Last updated: 2024-08-21

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Knit directory: CCL19_FRCs_lung_cancer/

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Rmd 3d8b8f0 Pchryssa 2024-08-21 Comparison NSCLC tonsils
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Rmd 0b8c110 Pchryssa 2024-08-21 NSCLC vs tonsils

Load packages

suppressPackageStartupMessages({
  library(here)
  library(purrr)
  library(dplyr)
  library(stringr)
  library(patchwork)
  library(Seurat)
  library(Matrix)
  library(dittoSeq)
  library(gridExtra)
  library(gsubfn)
  library(ggsci)
  library(bigmds)
  library(tidyverse)
})

Comparison between NSCLC CCL19⁺ FRCs and Tonsilar FRCs

Set directory

basedir <- here()

Read NSCLC CCL19⁺ TRC PRC data

NCLS_FRCS <- readRDS(paste0(basedir,"/data/Human/NSCLC_CCL19_TRC_PRC_CAFs.rds"))

Read Tonsilar FRC data

Tons_FRC_data <-readRDS(paste0(basedir,"/data/Human/mergedHumanTonsilExtendedDataset_incAcuteTonsilitis_mapped_wocl11+12+14_seuratFRC.rds"))

Define color palette

cols<- pal_igv()(51)
names(cols) <- c(0:50)

Tonsilar FRCs (Supplementary Figure 2I)

Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_1"] <- paste0("ACTA2", expression("\U207A"),"PRC_1")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_2"] <- paste0("ACTA2", expression("\U207A"),"PRC_2")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_3"] <- paste0("ACTA2", expression("\U207A"),"PRC_3")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_4"] <- paste0("ACTA2", expression("\U207A"),"PRC_4")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "ACTA2+PRC_5"] <- paste0("ACTA2", expression("\U207A"),"PRC_5")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "FDC_6"] <- "FDC"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_10"] <- paste0("PI16", expression("\U207A"),"RC_1")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_11"] <- paste0("PI16", expression("\U207A"),"RC_2")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "PI16+RC_12"] <- paste0("PI16", expression("\U207A"),"RC_3")
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_7"] <- "TRC_1"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_8"] <- "TRC_2"
Tons_FRC_data@meta.data$clusterLabel[Tons_FRC_data@meta.data$clusterLabel == "TRC_9"] <- "TRC_3"

colDataset <- cols[3:15]
names(colDataset) <- unique(Tons_FRC_data$clusterLabel)

DimPlot(Tons_FRC_data, reduction = "umap", group.by = "clusterLabel",cols=colDataset)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.grid.major = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2") + ggtitle("Tonsilar FRCs (De Martin et al 2023)")

Merge NSCLC TRC and PRC with Tonsilar FRCs

NCLS_FRCS$Disease_short <-rep("NSCLC",nrow(NCLS_FRCS@meta.data))
Tons_FRC_data$Disease_short <-rep("Tonsil",nrow(Tons_FRC_data@meta.data))
colnames(Tons_FRC_data@meta.data)[names(Tons_FRC_data@meta.data) == 'clusterLabel'] <- 'cell_type'

data_merge <- merge(NCLS_FRCS, y = c(Tons_FRC_data),
             add.cell.ids = c("NCLS_FRCS","Tons_FRC_data"),
             project = "merge_nsclc_tonsils")

#Preprocessing
resolution <- c(0.1, 0.25, 0.4, 0.6,0.8, 1.)
data_merge  <- preprocessing(data_merge,resolution)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33594
Number of edges: 1098322

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9641
Number of communities: 6
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33594
Number of edges: 1098322

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9342
Number of communities: 12
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33594
Number of edges: 1098322

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9153
Number of communities: 13
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33594
Number of edges: 1098322

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8977
Number of communities: 17
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33594
Number of edges: 1098322

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8821
Number of communities: 21
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 33594
Number of edges: 1098322

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8698
Number of communities: 27
Elapsed time: 5 seconds

Save merged NSCLC CCL19⁺ TRC PRC and Tonsilar FRC data

#saveRDS(data_merge, paste0(basedir,"/data/Human/NSCLC_Ccl19_tonsil_merged.rds"))

Integrate data to correct for batch effects due to different tissues via seurat

Step 1

obj.list <-SplitObject(data_merge, split.by = 'cell_type')
#For each object in list we see to run normalization and identify highly variable features
for (i in 1:length(obj.list)){
  #Normalization
  obj.list[[i]] <- NormalizeData(obj.list[[i]], normalization.method = "LogNormalize", scale.factor = 10000) 
  #Find high variable genes
  obj.list[[i]] <- FindVariableFeatures(obj.list[[i]], selection.method = "vst", nfeatures = 2000)
}

Step 2

#select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = obj.list)
#Find anchors to integrate the data across different patients (Canonical correlation analysis)
anchors <- FindIntegrationAnchors(object.list = obj.list, anchor.features = features)
# Create an 'integrated' data assay
seurat_integrated <- IntegrateData(anchorset = anchors)

Step 3

# We run a single integrated analysis on all cells!
DefaultAssay(seurat_integrated) <- "integrated"

# Run the standard workflow for visualization and clustering
seurat_integrated <- ScaleData(seurat_integrated, verbose = FALSE)
seurat_integrated <- RunPCA(object = seurat_integrated, npcs = 30, verbose = FALSE,seed.use = 8734) 
seurat_integrated <- RunTSNE(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
seurat_integrated<- RunUMAP(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
seurat_integrated <- FindNeighbors(object = seurat_integrated, reduction = "pca", dims = 1:20, seed.use = 8734)
#Clustering 
resolution <- c(0.1, 0.25, 0.4, 0.6,0.8, 1.,1.2,1.4,1.8)
for(k in 1:length(resolution)){
  seurat_integrated <- FindClusters(object = seurat_integrated, resolution = resolution[k], random.seed = 8734)
}

Apply Divide-and-conquer MDS algorithm proposed by Delicado P. and C. Pachón-García (2021) for fast MDS computation due to large dataset size

DefaultAssay(seurat_integrated) <-'integrated'

#MDS computation
mds <- divide_conquer_mds(x = t(GetAssayData(seurat_integrated, slot = 'scale.data')), l = 200, c_points = 5 * 2, r = 2, n_cores = 1)$points
colnames(mds) <- paste0("MDSDIVCONQ_", 1:2)

# Store MDS representation as a custom dimensional reduction field
seurat_integrated[['mds_div_conq']] <- CreateDimReducObject(embeddings = mds, key = 'MDSDIVCONQ_', assay = DefaultAssay(seurat_integrated))

Multidimensional scaling (MDS) plot

Multidimensional scaling (MDS) visualizes the level of similarity of variables in a data set. MDS recognizes the structure of the dataset in 2D, as it maintains the pairwise distances between data points.

Due to the large size of the integrated dataset of Tonsilar and NSCLC FRCs, the classical MDS algorithm suffers from computational problems and thus MDS configuration can not be obtained. To resolve this issue, we used the Divide-and-conquer MDS algorithm proposed by Delicado P. and C. Pachón-García (2021) for large data sets from the bigmds R package.

In the MDS plot:

  1. Positions of cells are based on the calculated MDS representation
  2. Positions of points in the final plot are calculated by taking the mean Gaussian kernel of coordinates of cells in a group

Gaussian kernel function flexibly measures the similarity between data points in a high-dimensional space, given its ability to capture complex relationships that may not be linear or easily separable in the original feature space. When calculating Euclidean distance, the value increases with distance, thus the kernel function weights these observations accordingly.

Please note that we provide the integrated object with the MDS representation given that it takes some time to be generated.

Read integrated object with MDS representation

seurat_integrated <- readRDS(paste0(basedir,"/data/Human/Tonsil_Ccl19_TRC_PRC_final_mds.rds"))

Visualize MDS plot (Supplementary Figure 2J)

mds_tx_condition <- seurat_integrated@reductions$mds_div_conq@cell.embeddings %>% 
as.data.frame() %>% cbind(tx = seurat_integrated@meta.data$Disease_short)

mds_tx_celltype <- seurat_integrated@reductions$mds_div_conq@cell.embeddings %>% 
as.data.frame() %>% cbind(tx = seurat_integrated@meta.data$cell_type)

mds_tx_TOTAL <- merge(mds_tx_condition, mds_tx_celltype, by=c("MDSDIVCONQ_1", "MDSDIVCONQ_2"), all.x=T, all.y=T)
colnames(mds_tx_TOTAL) <-c("MDS_1", "MDS_2", "Condition","Celltype")

#Color palette
colDataset <- cols[1:15]
names(colDataset) <- unique(seurat_integrated$cell_type)

# Use mean gaussian kernel
mds_tx_TOTAL_gk <- mds_tx_TOTAL %>% 
  group_by(Celltype,Condition) %>% 
  mutate(count_mds1 = mean(GK(MDS_1))) %>%
  mutate(count_mds2 = mean(GK(MDS_2)))

ggplot(mds_tx_TOTAL_gk, aes(x=count_mds1, y=count_mds2, color=Celltype, shape = Condition)) + geom_point(stroke = 1.5) + ylab("MDS2") + xlab("MDS1") + coord_cartesian(xlim = c(0, max(mds_tx_TOTAL_gk$count_mds1,mds_tx_TOTAL_gk$count_mds2)), ylim = c(0, max(mds_tx_TOTAL_gk$count_mds1,mds_tx_TOTAL_gk$count_mds2)) ) +
scale_color_manual(values=colDataset) + scale_shape_manual(values = c(2, 3)) + 
  theme(aspect.ratio = 2,axis.line = element_line(colour = "black"),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    panel.background = element_blank(),
    axis.text.y = element_text(angle = 0, vjust = 0.5,colour = "black",size = 10),
    axis.text.x = element_text(angle = 0, vjust = 0.5,colour = "black",size = 10)) 

Session info

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Zurich
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.2    forcats_1.0.0      readr_2.1.4        tidyverse_2.0.0   
 [5] bigmds_3.0.0       ggsci_3.0.0        gsubfn_0.7         proto_1.0.0       
 [9] gridExtra_2.3      dittoSeq_1.12.1    ggplot2_3.4.2      Matrix_1.6-0      
[13] SeuratObject_4.1.3 Seurat_4.3.0.1     patchwork_1.1.2    stringr_1.5.0     
[17] dplyr_1.1.2        purrr_1.0.1        here_1.0.1         magrittr_2.0.3    
[21] circlize_0.4.15    tidyr_1.3.0        tibble_3.2.1       workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.21            splines_4.3.1              
  [3] later_1.3.1                 bitops_1.0-7               
  [5] polyclip_1.10-4             lifecycle_1.0.3            
  [7] tcltk_4.3.1                 rprojroot_2.0.3            
  [9] globals_0.16.2              processx_3.8.2             
 [11] lattice_0.21-8              MASS_7.3-60                
 [13] plotly_4.10.2               sass_0.4.7                 
 [15] rmarkdown_2.23              jquerylib_0.1.4            
 [17] yaml_2.3.7                  httpuv_1.6.11              
 [19] sctransform_0.3.5           sp_2.0-0                   
 [21] spatstat.sparse_3.0-2       reticulate_1.36.1          
 [23] cowplot_1.1.1               pbapply_1.7-2              
 [25] RColorBrewer_1.1-3          abind_1.4-5                
 [27] zlibbioc_1.46.0             Rtsne_0.16                 
 [29] GenomicRanges_1.52.0        BiocGenerics_0.46.0        
 [31] RCurl_1.98-1.12             pracma_2.4.4               
 [33] git2r_0.33.0                GenomeInfoDbData_1.2.10    
 [35] IRanges_2.34.1              S4Vectors_0.38.1           
 [37] ggrepel_0.9.3               svd_0.5.5                  
 [39] irlba_2.3.5.1               listenv_0.9.0              
 [41] spatstat.utils_3.1-0        pheatmap_1.0.12            
 [43] goftest_1.2-3               spatstat.random_3.1-5      
 [45] fitdistrplus_1.1-11         parallelly_1.36.0          
 [47] leiden_0.4.3                codetools_0.2-19           
 [49] DelayedArray_0.28.0         tidyselect_1.2.0           
 [51] shape_1.4.6                 farver_2.1.1               
 [53] matrixStats_1.0.0           stats4_4.3.1               
 [55] spatstat.explore_3.2-1      jsonlite_1.8.7             
 [57] ellipsis_0.3.2              progressr_0.13.0           
 [59] ggridges_0.5.4              survival_3.5-5             
 [61] systemfonts_1.0.4           tools_4.3.1                
 [63] ragg_1.2.5                  ica_1.0-3                  
 [65] Rcpp_1.0.11                 glue_1.6.2                 
 [67] SparseArray_1.2.4           xfun_0.39                  
 [69] MatrixGenerics_1.12.3       GenomeInfoDb_1.36.1        
 [71] withr_2.5.0                 fastmap_1.1.1              
 [73] fansi_1.0.4                 callr_3.7.3                
 [75] digest_0.6.33               timechange_0.2.0           
 [77] R6_2.5.1                    mime_0.12                  
 [79] textshaping_0.3.6           colorspace_2.1-0           
 [81] scattermore_1.2             tensor_1.5                 
 [83] spatstat.data_3.0-1         utf8_1.2.3                 
 [85] generics_0.1.3              data.table_1.14.8          
 [87] httr_1.4.6                  htmlwidgets_1.6.2          
 [89] S4Arrays_1.2.1              whisker_0.4.1              
 [91] uwot_0.1.16                 pkgconfig_2.0.3            
 [93] gtable_0.3.3                lmtest_0.9-40              
 [95] SingleCellExperiment_1.22.0 XVector_0.40.0             
 [97] htmltools_0.5.5             scales_1.2.1               
 [99] Biobase_2.60.0              png_0.1-8                  
[101] knitr_1.43                  rstudioapi_0.15.0          
[103] tzdb_0.4.0                  reshape2_1.4.4             
[105] nlme_3.1-162                cachem_1.0.8               
[107] zoo_1.8-12                  GlobalOptions_0.1.2        
[109] KernSmooth_2.23-22          parallel_4.3.1             
[111] miniUI_0.1.1.1              pillar_1.9.0               
[113] grid_4.3.1                  vctrs_0.6.3                
[115] RANN_2.6.1                  promises_1.2.0.1           
[117] xtable_1.8-4                cluster_2.1.4              
[119] evaluate_0.21               cli_3.6.1                  
[121] compiler_4.3.1              rlang_1.1.1                
[123] crayon_1.5.2                future.apply_1.11.0        
[125] labeling_0.4.2              ps_1.7.5                   
[127] getPass_0.2-4               plyr_1.8.8                 
[129] fs_1.6.3                    stringi_1.7.12             
[131] viridisLite_0.4.2           deldir_1.0-9               
[133] munsell_0.5.0               lazyeval_0.2.2             
[135] spatstat.geom_3.2-4         hms_1.1.3                  
[137] future_1.33.0               shiny_1.7.4.1              
[139] highr_0.10                  SummarizedExperiment_1.30.2
[141] ROCR_1.0-11                 igraph_1.5.0.1             
[143] bslib_0.5.0                
date()
[1] "Wed Aug 21 12:49:07 2024"

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Zurich
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] lubridate_1.9.2    forcats_1.0.0      readr_2.1.4        tidyverse_2.0.0   
 [5] bigmds_3.0.0       ggsci_3.0.0        gsubfn_0.7         proto_1.0.0       
 [9] gridExtra_2.3      dittoSeq_1.12.1    ggplot2_3.4.2      Matrix_1.6-0      
[13] SeuratObject_4.1.3 Seurat_4.3.0.1     patchwork_1.1.2    stringr_1.5.0     
[17] dplyr_1.1.2        purrr_1.0.1        here_1.0.1         magrittr_2.0.3    
[21] circlize_0.4.15    tidyr_1.3.0        tibble_3.2.1       workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.21            splines_4.3.1              
  [3] later_1.3.1                 bitops_1.0-7               
  [5] polyclip_1.10-4             lifecycle_1.0.3            
  [7] tcltk_4.3.1                 rprojroot_2.0.3            
  [9] globals_0.16.2              processx_3.8.2             
 [11] lattice_0.21-8              MASS_7.3-60                
 [13] plotly_4.10.2               sass_0.4.7                 
 [15] rmarkdown_2.23              jquerylib_0.1.4            
 [17] yaml_2.3.7                  httpuv_1.6.11              
 [19] sctransform_0.3.5           sp_2.0-0                   
 [21] spatstat.sparse_3.0-2       reticulate_1.36.1          
 [23] cowplot_1.1.1               pbapply_1.7-2              
 [25] RColorBrewer_1.1-3          abind_1.4-5                
 [27] zlibbioc_1.46.0             Rtsne_0.16                 
 [29] GenomicRanges_1.52.0        BiocGenerics_0.46.0        
 [31] RCurl_1.98-1.12             pracma_2.4.4               
 [33] git2r_0.33.0                GenomeInfoDbData_1.2.10    
 [35] IRanges_2.34.1              S4Vectors_0.38.1           
 [37] ggrepel_0.9.3               svd_0.5.5                  
 [39] irlba_2.3.5.1               listenv_0.9.0              
 [41] spatstat.utils_3.1-0        pheatmap_1.0.12            
 [43] goftest_1.2-3               spatstat.random_3.1-5      
 [45] fitdistrplus_1.1-11         parallelly_1.36.0          
 [47] leiden_0.4.3                codetools_0.2-19           
 [49] DelayedArray_0.28.0         tidyselect_1.2.0           
 [51] shape_1.4.6                 farver_2.1.1               
 [53] matrixStats_1.0.0           stats4_4.3.1               
 [55] spatstat.explore_3.2-1      jsonlite_1.8.7             
 [57] ellipsis_0.3.2              progressr_0.13.0           
 [59] ggridges_0.5.4              survival_3.5-5             
 [61] systemfonts_1.0.4           tools_4.3.1                
 [63] ragg_1.2.5                  ica_1.0-3                  
 [65] Rcpp_1.0.11                 glue_1.6.2                 
 [67] SparseArray_1.2.4           xfun_0.39                  
 [69] MatrixGenerics_1.12.3       GenomeInfoDb_1.36.1        
 [71] withr_2.5.0                 fastmap_1.1.1              
 [73] fansi_1.0.4                 callr_3.7.3                
 [75] digest_0.6.33               timechange_0.2.0           
 [77] R6_2.5.1                    mime_0.12                  
 [79] textshaping_0.3.6           colorspace_2.1-0           
 [81] scattermore_1.2             tensor_1.5                 
 [83] spatstat.data_3.0-1         utf8_1.2.3                 
 [85] generics_0.1.3              data.table_1.14.8          
 [87] httr_1.4.6                  htmlwidgets_1.6.2          
 [89] S4Arrays_1.2.1              whisker_0.4.1              
 [91] uwot_0.1.16                 pkgconfig_2.0.3            
 [93] gtable_0.3.3                lmtest_0.9-40              
 [95] SingleCellExperiment_1.22.0 XVector_0.40.0             
 [97] htmltools_0.5.5             scales_1.2.1               
 [99] Biobase_2.60.0              png_0.1-8                  
[101] knitr_1.43                  rstudioapi_0.15.0          
[103] tzdb_0.4.0                  reshape2_1.4.4             
[105] nlme_3.1-162                cachem_1.0.8               
[107] zoo_1.8-12                  GlobalOptions_0.1.2        
[109] KernSmooth_2.23-22          parallel_4.3.1             
[111] miniUI_0.1.1.1              pillar_1.9.0               
[113] grid_4.3.1                  vctrs_0.6.3                
[115] RANN_2.6.1                  promises_1.2.0.1           
[117] xtable_1.8-4                cluster_2.1.4              
[119] evaluate_0.21               cli_3.6.1                  
[121] compiler_4.3.1              rlang_1.1.1                
[123] crayon_1.5.2                future.apply_1.11.0        
[125] labeling_0.4.2              ps_1.7.5                   
[127] getPass_0.2-4               plyr_1.8.8                 
[129] fs_1.6.3                    stringi_1.7.12             
[131] viridisLite_0.4.2           deldir_1.0-9               
[133] munsell_0.5.0               lazyeval_0.2.2             
[135] spatstat.geom_3.2-4         hms_1.1.3                  
[137] future_1.33.0               shiny_1.7.4.1              
[139] highr_0.10                  SummarizedExperiment_1.30.2
[141] ROCR_1.0-11                 igraph_1.5.0.1             
[143] bslib_0.5.0