Comparison of naive CCL19-EYFP cells vs mCOV-FIt31-g33 CCL19-EYFP cells

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Last updated: 2024-09-23

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Knit directory: CCL19_FRCs_lung_cancer/

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File	Version	Author	Date	Message
Rmd	05125c2	Pchryssa	2024-09-23	CCL19-EYFP cells vs mCOV-FIt31-g33 CCL19-EYFP cells

---

Load packages

suppressPackageStartupMessages({
  library(here)
  library(purrr)
  library(dplyr)
  library(stringr)
  library(patchwork)
  library(Seurat)
  library(Matrix)
  library(gridExtra)
  library(gsubfn)
  library(ggsci)
  library(biomaRt)
  library(tidyverse)
  library(msigdbr)
  library(stats)
  library(clusterProfiler)
  library(dict)
  library(openxlsx)
  library(DOSE)
  library(enrichplot)
})

Set directory

basedir <- here()

Read CCL19-EYFP⁺ cell data from naïve lungs and excised LLC-gp33 tumors on day 23

CCL19_EYFP <- readRDS(paste0(basedir,"/data/Mouse/CCL19_EYFP_nonmCOV.rds"))

Read CCL19-EYFP⁺ mCOV-FIt31-g33 cell data

CCL19_EYFP_mCOV <- readRDS(paste0(basedir,"/data/Mouse/mCOV.rds"))

Feature plots

Cxcl13 LLC naive

FeaturePlot(CCL19_EYFP, reduction = "umap",
          features = get_full_gene_name('Cxcl13',CCL19_EYFP),raster=FALSE,
          cols=c("lightgrey", "darkred"),min.cutoff = 0, max.cutoff = 6) + ggtitle(paste0("Ccl19-EYFP", "\U207A ", "cells (Naive)"))

Cxcl13 mCOV-FIt31-g33

FeaturePlot(CCL19_EYFP_mCOV, reduction = "umap", 
          features = get_full_gene_name('Cxcl13',CCL19_EYFP_mCOV),raster=FALSE,
          cols=c("lightgrey", "darkred")) + ggtitle(paste0("Ccl19-EYFP", "\U207A ", "cells (mCOV-FIt31-g33)"))

Cr2 mCOV-FIt31-g33

FeaturePlot(CCL19_EYFP_mCOV, reduction = "umap", 
          features = get_full_gene_name('Cr2',CCL19_EYFP_mCOV),raster=FALSE,
          cols=c("lightgrey", "darkred")) + ggtitle(paste0("Ccl19-EYFP", "\U207A ", "cells (mCOV-FIt31-g33)"))

Let’s compare the gene expression between TRC/PRC (d23) and their progenitor subsets (d15) to identify gene programs that may be relevant for their function in supporting the T cell niches

#Set new annotation: Change Smoc1 AdvFB to Cd34 AdvFB
CCL19_EYFP@meta.data$annot[CCL19_EYFP@meta.data$annot == paste0("Smoc1", expression("\u207A "), "AdvFB") ] <- paste0("Cd34", expression("\u207A "), "AdvFB")

progenitors_d15 <- subset(CCL19_EYFP, TimePoint == "d15" & annot %in% c(paste0("Cd34", expression("\u207A "), "AdvFB"),"SMC/PC"))

progenitors_mCOV <- subset(CCL19_EYFP_mCOV, annot %in% c(paste0("Rgs5", expression("\u207A "), "PRC"),paste0("Sulf1", expression("\u207A "), "TRC"),"TLS TRC"))

data_merge <- merge(progenitors_d15, y = c(progenitors_mCOV),
             add.cell.ids = c("progenitors_d15","progenitors_mCOV"),
             project = "progenitors_d15_mCOV")

#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: 2779
Number of edges: 94713

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9629
Number of communities: 4
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2779
Number of edges: 94713

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9246
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 2779
Number of edges: 94713

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8931
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: 2779
Number of edges: 94713

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8597
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: 2779
Number of edges: 94713

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8353
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: 2779
Number of edges: 94713

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8163
Number of communities: 14
Elapsed time: 0 seconds

Save merged data progenitors Day 15 (naive) and Day 23 (mCOV-FIt31-g33)

#saveRDS(data_merge,paste0(basedir,"/data/Human/progenitors_d15_mCOV.rds"))

Pathway analysis

CD34⁺ AdvFB (Day 15) vs TLS TRC (Day 23) (Supplementary Figure 10C)

# Step 1 : Set output directory
subDir <- "GSEA_AdvFB_TLS/"
saving_path <- paste0(basedir,"/output/")
final_dir <- file.path(saving_path, subDir)
dir.create(final_dir, showWarnings = FALSE,recursive = TRUE)
map_df <- ExtractMouseGeneSets(final_dir)

# Step 2: Customize parameters
httr::set_config(httr::config(ssl_verifypeer = FALSE))
organism <- "org.Mm.eg.db"

disease_phase <- "d15vsd23_AdvFB_TLS"
datatype <- "SYMBOL"

advfb_tls <-subset(data_merge, annot %in% c(paste0("Cd34", expression("\u207A "), "AdvFB"),"TLS TRC"))

Idents(advfb_tls) <- advfb_tls$annot
  
DEmarkers <-FindAllMarkers(advfb_tls, only.pos=T, logfc.threshold = 0.1,
                           min.pct = 0.1)

Vec <-unique(advfb_tls$annot)
EnrichParameters_TLS <-customize_parameters(Vec,DEmarkers,organism,datatype,disease_phase,saving_path) 

[1] "Finish Enrichment_Analysis for GO Cd34⁺ AdvFB"
[1] "Finish Enrichment_Analysis for GO TLS TRC"

# Step 3: Enrichment Analysis
for (i in seq(1,length(EnrichParameters_TLS$enrichcl_list))){
  terms<- EnrichParameters_TLS$enrichcl_list[[i]]
  # Filter on the most significant pathways (keep rows where p.adjust<= 0.05)
  terms<- terms@result[terms@result$p.adjust <= 0.05,]
  population <- Vec[i]
  population<- gsub("/", "_", population)
  write.xlsx(terms, paste0(final_dir,"/","GO_Pathways_",population,".xlsx"),row.names = TRUE)
}

#Step 4: Plot enriched pathways
pathways <-c("cell chemotaxis", "tissue remodeling","activation of immune response",
             "leukocyte mediated immunity", "interleukin-1 production", "tumor necrosis factor production")

TLS_terms <- EnrichParameters_TLS$enrichcl_list[[2]]@result

selec_pathways <- TLS_terms[TLS_terms$Description %in% pathways,]

selec_pathways$Description <- factor(selec_pathways$Description, levels = rev(pathways))
selec_pathways <- selec_pathways[order(selec_pathways$Description), ]

ggplot(data=selec_pathways, aes(x=Description, y=qscore, fill = analysis)) + xlab(NULL) +
    geom_bar(stat="identity",position="dodge",colour = "black",show.legend = FALSE, width= 0.8, size = 1 ) + coord_flip() +
    scale_y_continuous(expand = expansion(c(0,0)), limits = c(0.0, 6),breaks = c(0,2,4,6)) +
    scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 80)) +
    theme( legend.justification = "top",
           plot.title = element_text(hjust = 0.5,size = 12,face="bold"),axis.line = element_line(colour = "black"),
           panel.grid.major = element_blank(),
           panel.grid.minor = element_blank(),
           axis.text.x = element_text(angle = 0, vjust = 0.5,colour = "black", size = 10),
           axis.text.y = element_text(angle = 0, vjust = 0.8,colour = "black", size = 10, face=ifelse(levels(selec_pathways$Description)=="tumor necrosis factor production","bold","plain")),
           axis.title.y = element_text(size = rel(2), angle = 45),
           axis.title.x = element_text(size = rel(1.5), angle = 0),
           axis.text = element_text(size = 8),
           panel.background = element_blank(), legend.position = "none") +
    scale_fill_manual(values = "dark gray") + ggtitle("Enriched in TLS TRC (vs. AdvFB day 15)")

CD34⁺ AdvFB (Day 15) vs Sulf1⁺ TRC (Day 23) (Supplementary Figure 10D)

# Step 1 : Set output directory
subDir <- "GSEA_AdvFB_SULF1/"
saving_path <- paste0(basedir,"/output/")
final_dir <- file.path(saving_path, subDir)
dir.create(final_dir, showWarnings = FALSE,recursive = TRUE)
map_df <- ExtractMouseGeneSets(final_dir)

# Step 2: Customize parameters
httr::set_config(httr::config(ssl_verifypeer = FALSE))
organism <- "org.Mm.eg.db"

disease_phase <- "d15vsd23_AdvFB_SULF1"
datatype <- "SYMBOL"

AdvFB_sulf1 <-subset(data_merge, annot %in% c(paste0("Cd34", expression("\u207A "), "AdvFB"),paste0("Sulf1", expression("\u207A "), "TRC")))

Idents(AdvFB_sulf1) <- AdvFB_sulf1$annot
  
DEmarkers <-FindAllMarkers(AdvFB_sulf1, only.pos=T, logfc.threshold = 0.1,
                           min.pct = 0.1)

Vec <-unique(AdvFB_sulf1$annot)
EnrichParameters_Sulf1 <-customize_parameters(Vec,DEmarkers,organism,datatype,disease_phase,saving_path) 

[1] "Finish Enrichment_Analysis for GO Cd34⁺ AdvFB"
[1] "Finish Enrichment_Analysis for GO Sulf1⁺ TRC"

# Step 3: Enrichment Analysis
for (i in seq(1,length(EnrichParameters_Sulf1$enrichcl_list))){
  terms<- EnrichParameters_Sulf1$enrichcl_list[[i]]
  # Filter on the most significant pathways (keep rows where p.adjust<= 0.05)
  terms<- terms@result[terms@result$p.adjust <= 0.05,]
  population <- Vec[i]
  population<- gsub("/", "_", population)
  write.xlsx(terms, paste0(final_dir,"/","GO_Pathways_",population,".xlsx"),row.names = TRUE)
}

#Step 4: Plot enriched pathways
pathways <-c("regulation of cytokine-mediated signaling pathway", "lymphocyte homeostasis","actin filament bundle organization",
             "positive regulation of cell-cell adhesion", "regulation of T cell activation", "tissue remodeling")

TRC_term_sulf1 <- EnrichParameters_Sulf1$enrichcl_list[[2]]@result

selec_pathways <- TRC_term_sulf1[TRC_term_sulf1$Description %in% pathways,]

selec_pathways$Description <- factor(selec_pathways$Description, levels = rev(pathways))
selec_pathways <- selec_pathways[order(selec_pathways$Description), ]

ggplot(data=selec_pathways, aes(x=Description, y=qscore, fill = analysis)) + xlab(NULL) +
    geom_bar(stat="identity",position="dodge",colour = "black",show.legend = FALSE, width= 0.8, size = 1 ) + coord_flip() +
    scale_y_continuous(expand = expansion(c(0,0)), limits = c(0.0, 6),breaks = c(0,2,4,6)) +
    scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 80)) +
    theme( legend.justification = "top",
           plot.title = element_text(hjust = 0.5,size = 12,face="bold"),axis.line = element_line(colour = "black"),
           panel.grid.major = element_blank(),
           panel.grid.minor = element_blank(),
           axis.text.x = element_text(angle = 0, vjust = 0.5,colour = "black", size = 10),
           axis.text.y = element_text(angle = 0, vjust = 0.8,colour = "black", size = 10, face=ifelse(levels(selec_pathways$Description)=="regulation of T cell activation","bold","plain")),
           axis.title.y = element_text(size = rel(2), angle = 45),
           axis.title.x = element_text(size = rel(1.5), angle = 0),
           axis.text = element_text(size = 8),
           panel.background = element_blank(), legend.position = "none") +
    scale_fill_manual(values = "dark gray") + ggtitle(paste0("Enriched in Sulf1", "\U207A ", "TRC (vs. AdvFB day 15)")) 

SMC/PC (Day 15) vs PRC (Day 23) (Supplementary Figure 10E)

# Step 1 : Set output directory
subDir <- "GSEA_SMC_PRC/"
saving_path <- paste0(basedir,"/output/")
final_dir <- file.path(saving_path, subDir)
dir.create(final_dir, showWarnings = FALSE,recursive = TRUE)
map_df <- ExtractMouseGeneSets(saving_path)

# Step 2: Customize parameters
httr::set_config(httr::config(ssl_verifypeer = FALSE))
organism <- "org.Mm.eg.db"

disease_phase <- "d15vsd23_SMC_PRC"
datatype <- "SYMBOL"

smc_prc <-subset(data_merge, annot %in% c("SMC/PC", paste0("Rgs5", expression("\u207A "), "PRC")))

Idents(smc_prc) <- smc_prc$annot
  
DEmarkers <-FindAllMarkers(smc_prc, only.pos=T, logfc.threshold = 0.1,
                           min.pct = 0.1)

Vec <-unique(smc_prc$annot)
EnrichParameters <-customize_parameters(Vec,DEmarkers,organism,datatype,disease_phase,saving_path) 

[1] "Finish Enrichment_Analysis for GO SMC/PC"
[1] "Finish Enrichment_Analysis for GO Rgs5⁺ PRC"

# Step 3: Enrichment Analysis
for (i in seq(1,length(EnrichParameters$enrichcl_list))){
  terms<- EnrichParameters$enrichcl_list[[i]]
  # Filter on the most significant pathways (keep rows where p.adjust<= 0.05)
  terms<- terms@result[terms@result$p.adjust <= 0.05,]
  population <- Vec[i]
  population<- gsub("/", "_", population)
  write.xlsx(terms, paste0(final_dir,"/","GO_Pathways_",population,".xlsx"),row.names = TRUE)
}

# Step 4: Plot enriched pathways 
pathways <-c("T cell proliferation", "leukocyte migration","T cell mediated immunity",
             "response to type II interferon", "regulation of angiogenesis", "cytokine-mediated signaling pathway")

PRC_terms <- EnrichParameters$enrichcl_list[[2]]@result

selec_pathways <- PRC_terms[PRC_terms$Description %in% pathways,]

selec_pathways$Description <- factor(selec_pathways$Description, levels = rev(pathways))
selec_pathways <- selec_pathways[order(selec_pathways$Description), ]

ggplot(data=selec_pathways, aes(x=Description, y=qscore, fill = analysis)) + xlab(NULL) +
    geom_bar(stat="identity",position="dodge",colour = "black",show.legend = FALSE, width= 0.8, size = 1 ) + coord_flip() +
    scale_y_continuous(expand = expansion(c(0,0)), limits = c(0.0, 15),breaks = c(0,5,10,15)) +
    scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 80)) +
    theme( legend.justification = "top",
           plot.title = element_text(hjust = 0.5,size = 12,face="bold"),axis.line = element_line(colour = "black"),
           panel.grid.major = element_blank(),
           panel.grid.minor = element_blank(),
           axis.text.x = element_text(angle = 0, vjust = 0.5,colour = "black", size = 10),
           axis.text.y = element_text(angle = 0, vjust = 0.8,colour = "black", size = 10, face=ifelse(levels(selec_pathways$Description)=="cytokine-mediated signaling pathway","bold","plain")),
           axis.title.y = element_text(size = rel(2), angle = 45),
           axis.title.x = element_text(size = rel(1.5), angle = 0),
           axis.text = element_text(size = 8),
           panel.background = element_blank(), legend.position = "none") +
    scale_fill_manual(values = "dark gray") + ggtitle("Enriched in PRC (vs. SMC/PC day 15)")

Gene-Concept Networks

Enriched in TLS TRC (vs. AdvFB day 15) (Supplementary Figure 10F)

pathways <- c("tumor necrosis factor production")

cnetplot(EnrichParameters_TLS$enrichcl_list[[2]], node_label="gene", layout = "kk", showCategory = pathways,
        max.overlaps=Inf,color.params = list(gene ="black",
                                             category = "red",
                                             edge = TRUE),
                          cex.params = list(category_label = 0.0000001,
                                            label_gene = 0.000001, gene_label= 0.6)) + theme(legend.text=element_text(size=8))

Enriched in Sulf1⁺ TRC (vs. AdvFB day 15) (Supplementary Figure 10G)

pathways <- c("regulation of T cell activation")

cnetplot(EnrichParameters_Sulf1$enrichcl_list[[2]], node_label="gene", layout = "kk", showCategory = pathways,
        max.overlaps=Inf,color.params = list(gene ="black",
                                             category = "red",
                                             edge = TRUE),
                          cex.params = list(category_label = 0.0000001,
                                            label_gene = 0.000001, gene_label= 0.6)) + theme(legend.text=element_text(size=8))

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] enrichplot_1.20.0     DOSE_3.26.1           openxlsx_4.2.5.2     
 [4] dict_0.10.0           clusterProfiler_4.8.2 msigdbr_7.5.1        
 [7] lubridate_1.9.2       forcats_1.0.0         readr_2.1.4          
[10] ggplot2_3.4.2         tidyverse_2.0.0       biomaRt_2.56.1       
[13] ggsci_3.0.0           gsubfn_0.7            proto_1.0.0          
[16] gridExtra_2.3         Matrix_1.6-0          SeuratObject_4.1.3   
[19] Seurat_4.3.0.1        patchwork_1.1.2       stringr_1.5.0        
[22] dplyr_1.1.2           purrr_1.0.1           here_1.0.1           
[25] magrittr_2.0.3        circlize_0.4.15       tidyr_1.3.0          
[28] tibble_3.2.1          workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] fs_1.6.3                matrixStats_1.0.0       spatstat.sparse_3.0-2  
  [4] bitops_1.0-7            HDO.db_0.99.1           httr_1.4.6             
  [7] RColorBrewer_1.1-3      tools_4.3.1             sctransform_0.3.5      
 [10] utf8_1.2.3              R6_2.5.1                lazyeval_0.2.2         
 [13] uwot_0.1.16             withr_2.5.0             sp_2.0-0               
 [16] prettyunits_1.1.1       progressr_0.13.0        textshaping_0.3.6      
 [19] cli_3.6.1               Biobase_2.60.0          spatstat.explore_3.2-1 
 [22] scatterpie_0.2.1        labeling_0.4.2          sass_0.4.7             
 [25] spatstat.data_3.0-1     ggridges_0.5.4          pbapply_1.7-2          
 [28] systemfonts_1.0.4       yulab.utils_0.0.6       gson_0.1.0             
 [31] parallelly_1.36.0       limma_3.56.2            rstudioapi_0.15.0      
 [34] RSQLite_2.3.1           generics_0.1.3          gridGraphics_0.5-1     
 [37] shape_1.4.6             ica_1.0-3               spatstat.random_3.1-5  
 [40] zip_2.3.0               GO.db_3.17.0            fansi_1.0.4            
 [43] S4Vectors_0.38.1        abind_1.4-5             lifecycle_1.0.3        
 [46] whisker_0.4.1           yaml_2.3.7              qvalue_2.32.0          
 [49] BiocFileCache_2.8.0     Rtsne_0.16              grid_4.3.1             
 [52] blob_1.2.4              promises_1.2.0.1        crayon_1.5.2           
 [55] miniUI_0.1.1.1          lattice_0.21-8          cowplot_1.1.1          
 [58] KEGGREST_1.40.0         pillar_1.9.0            knitr_1.43             
 [61] fgsea_1.26.0            tcltk_4.3.1             future.apply_1.11.0    
 [64] codetools_0.2-19        fastmatch_1.1-4         leiden_0.4.3           
 [67] glue_1.6.2              getPass_0.2-4           downloader_0.4         
 [70] ggfun_0.1.1             data.table_1.14.8       vctrs_0.6.3            
 [73] png_0.1-8               treeio_1.24.3           org.Mm.eg.db_3.17.0    
 [76] gtable_0.3.3            cachem_1.0.8            xfun_0.39              
 [79] mime_0.12               tidygraph_1.2.3         survival_3.5-5         
 [82] ellipsis_0.3.2          fitdistrplus_1.1-11     ROCR_1.0-11            
 [85] nlme_3.1-162            ggtree_3.8.2            bit64_4.0.5            
 [88] progress_1.2.2          filelock_1.0.2          RcppAnnoy_0.0.21       
 [91] GenomeInfoDb_1.36.1     rprojroot_2.0.3         bslib_0.5.0            
 [94] irlba_2.3.5.1           KernSmooth_2.23-22      colorspace_2.1-0       
 [97] BiocGenerics_0.46.0     DBI_1.1.3               tidyselect_1.2.0       
[100] processx_3.8.2          bit_4.0.5               compiler_4.3.1         
[103] curl_5.0.1              git2r_0.33.0            xml2_1.3.5             
[106] plotly_4.10.2           shadowtext_0.1.2        scales_1.2.1           
[109] lmtest_0.9-40           callr_3.7.3             rappdirs_0.3.3         
[112] digest_0.6.33           goftest_1.2-3           spatstat.utils_3.1-0   
[115] rmarkdown_2.23          XVector_0.40.0          htmltools_0.5.5        
[118] pkgconfig_2.0.3         highr_0.10              dbplyr_2.3.3           
[121] fastmap_1.1.1           rlang_1.1.1             GlobalOptions_0.1.2    
[124] htmlwidgets_1.6.2       shiny_1.7.4.1           farver_2.1.1           
[127] jquerylib_0.1.4         zoo_1.8-12              jsonlite_1.8.7         
[130] BiocParallel_1.34.2     GOSemSim_2.26.1         RCurl_1.98-1.12        
[133] GenomeInfoDbData_1.2.10 ggplotify_0.1.1         munsell_0.5.0          
[136] Rcpp_1.0.11             ape_5.7-1               babelgene_22.9         
[139] viridis_0.6.4           reticulate_1.36.1       stringi_1.7.12         
[142] ggraph_2.1.0            zlibbioc_1.46.0         MASS_7.3-60            
[145] plyr_1.8.8              parallel_4.3.1          listenv_0.9.0          
[148] ggrepel_0.9.3           deldir_1.0-9            Biostrings_2.68.1      
[151] graphlayouts_1.0.0      splines_4.3.1           tensor_1.5             
[154] hms_1.1.3               ps_1.7.5                igraph_1.5.0.1         
[157] spatstat.geom_3.2-4     reshape2_1.4.4          stats4_4.3.1           
[160] XML_3.99-0.14           evaluate_0.21           tzdb_0.4.0             
[163] tweenr_2.0.2            httpuv_1.6.11           RANN_2.6.1             
[166] polyclip_1.10-4         future_1.33.0           scattermore_1.2        
[169] ggforce_0.4.1           xtable_1.8-4            tidytree_0.4.4         
[172] later_1.3.1             ragg_1.2.5              viridisLite_0.4.2      
[175] aplot_0.1.10            memoise_2.0.1           AnnotationDbi_1.62.2   
[178] IRanges_2.34.1          cluster_2.1.4           timechange_0.2.0       
[181] globals_0.16.2         

date()

[1] "Mon Sep 23 21:58:43 2024"

Session information

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] enrichplot_1.20.0     DOSE_3.26.1           openxlsx_4.2.5.2     
 [4] dict_0.10.0           clusterProfiler_4.8.2 msigdbr_7.5.1        
 [7] lubridate_1.9.2       forcats_1.0.0         readr_2.1.4          
[10] ggplot2_3.4.2         tidyverse_2.0.0       biomaRt_2.56.1       
[13] ggsci_3.0.0           gsubfn_0.7            proto_1.0.0          
[16] gridExtra_2.3         Matrix_1.6-0          SeuratObject_4.1.3   
[19] Seurat_4.3.0.1        patchwork_1.1.2       stringr_1.5.0        
[22] dplyr_1.1.2           purrr_1.0.1           here_1.0.1           
[25] magrittr_2.0.3        circlize_0.4.15       tidyr_1.3.0          
[28] tibble_3.2.1          workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] fs_1.6.3                matrixStats_1.0.0       spatstat.sparse_3.0-2  
  [4] bitops_1.0-7            HDO.db_0.99.1           httr_1.4.6             
  [7] RColorBrewer_1.1-3      tools_4.3.1             sctransform_0.3.5      
 [10] utf8_1.2.3              R6_2.5.1                lazyeval_0.2.2         
 [13] uwot_0.1.16             withr_2.5.0             sp_2.0-0               
 [16] prettyunits_1.1.1       progressr_0.13.0        textshaping_0.3.6      
 [19] cli_3.6.1               Biobase_2.60.0          spatstat.explore_3.2-1 
 [22] scatterpie_0.2.1        labeling_0.4.2          sass_0.4.7             
 [25] spatstat.data_3.0-1     ggridges_0.5.4          pbapply_1.7-2          
 [28] systemfonts_1.0.4       yulab.utils_0.0.6       gson_0.1.0             
 [31] parallelly_1.36.0       limma_3.56.2            rstudioapi_0.15.0      
 [34] RSQLite_2.3.1           generics_0.1.3          gridGraphics_0.5-1     
 [37] shape_1.4.6             ica_1.0-3               spatstat.random_3.1-5  
 [40] zip_2.3.0               GO.db_3.17.0            fansi_1.0.4            
 [43] S4Vectors_0.38.1        abind_1.4-5             lifecycle_1.0.3        
 [46] whisker_0.4.1           yaml_2.3.7              qvalue_2.32.0          
 [49] BiocFileCache_2.8.0     Rtsne_0.16              grid_4.3.1             
 [52] blob_1.2.4              promises_1.2.0.1        crayon_1.5.2           
 [55] miniUI_0.1.1.1          lattice_0.21-8          cowplot_1.1.1          
 [58] KEGGREST_1.40.0         pillar_1.9.0            knitr_1.43             
 [61] fgsea_1.26.0            tcltk_4.3.1             future.apply_1.11.0    
 [64] codetools_0.2-19        fastmatch_1.1-4         leiden_0.4.3           
 [67] glue_1.6.2              getPass_0.2-4           downloader_0.4         
 [70] ggfun_0.1.1             data.table_1.14.8       vctrs_0.6.3            
 [73] png_0.1-8               treeio_1.24.3           org.Mm.eg.db_3.17.0    
 [76] gtable_0.3.3            cachem_1.0.8            xfun_0.39              
 [79] mime_0.12               tidygraph_1.2.3         survival_3.5-5         
 [82] ellipsis_0.3.2          fitdistrplus_1.1-11     ROCR_1.0-11            
 [85] nlme_3.1-162            ggtree_3.8.2            bit64_4.0.5            
 [88] progress_1.2.2          filelock_1.0.2          RcppAnnoy_0.0.21       
 [91] GenomeInfoDb_1.36.1     rprojroot_2.0.3         bslib_0.5.0            
 [94] irlba_2.3.5.1           KernSmooth_2.23-22      colorspace_2.1-0       
 [97] BiocGenerics_0.46.0     DBI_1.1.3               tidyselect_1.2.0       
[100] processx_3.8.2          bit_4.0.5               compiler_4.3.1         
[103] curl_5.0.1              git2r_0.33.0            xml2_1.3.5             
[106] plotly_4.10.2           shadowtext_0.1.2        scales_1.2.1           
[109] lmtest_0.9-40           callr_3.7.3             rappdirs_0.3.3         
[112] digest_0.6.33           goftest_1.2-3           spatstat.utils_3.1-0   
[115] rmarkdown_2.23          XVector_0.40.0          htmltools_0.5.5        
[118] pkgconfig_2.0.3         highr_0.10              dbplyr_2.3.3           
[121] fastmap_1.1.1           rlang_1.1.1             GlobalOptions_0.1.2    
[124] htmlwidgets_1.6.2       shiny_1.7.4.1           farver_2.1.1           
[127] jquerylib_0.1.4         zoo_1.8-12              jsonlite_1.8.7         
[130] BiocParallel_1.34.2     GOSemSim_2.26.1         RCurl_1.98-1.12        
[133] GenomeInfoDbData_1.2.10 ggplotify_0.1.1         munsell_0.5.0          
[136] Rcpp_1.0.11             ape_5.7-1               babelgene_22.9         
[139] viridis_0.6.4           reticulate_1.36.1       stringi_1.7.12         
[142] ggraph_2.1.0            zlibbioc_1.46.0         MASS_7.3-60            
[145] plyr_1.8.8              parallel_4.3.1          listenv_0.9.0          
[148] ggrepel_0.9.3           deldir_1.0-9            Biostrings_2.68.1      
[151] graphlayouts_1.0.0      splines_4.3.1           tensor_1.5             
[154] hms_1.1.3               ps_1.7.5                igraph_1.5.0.1         
[157] spatstat.geom_3.2-4     reshape2_1.4.4          stats4_4.3.1           
[160] XML_3.99-0.14           evaluate_0.21           tzdb_0.4.0             
[163] tweenr_2.0.2            httpuv_1.6.11           RANN_2.6.1             
[166] polyclip_1.10-4         future_1.33.0           scattermore_1.2        
[169] ggforce_0.4.1           xtable_1.8-4            tidytree_0.4.4         
[172] later_1.3.1             ragg_1.2.5              viridisLite_0.4.2      
[175] aplot_0.1.10            memoise_2.0.1           AnnotationDbi_1.62.2   
[178] IRanges_2.34.1          cluster_2.1.4           timechange_0.2.0       
[181] globals_0.16.2