, output_colname = "bcell_milieu")
Time difference of 2.779587 mins
[1] "milieus successfully added to sce object"
# number of chemokine communities
length(unique(sce_prot$bcell_milieu))
[1] 375
end = Sys.time()
print(end-start)
Time difference of 13.098 mins

Plot some patches

example <- findPatch(sce_prot[,sce_prot$Description %in% c("D4")], sce_prot[,sce_prot$celltype %in% c("B cell", "BnT cell")]$cellID, 
                    'cellID', 
                    'Center_X', 'Center_Y', 
                    'ImageNumber', 
                    distance = 15, 
                    min_clust_size = 10,
                    output_colname = "example_patch")
Time difference of 0.4352202 secs
[1] "patches successfully added to sce object"
example <- findMilieu(example, 
              'cellID', 
              'Center_X', 'Center_Y', 
              'ImageNumber', 
              'example_patch', 
              distance = 30,
              output_colname = "example_milieu",
              plot = TRUE)
Time difference of 1.118382 secs
[1] "milieus successfully added to sce object"

Save SCE object

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] RANN_2.6.1                  ggbeeswarm_0.6.0           
 [3] sf_1.0-5                    data.table_1.14.2          
 [5] concaveman_1.1.0            cytomapper_1.6.0           
 [7] EBImage_4.36.0              forcats_0.5.1              
 [9] stringr_1.4.0               purrr_0.3.4                
[11] readr_2.1.2                 tidyr_1.2.0                
[13] tibble_3.1.6                tidyverse_1.3.1            
[15] ggridges_0.5.3              cowplot_1.1.1              
[17] reshape2_1.4.4              CATALYST_1.18.1            
[19] igraph_1.2.11               viridis_0.6.2              
[21] viridisLite_0.4.0           scater_1.22.0              
[23] scuttle_1.4.0               ggplot2_3.3.5              
[25] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[27] Biobase_2.54.0              GenomicRanges_1.46.1       
[29] GenomeInfoDb_1.30.1         IRanges_2.28.0             
[31] S4Vectors_0.32.3            BiocGenerics_0.40.0        
[33] MatrixGenerics_1.6.0        matrixStats_0.61.0         
[35] dplyr_1.0.7                 workflowr_1.7.0            

loaded via a namespace (and not attached):
  [1] scattermore_0.7             flowWorkspace_4.6.0        
  [3] knitr_1.37                  irlba_2.3.5                
  [5] multcomp_1.4-18             DelayedArray_0.20.0        
  [7] RCurl_1.98-1.5              doParallel_1.0.16          
  [9] generics_0.1.2              flowCore_2.6.0             
 [11] ScaledMatrix_1.2.0          terra_1.5-17               
 [13] callr_3.7.0                 TH.data_1.1-0              
 [15] proxy_0.4-26                ggpointdensity_0.1.0       
 [17] tzdb_0.2.0                  xml2_1.3.3                 
 [19] lubridate_1.8.0             httpuv_1.6.5               
 [21] assertthat_0.2.1            xfun_0.29                  
 [23] hms_1.1.1                   jquerylib_0.1.4            
 [25] evaluate_0.14               promises_1.2.0.1           
 [27] fansi_1.0.2                 dbplyr_2.1.1               
 [29] readxl_1.3.1                Rgraphviz_2.38.0           
 [31] DBI_1.1.2                   htmlwidgets_1.5.4          
 [33] ellipsis_0.3.2              ggcyto_1.22.0              
 [35] ggnewscale_0.4.5            ggpubr_0.4.0               
 [37] backports_1.4.1             V8_4.0.0                   
 [39] cytolib_2.6.1               svgPanZoom_0.3.4           
 [41] RcppParallel_5.1.5          sparseMatrixStats_1.6.0    
 [43] vctrs_0.3.8                 abind_1.4-5                
 [45] withr_2.4.3                 ggforce_0.3.3              
 [47] aws.signature_0.6.0         svglite_2.0.0              
 [49] cluster_2.1.2               crayon_1.4.2               
 [51] drc_3.0-1                   units_0.7-2                
 [53] pkgconfig_2.0.3             tweenr_1.0.2               
 [55] vipor_0.4.5                 rlang_1.0.0                
 [57] lifecycle_1.0.1             sandwich_3.0-1             
 [59] modelr_0.1.8                rsvd_1.0.5                 
 [61] cellranger_1.1.0            rprojroot_2.0.2            
 [63] polyclip_1.10-0             graph_1.72.0               
 [65] tiff_0.1-11                 Matrix_1.4-0               
 [67] raster_3.5-15               carData_3.0-5              
 [69] Rhdf5lib_1.16.0             zoo_1.8-9                  
 [71] reprex_2.0.1                base64enc_0.1-3            
 [73] beeswarm_0.4.0              whisker_0.4                
 [75] GlobalOptions_0.1.2         processx_3.5.2             
 [77] pheatmap_1.0.12             png_0.1-7                  
 [79] rjson_0.2.21                bitops_1.0-7               
 [81] shinydashboard_0.7.2        getPass_0.2-2              
 [83] KernSmooth_2.23-20          rhdf5filters_1.6.0         
 [85] ConsensusClusterPlus_1.58.0 DelayedMatrixStats_1.16.0  
 [87] classInt_0.4-3              shape_1.4.6                
 [89] jpeg_0.1-9                  rstatix_0.7.0              
 [91] ggsignif_0.6.3              aws.s3_0.3.21              
 [93] beachmat_2.10.0             scales_1.1.1               
 [95] magrittr_2.0.2              plyr_1.8.6                 
 [97] hexbin_1.28.2               zlibbioc_1.40.0            
 [99] compiler_4.1.2              RColorBrewer_1.1-2         
[101] plotrix_3.8-2               clue_0.3-60                
[103] cli_3.1.1                   XVector_0.34.0             
[105] ncdfFlow_2.40.0             ps_1.6.0                   
[107] FlowSOM_2.2.0               MASS_7.3-55                
[109] tidyselect_1.1.1            stringi_1.7.6              
[111] RProtoBufLib_2.6.0          highr_0.9                  
[113] yaml_2.2.2                  BiocSingular_1.10.0        
[115] locfit_1.5-9.4              latticeExtra_0.6-29        
[117] ggrepel_0.9.1               grid_4.1.2                 
[119] sass_0.4.0                  tools_4.1.2                
[121] parallel_4.1.2              CytoML_2.6.0               
[123] circlize_0.4.13             rstudioapi_0.13            
[125] foreach_1.5.2               git2r_0.29.0               
[127] gridExtra_2.3               farver_2.1.0               
[129] Rtsne_0.15                  digest_0.6.29              
[131] shiny_1.7.1                 Rcpp_1.0.8                 
[133] car_3.0-12                  broom_0.7.12               
[135] later_1.3.0                 httr_1.4.2                 
[137] ComplexHeatmap_2.10.0       colorspace_2.0-2           
[139] rvest_1.0.2                 XML_3.99-0.8               
[141] fs_1.5.2                    splines_4.1.2              
[143] RBGL_1.70.0                 sp_1.4-6                   
[145] systemfonts_1.0.3           xtable_1.8-4               
[147] jsonlite_1.7.3              R6_2.5.1                   
[149] pillar_1.7.0                htmltools_0.5.2            
[151] mime_0.12                   nnls_1.4                   
[153] glue_1.6.1                  fastmap_1.1.0              
[155] BiocParallel_1.28.3         BiocNeighbors_1.12.0       
[157] fftwtools_0.9-11            class_7.3-20               
[159] codetools_0.2-18            mvtnorm_1.1-3              
[161] utf8_1.2.2                  lattice_0.20-45            
[163] bslib_0.3.1                 curl_4.3.2                 
[165] colorRamps_2.3              gtools_3.9.2               
[167] survival_3.2-13             rmarkdown_2.11             
[169] munsell_0.5.0               e1071_1.7-9                
[171] rhdf5_2.38.0                GetoptLong_1.0.5           
[173] GenomeInfoDbData_1.2.7      iterators_1.0.13           
[175] HDF5Array_1.22.1            haven_2.4.3                
[177] gtable_0.3.0