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 1014aa0 Pchryssa 2024-11-05 Correct figure ordering
html a302606 Pchryssa 2024-09-23 Build site.
Rmd 20be992 Pchryssa 2024-09-23 Add NSCLC and BRCA CAFss
html c767554 Pchryssa 2024-09-23 Build site.
Rmd e8aebaf Pchryssa 2024-09-23 Modify figure order

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, NSCLC CCL19⁺ CAFs and BRCA CAFs

NSCLC CCL19⁺ FRCs and NSCLC CCL19⁺ CAFs

Set directory

basedir <- here()

Read CCL19 FRC data

NSCLC_CCL19_data <- readRDS(paste0(basedir,"/data/Human/NSCLC_CCL19_FRCs_CAFs.rds"))

Read NSCLC CCL19⁺ TRC PRC data

NSCLC_CCL19_TRC_PRC <- readRDS(paste0(basedir,"/data/Human/NSCLC_CCL19_TRC_PRC_CAFs.rds"))

Define color palette

palet <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C")
names(palet) <- c("CAF2/TRC","CAF1/PRC","AdvFB" ,"SMC/PC")

Dotplot with apCAF, iCAF and myCAF gene signatures (Supplementary Figure 2C)

data_conv <-NSCLC_CCL19_TRC_PRC
data_conv <-Remove_ensebl_id(data_conv)

CAF_subsets <- list("iCAF genes"= c("EFEMP1","IL6","C3","CFD","CLU","CXCL12","TNXB",
                                    "HAS1","PLA2G2A","GSN","PCOLCE2","CD34","LEPR",
                                    "CXCL14","CCL2","ADH1B","GPC3","VCAM1","TNC","ALDH1A2"),
                     "apCAF genes" = c("CD74","HLA-DRB1","HLA-DRA","HLA-DQB1"),
                    "myCAF genes"=c("FAP","POSTN","LRRC15","MMP11","COL10A1","COMP","COL8A1","GREM1","SULF1","COL13A1",
                    "COL5A2","LRRC17","COL12A1","THBS2","GJB2","ACTA2","MYH11","ACTG2","DES","COL4A6","BCAM",
                    "RGS5","MCAM","COL4A1","COL18A1","LAMC3","ARHGDIB"))

DotPlot(object = data_conv, features=CAF_subsets,group.by = "cell_type" ,scale=FALSE,dot.scale = 4) +  theme(strip.placement = "outside") + 
  theme(axis.text.x = element_text(angle = 90,hjust = 1,colour = NULL,face="bold"),axis.title.x.top=element_text(hjust = 1,face="bold")) +xlab(" ")+ylab(" ")

Version Author Date
c767554 Pchryssa 2024-09-23

CAF Signatures (Supplementary Figure 2D)

apCAF signature

apCAF_genes <-c("CD74","HLA-DRB1","HLA-DRA","HLA-DQB1")

#Get full gene name (together with the ensembl id)
apCAF_genes <- unlist(lapply(apCAF_genes, function(x) {
  get_full_gene_name(x,NSCLC_CCL19_data)
})) 

slot_type <-"data"
gn <- "apCAF"
Visualize_GeneSignatures_sc(NSCLC_CCL19_data, apCAF_genes, slot_type, 'average.mean',gn) + ggtitle("apCAF signature")

Version Author Date
c767554 Pchryssa 2024-09-23

iCAF signature

iCAF_genes <-c("EFEMP1","IL6","C3","CFD","CLU","CXCL12","TNXB","HAS1","PLA2G2A","GSN","PCOLCE2","CD34","LEPR","CXCL14","CCL2","ADH1B","GPC3","VCAM1","TNC","ALDH1A2")

#Get full gene name (together with the ensembl id)
iCAF_genes <- unlist(lapply(iCAF_genes, function(x) {
  get_full_gene_name(x,NSCLC_CCL19_data)
})) 

slot_type <-"data"
gn <- "iCAF"
Visualize_GeneSignatures_sc(NSCLC_CCL19_data, iCAF_genes, slot_type, 'average.mean',gn) + ggtitle("iCAF signature")

Version Author Date
c767554 Pchryssa 2024-09-23

myCAF signature

myCAF_genes <-c("FAP","POSTN","LRRC15","Mmp11","COL10A1","COMP","COL8A1","GREM1","SULF1","COL13A1","COL5A2","LRRC17","COL12A1","Thbs2","GJB2","ACTA2","MYH11","ACTG2","DES","COL4A6","BCAM","RGS5","MCAM","COL4A1","COL18A1","LAMC3","ARHGDIB")

#Get full gene name (together with the ensembl id)
myCAF_genes <- unlist(lapply(myCAF_genes, function(x) {
  get_full_gene_name(x,NSCLC_CCL19_data)
})) 

slot_type <-"data"
gn <- "myCAF"
Visualize_GeneSignatures_sc(NSCLC_CCL19_data, myCAF_genes, slot_type, 'average.mean',gn) + ggtitle("myCAF signature")

Version Author Date
c767554 Pchryssa 2024-09-23

BRCA (breast cancer) CAFs

Read BRCA data from Cords et al, 2023

data_breast <- readRDS(paste0(basedir,"/data/Public/BREAST_fibro_tumour.rds"))

Define color palette

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

CAF annotation

DimPlot(data_breast, reduction = "umap", group.by = "CAFtype")+
  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("CAFs in breast cancer"))

Version Author Date
a302606 Pchryssa 2024-09-23

Subset on CCL19⁺ cells

ccl19_data<-subset(x=data_breast,subset=CCL19>0, invert=FALSE)


resolution <- c(0.1, 0.25, 0.4, 0.5,0.6,0.8, 1.,1.2,1.4,1.6,1.8,2.)

# run sctransform
ccl19_data  <- SCTransform(ccl19_data, vars.to.regress = "percent.mt", verbose = FALSE)
ccl19_data <- RunPCA(object = ccl19_data, assay = "SCT",npcs = 30, verbose = FALSE,seed.use = 8734)
ccl19_data <- RunTSNE(object = ccl19_data, assay = "SCT",reduction = "pca", dims = 1:20, seed.use = 8734)
ccl19_data <- RunUMAP(object = ccl19_data, assay = "SCT", reduction = "pca", dims = 1:20, seed.use = 8734)
ccl19_data <- FindNeighbors(object = ccl19_data, reduction = "pca", dims = 1:20, seed.use = 8734)
for(k in 1:length(resolution)){
  ccl19_data <- FindClusters(object = ccl19_data, resolution = resolution[k], random.seed = 8734)
}
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9373
Number of communities: 3
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8787
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: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8452
Number of communities: 7
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8278
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: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8132
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: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7840
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: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7553
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: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7333
Number of communities: 11
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7125
Number of communities: 11
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6937
Number of communities: 13
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6747
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: 1500
Number of edges: 52230

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.6573
Number of communities: 15
Elapsed time: 0 seconds

Annotate CCL19⁺ breast cancer data

ccl19_data$cell_type <- -1
ccl19_data$cell_type[which(ccl19_data$SCT_snn_res.0.1 == 1)] <- "CAF1/PRC"
ccl19_data$cell_type[which(ccl19_data$SCT_snn_res.0.1 == 0)] <- "CAF2/TRC"
ccl19_data$cell_type[which(ccl19_data$SCT_snn_res.0.1 == 2)] <- "CAF2/TRC"

CCL19⁺ fibroblasts (BRCA) (Supplementary Figure 2F)

palet <- cols[4:10]
names(palet) <- c("CAF1/PRC","CAF2/TRC")

DimPlot(ccl19_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(paste0("CCL19", expression("\u207A"), " fibroblasts (BRCA)"))

Version Author Date
a302606 Pchryssa 2024-09-23

Dotplot (Supplementary Figure 2G)

data_conv <-ccl19_data

Idents(data_conv) <- data_conv$cell_type
levels(data_conv)<-levels(data_conv)[order(match(levels(data_conv),c("CAF1/PRC","CAF2/TRC")))]
data_conv$cell_type <- factor(as.character(data_conv@active.ident), levels = rev(c("CAF1/PRC","CAF2/TRC")))

gene_list <-c("CCL19","CCL21","PDPN","FAP","POSTN","CLU","LEPR","CD34","SULF1","DPT","ICAM1","VCAM1","ACTA2","MYH11","MCAM","NOTCH3","RGS5","DES")

dittoDotPlot(data_conv, vars = gene_list, group.by = "cell_type", size = 8,legend.size.title = "Expression (%)",scale = FALSE, max = 3.5) + ylab(" ")

Version Author Date
a302606 Pchryssa 2024-09-23

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] 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] "Tue Nov  5 21:36:47 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] 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