Last updated: 2024-11-05

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Knit directory: CCL19_FRCs_lung_cancer/

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File Version Author Date Message
Rmd c8d516d Pchryssa 2024-11-05 Correct figure ordering
html e715b19 Pchryssa 2024-09-23 Build site.
Rmd 2776d81 Pchryssa 2024-09-23 Modify figure order
html b4a14d5 Pchryssa 2024-08-21 Build site.
Rmd 07a0991 Pchryssa 2024-08-21 NSCLC TILS

Load packages

suppressPackageStartupMessages({
  library(here)
  library(purrr)
  library(stringr)
  library(patchwork)
  library(Seurat)
  library(Matrix)
  library(dittoSeq)
  library(gridExtra)
  library(dplyr)
})

Infiltrating lymphocytes in NSCLC

Set directory

basedir <- here()

Read NSCLC TIL data

NSCLS_TIL_data <-readRDS(paste0(basedir,"/data/Human/NSCLC_TILs.rds"))

NSCLC infiltrating lymphocytes (Supplementary Figure 3C)

umap

#Define color palet
palet <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C","#4DAF4A","#377EB8","#A65628","#222F75","#FB9A99")
names(palet) <- c("TRC","PRC","AdvFB" ,"SMC/PC",  paste0("CD4", "\u207A ", "T cells"),"B cells", "Regulatory T cells" ,paste0("Cycling CD8", "\u207A ", "T cells"),paste0("CD8", "\u207A ", "T cells"))

palet <- palet[names(palet) %in% unique(NSCLS_TIL_data$cell_type)]

DimPlot(NSCLS_TIL_data, reduction = "umap", group.by = "cell_type",cols = palet)+
  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("NSCLC infiltrating lymphocytes")

Version Author Date
80d46cf Pchryssa 2024-08-26

CD4

gg <- FeaturePlot(NSCLS_TIL_data, reduction = "umap", 
          features =  get_full_gene_name("CD4",NSCLS_TIL_data)[4],
          cols=c("lightgrey", "darkred"),
          order = T,combin = FALSE)

 
gg[[1]]$labels$title <- "CD4"
gg

Version Author Date
80d46cf Pchryssa 2024-08-26

CD8A

gg <-FeaturePlot(NSCLS_TIL_data, reduction = "umap", 
          features =  get_full_gene_name("CD8A",NSCLS_TIL_data),
          cols=c("lightgrey", "darkred"),
          order = T,combin = FALSE)

gg[[1]]$labels$title <- "CD8A"
gg

Version Author Date
80d46cf Pchryssa 2024-08-26

CD79A

gg <-FeaturePlot(NSCLS_TIL_data, reduction = "umap", 
          features =  get_full_gene_name("CD79A",NSCLS_TIL_data),
          cols=c("lightgrey", "darkred"),
          order = T,combin = FALSE) 

gg[[1]]$labels$title <- "CD79A"
gg

Version Author Date
80d46cf Pchryssa 2024-08-26

Read NSCLS CCL19⁺ FRCs and NSCLS TILs

merged_data <-readRDS(paste0(basedir,"/data/Human/NSCLC_TILs_SI3.rds"))

CD8⁺ T cell subsets in NSCLC

NCLS_TIL_FRC_cd8 <- subset(merged_data, cell_type %in% c(paste0("CD8", "\u207A ", "T cells"), paste0("Cycling CD8", "\u207A ", "T cells")))

#Preprocessing
resolution <- c(0.1, 0.25, 0.4, 0.6,0.7, 0.8, 0.9, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0)
NCLS_TIL_FRC_cd8 <- FindVariableFeatures(NCLS_TIL_FRC_cd8, selection.method = "vst", nfeatures = 2000)
NCLS_TIL_FRC_cd8 <- ScaleData(NCLS_TIL_FRC_cd8)
NCLS_TIL_FRC_cd8 <- RunPCA(object = NCLS_TIL_FRC_cd8, npcs = 30, verbose = FALSE,seed.use = 8734)
NCLS_TIL_FRC_cd8 <- RunTSNE(object = NCLS_TIL_FRC_cd8, reduction = "pca", dims = 1:20, seed.use = 8734)
NCLS_TIL_FRC_cd8 <- RunUMAP(object = NCLS_TIL_FRC_cd8, reduction = "pca", dims = 1:20, seed.use = 8734)
NCLS_TIL_FRC_cd8 <- FindNeighbors(object = NCLS_TIL_FRC_cd8, reduction = "pca", dims = 1:20, seed.use = 8734)
for(k in 1:length(resolution)){
  NCLS_TIL_FRC_cd8 <- FindClusters(object = NCLS_TIL_FRC_cd8, resolution = resolution[k], random.seed = 8734)
}
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9314
Number of communities: 5
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8926
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8638
Number of communities: 10
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8382
Number of communities: 12
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8263
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8160
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8057
Number of communities: 14
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7955
Number of communities: 15
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7793
Number of communities: 16
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7641
Number of communities: 18
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7507
Number of communities: 21
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7374
Number of communities: 21
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 4918
Number of edges: 176244

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7254
Number of communities: 22
Elapsed time: 0 seconds

Annotation of CD8⁺ T cell populations

NCLS_TIL_FRC_cd8$cell_type <- NULL
NCLS_TIL_FRC_cd8$cell_type <--1

NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 0)] <- paste0("Stem-like/Naive CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 1)] <-paste0("Stem-like/Naive CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 2)] <- paste0("Effector-memory CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 3)] <- paste0("Effector-memory CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 4)] <-  paste0("Exhausted CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 5)] <- paste0("Effector CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 6)] <-paste0("Exhausted CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 7)] <- paste0("Exhausted CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 8)] <- paste0("Cycling CD8", "\u207A ", "T cells")
NCLS_TIL_FRC_cd8$cell_type[which(NCLS_TIL_FRC_cd8$RNA_snn_res.0.4 == 9)] <- paste0("Effector CD8", "\u207A ", "T cells")

CD8⁺ T cells in NSCLC (Figure 3D)

#Extend palet for CD8 T cell subsets
palet <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C","#F8766D","#00C08B","#7CAE00","#00B4F0","#F564E3")
names(palet) <- c("TRC","PRC","AdvFB" ,"SMC/PC",paste0("Stem-like/Naive CD8", "\u207A ", "T cells"),paste0("Exhausted CD8", "\u207A ", "T cells"), paste0("Effector-memory CD8", "\u207A ", "T cells"),paste0("Effector CD8", "\u207A ", "T cells"),paste0("Cycling CD8", "\u207A ", "T cells"))

palet <- palet[names(palet) %in% unique(NCLS_TIL_FRC_cd8$cell_type)]

DimPlot(NCLS_TIL_FRC_cd8, reduction = "umap", group.by = "cell_type", cols = palet)+
  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(paste0("CD8", "\U207A ", "T cells (NSCLC)"))

Version Author Date
80d46cf Pchryssa 2024-08-26

Save CD8⁺ T cell data

saveRDS(NCLS_TIL_FRC_cd8,paste0(basedir,"/data/Human/NSCLC_TILs_CD8_pop.rds"))

Dotplot CD8⁺ T cells (Supplementary Figure 3F)

genes <-c("CCR7","SELL","TCF7","NKG7","GZMA","GZMB","GZMM","GZMK","CX3CR1","GNLY","FCGR3A",  "KIR3DL2","KLRF1","TIGIT","CTLA4","LAG3","ZEB2","TOP2A","MKI67","PCLAF")

data_conv <-Remove_ensebl_id(NCLS_TIL_FRC_cd8) 
data_conv$cell_type <- factor(data_conv$cell_type, levels = rev(c(paste0("Cycling CD8", "\u207A ", "T cells"),
                                                      paste0("Effector CD8", "\u207A ", "T cells"),
                                                      paste0("Exhausted CD8", "\u207A ", "T cells"),
                                                      paste0("Effector-memory CD8", "\u207A ", "T cells"),
                                                      paste0("Stem-like/Naive CD8", "\u207A ", "T cells"))))
  
gg <-dittoDotPlot(data_conv, vars = genes, group.by = "cell_type", size = 4)
gg + coord_fixed(ratio=0.8) + ylab("")

Version Author Date
80d46cf Pchryssa 2024-08-26

Session info

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.9

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] dplyr_1.1.2        gridExtra_2.3      dittoSeq_1.12.1    ggplot2_3.4.2     
 [5] Matrix_1.6-0       SeuratObject_4.1.3 Seurat_4.3.0.1     patchwork_1.1.2   
 [9] stringr_1.5.0      purrr_1.0.1        here_1.0.1         magrittr_2.0.3    
[13] circlize_0.4.15    tidyr_1.3.0        tibble_3.2.1       workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.15.0          
  [3] jsonlite_1.8.7              shape_1.4.6                
  [5] spatstat.utils_3.1-0        farver_2.1.1               
  [7] rmarkdown_2.23              ragg_1.2.5                 
  [9] zlibbioc_1.46.0             GlobalOptions_0.1.2        
 [11] fs_1.6.3                    vctrs_0.6.3                
 [13] ROCR_1.0-11                 spatstat.explore_3.2-1     
 [15] RCurl_1.98-1.12             S4Arrays_1.2.1             
 [17] htmltools_0.5.5             SparseArray_1.2.4          
 [19] sass_0.4.7                  sctransform_0.3.5          
 [21] parallelly_1.36.0           KernSmooth_2.23-22         
 [23] bslib_0.5.0                 htmlwidgets_1.6.2          
 [25] ica_1.0-3                   plyr_1.8.8                 
 [27] plotly_4.10.2               zoo_1.8-12                 
 [29] cachem_1.0.8                whisker_0.4.1              
 [31] igraph_1.5.0.1              mime_0.12                  
 [33] lifecycle_1.0.3             pkgconfig_2.0.3            
 [35] R6_2.5.1                    fastmap_1.1.1              
 [37] GenomeInfoDbData_1.2.10     MatrixGenerics_1.12.3      
 [39] fitdistrplus_1.1-11         future_1.33.0              
 [41] shiny_1.7.4.1               digest_0.6.33              
 [43] colorspace_2.1-0            S4Vectors_0.38.1           
 [45] ps_1.7.5                    rprojroot_2.0.3            
 [47] tensor_1.5                  irlba_2.3.5.1              
 [49] textshaping_0.3.6           GenomicRanges_1.52.0       
 [51] labeling_0.4.2              progressr_0.13.0           
 [53] fansi_1.0.4                 spatstat.sparse_3.0-2      
 [55] httr_1.4.6                  polyclip_1.10-4            
 [57] abind_1.4-5                 compiler_4.3.1             
 [59] withr_2.5.0                 highr_0.10                 
 [61] MASS_7.3-60                 DelayedArray_0.28.0        
 [63] tools_4.3.1                 lmtest_0.9-40              
 [65] httpuv_1.6.11               future.apply_1.11.0        
 [67] goftest_1.2-3               glue_1.6.2                 
 [69] callr_3.7.3                 nlme_3.1-162               
 [71] promises_1.2.0.1            grid_4.3.1                 
 [73] Rtsne_0.16                  getPass_0.2-4              
 [75] cluster_2.1.4               reshape2_1.4.4             
 [77] generics_0.1.3              gtable_0.3.3               
 [79] spatstat.data_3.0-1         data.table_1.14.8          
 [81] XVector_0.40.0              sp_2.0-0                   
 [83] utf8_1.2.3                  BiocGenerics_0.46.0        
 [85] spatstat.geom_3.2-4         RcppAnnoy_0.0.21           
 [87] ggrepel_0.9.3               RANN_2.6.1                 
 [89] pillar_1.9.0                later_1.3.1                
 [91] splines_4.3.1               lattice_0.21-8             
 [93] survival_3.5-5              deldir_1.0-9               
 [95] tidyselect_1.2.0            SingleCellExperiment_1.22.0
 [97] miniUI_0.1.1.1              pbapply_1.7-2              
 [99] knitr_1.43                  git2r_0.33.0               
[101] IRanges_2.34.1              SummarizedExperiment_1.30.2
[103] scattermore_1.2             stats4_4.3.1               
[105] xfun_0.39                   Biobase_2.60.0             
[107] matrixStats_1.0.0           pheatmap_1.0.12            
[109] stringi_1.7.12              lazyeval_0.2.2             
[111] yaml_2.3.7                  evaluate_0.21              
[113] codetools_0.2-19            cli_3.6.1                  
[115] uwot_0.1.16                 systemfonts_1.0.4          
[117] xtable_1.8-4                reticulate_1.36.1          
[119] munsell_0.5.0               processx_3.8.2             
[121] jquerylib_0.1.4             GenomeInfoDb_1.36.1        
[123] Rcpp_1.0.11                 globals_0.16.2             
[125] spatstat.random_3.1-5       png_0.1-8                  
[127] parallel_4.3.1              ellipsis_0.3.2             
[129] bitops_1.0-7                listenv_0.9.0              
[131] viridisLite_0.4.2           scales_1.2.1               
[133] ggridges_0.5.4              crayon_1.5.2               
[135] leiden_0.4.3                rlang_1.1.1                
[137] cowplot_1.1.1              
date()
[1] "Tue Nov  5 21:44:12 2024"

sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.9

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] dplyr_1.1.2        gridExtra_2.3      dittoSeq_1.12.1    ggplot2_3.4.2     
 [5] Matrix_1.6-0       SeuratObject_4.1.3 Seurat_4.3.0.1     patchwork_1.1.2   
 [9] stringr_1.5.0      purrr_1.0.1        here_1.0.1         magrittr_2.0.3    
[13] circlize_0.4.15    tidyr_1.3.0        tibble_3.2.1       workflowr_1.7.1   

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