Last updated: 2024-11-06

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Knit directory: CCL19_FRCs_lung_cancer/

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Rmd dcc747f Pchryssa 2024-11-06 Correct figure ordering
html 13ef7c8 Pchryssa 2024-11-05 Build site.
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Rmd 116d217 Pchryssa 2024-09-23 Total CD8 T in mCOV-FIt31-gp33

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)
  library(dittoSeq)
  library(CellChat)
})

Set directory

basedir <- here()

Total CD8 T cells in DTR

Read CD8⁺ T cells in mCOV-FIt31-gp33

CD8_T <- readRDS(paste0(basedir,"/data/Mouse/CD3_CD8_annot_final.rds"))

Set color palette

cluster_palette <- Polychrome::palette36.colors()
names(cluster_palette) <-unique(CD8_T$annot) 

CD8⁺ T cells mCOV-FIt31-gp33

Supplementary Figure 6G

DimPlot(CD8_T, reduction = "umap", group.by = "annot")+
  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 (mCOV-FIt31-g33)"))

Version Author Date
82a5a4d Pchryssa 2024-09-23

Dotplot CD8⁺ T cell subsets (Supplementary Figure 6H)

data_conv <-CD8_T
data_conv <-Remove_ensebl_id(data_conv)

gene_list <-c("Pclaf","Top2a","Mki67","Klrg1","Gzma","Cxcr3","Gzmk","Ly6a","Ifng","Ccl4","Xcl1","Gzmb","Havcr2","Ctla4","Prf1","Lag3","Pdcd1","Tigit", "Tox","Ccl5","Bcl2","Il7r","Ccr7","Tcf7","Sell")

dittoDotPlot(data_conv, vars = gene_list, group.by = "annot", size = 5,legend.size.title = "Expression (%)",scale = TRUE) + theme(text = element_text(size = 10)) +ylab( " ") 

Version Author Date
82a5a4d Pchryssa 2024-09-23

Pathway analysis (Supplementary Figure 6I)

# Step 1 : Set output directory
subDir <- "GSEA_CD8_T/"
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 <- "Depl_vs_NDepl"
datatype <- "SYMBOL"


Idents(CD8_T) <- CD8_T$depleted
  
DEmarkers <-FindAllMarkers(CD8_T, only.pos=T, logfc.threshold = 0.25,
                           min.pct = 0.25)

Vec <-unique(CD8_T$depleted)
EnrichParameters_TLS <-customize_parameters(Vec,DEmarkers,organism,datatype,disease_phase,saving_path) 
[1] "Finish Enrichment_Analysis for GO DTR⁺"
[1] "Finish Enrichment_Analysis for GO DTR−"
# Step 3: Enrichment Analysis
for (i in seq(1,length(EnrichParameters_TLS$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("cytokine-mediated signaling pathway", "canonical Wnt signaling pathway","V(D)J recombination",
             "alpha-beta T cell differentiation", "interleukin-4 production", "alpha-beta T cell activation")

CD8_terms <- EnrichParameters_TLS$enrichcl_list[[2]]@result

selec_pathways <- CD8_terms[CD8_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, 3),breaks = c(0,1,2,3)) +
    scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 80)) +
    theme(aspect.ratio = 1.5, 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),
           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 Ccl19-EYFP (DTR","\U2212)"))

Version Author Date
82a5a4d Pchryssa 2024-09-23

Interactome analysis with Cellchat (Suoqin Jin et al., 2021) between TLS TRC, Sulf1⁺ TRC and Tetramer⁺ CD8⁺ T cells

Read GP33/34⁺ CD8⁺ T cells sorted from LLC-gp33 bearing DTR⁺ and DTR\(^−\) lungs on day 23, after mCOV-Flt3l-gp33 immunization

Tetra_CD8 <- readRDS(paste0(basedir,"/data/Mouse/Tetra_CD8_EXH.rds"))

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

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

Subset mCOV-FIt31-g33 data on Sulf1⁺ TRC and TLS TRC

TRCs <- subset(CCL19_EYFP_mCOV, annot %in% c(paste0("Sulf1", "\u207A ", "TRC"), "TLS TRC"))

Merge data

data_merge <- merge(Tetra_CD8, y = c(TRCs),
             add.cell.ids = c("Tetra_CD8","TRCs"),
             project = "merge_TRC_CD8_T")

#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: 4886
Number of edges: 175919

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9614
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: 4886
Number of edges: 175919

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9218
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: 4886
Number of edges: 175919

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8930
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: 4886
Number of edges: 175919

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8560
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: 4886
Number of edges: 175919

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8301
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: 4886
Number of edges: 175919

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8069
Number of communities: 12
Elapsed time: 0 seconds

Convert seurat object to cellchat object

data_merge$cell_type <-data_merge$annot

cellchat <- Cellchat_Analysis(data_merge)
[1] "Create a CellChat object from a data matrix"
Set cell identities for the new CellChat object 
The cell groups used for CellChat analysis are  Ccr7⁺ T cells Cycling T cells Eff. Mem. T cells Effector T cells Exhausted T cells Sulf1⁺ TRC TLS TRC 
cellchat <-CellChatDownstreamAnalysis(cellchat,"mouse",thresh = 0.05)
Issue identified!! Please check the official Gene Symbol of the following genes:  
 H2-BI H2-Ea-ps 
triMean is used for calculating the average gene expression per cell group. 
[1] ">>> Run CellChat on sc/snRNA-seq data <<< [2024-11-06 00:14:53.77436]"
[1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2024-11-06 00:17:54.487932]"

Set color palette

palet <-c("#16FF32", "#3283FE", "#FEAF16" , "#B00068" ,"#1CFFCE","#E41A1C","#1B9E77")
names(palet) <-c("Effector T cells",paste0("Ccr7", "\U207A ","T cells"),"Exhausted T cells","Cycling T cells","Eff. Mem. T cells",paste0("Sulf1", "\u207A ", "TRC"), "TLS TRC") 

Interactome analysis (Supplementary Figure 6J)

gg <- netAnalysis_signalingRole_scatter(cellchat,color.use = palet)
gg <- gg + theme(aspect.ratio = 1.3) + ggtitle("mCOV-FIt31-gp33")
gg

Version Author Date
82a5a4d Pchryssa 2024-09-23

We can take a look at all significant interactions and involved signaling pathways

Cell-cell communication mediated by specific ligand-receptor (L-R) pairs

Icam1 - (Itgal+/tgb2) (Supplementary Figure 6K)

palet<-palet[order(match(names(palet),rownames(cellchat@net$count)))]

# Circle plot
netVisual_individual(cellchat, signaling = pathways.show.all, pairLR.use = "ICAM1_ITGAL_ITGB2", color.use = palet,  layout = "circle")

Version Author Date
82a5a4d Pchryssa 2024-09-23
[[1]]

Vcam1 - (Itga4+Itgb1) (Supplementary Figure 6K)

# Circle plot
netVisual_individual(cellchat, signaling = pathways.show.all, pairLR.use = "ITGA4_ITGB1_VCAM1", color.use = palet,  layout = "circle")

Version Author Date
82a5a4d Pchryssa 2024-09-23
[[1]]

Cxcl16 - Cxcr6 (Supplementary Figure 6L)

# Circle plot
netVisual_individual(cellchat, signaling = pathways.show.all, pairLR.use = "CXCL16_CXCR6", color.use = palet,  layout = "circle")

Version Author Date
82a5a4d Pchryssa 2024-09-23
[[1]]

Tslp - (Il7r+Crlf2) (Supplementary Figure 6L)

# Circle plot
netVisual_individual(cellchat, signaling = pathways.show.all, pairLR.use = "TSLP_IL7R_CRLF2", color.use = palet,  layout = "circle")

Version Author Date
82a5a4d Pchryssa 2024-09-23
[[1]]

Tgfb1 - (Tgfbr1+Tgfbr2) (Supplementary Figure 6M)

# Circle plot
netVisual_individual(cellchat, signaling = pathways.show.all, pairLR.use = "TGFB1_TGFBR1_TGFBR2", color.use = palet,  layout = "circle")

Version Author Date
82a5a4d Pchryssa 2024-09-23
[[1]]

Mif - (Cd74+ Cd44) (Supplementary Figure 6M)

# Circle plot
netVisual_individual(cellchat, signaling = pathways.show.all, pairLR.use = "MIF_CD74_CD44", color.use = palet,  layout = "circle")

Version Author Date
82a5a4d Pchryssa 2024-09-23
[[1]]

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] CellChat_1.6.1        Biobase_2.60.0        BiocGenerics_0.46.0  
 [4] igraph_1.5.0.1        dittoSeq_1.12.1       enrichplot_1.20.0    
 [7] DOSE_3.26.1           openxlsx_4.2.5.2      dict_0.10.0          
[10] clusterProfiler_4.8.2 msigdbr_7.5.1         lubridate_1.9.2      
[13] forcats_1.0.0         readr_2.1.4           ggplot2_3.4.2        
[16] tidyverse_2.0.0       biomaRt_2.56.1        ggsci_3.0.0          
[19] gsubfn_0.7            proto_1.0.0           gridExtra_2.3        
[22] Matrix_1.6-0          SeuratObject_4.1.3    Seurat_4.3.0.1       
[25] patchwork_1.1.2       stringr_1.5.0         dplyr_1.1.2          
[28] purrr_1.0.1           here_1.0.1            magrittr_2.0.3       
[31] circlize_0.4.15       tidyr_1.3.0           tibble_3.2.1         
[34] workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] IRanges_2.34.1              progress_1.2.2             
  [3] goftest_1.2-3               Biostrings_2.68.1          
  [5] vctrs_0.6.3                 spatstat.random_3.1-5      
  [7] digest_0.6.33               png_0.1-8                  
  [9] shape_1.4.6                 registry_0.5-1             
 [11] git2r_0.33.0                ggrepel_0.9.3              
 [13] org.Mm.eg.db_3.17.0         deldir_1.0-9               
 [15] parallelly_1.36.0           MASS_7.3-60                
 [17] reshape2_1.4.4              httpuv_1.6.11              
 [19] foreach_1.5.2               qvalue_2.32.0              
 [21] withr_2.5.0                 xfun_0.39                  
 [23] ggfun_0.1.1                 ggpubr_0.6.0               
 [25] ellipsis_0.3.2              survival_3.5-5             
 [27] memoise_2.0.1               gson_0.1.0                 
 [29] systemfonts_1.0.4           ragg_1.2.5                 
 [31] tidytree_0.4.4              zoo_1.8-12                 
 [33] GlobalOptions_0.1.2         pbapply_1.7-2              
 [35] prettyunits_1.1.1           KEGGREST_1.40.0            
 [37] promises_1.2.0.1            scatterplot3d_0.3-44       
 [39] httr_1.4.6                  downloader_0.4             
 [41] rstatix_0.7.2               globals_0.16.2             
 [43] fitdistrplus_1.1-11         ps_1.7.5                   
 [45] rstudioapi_0.15.0           miniUI_0.1.1.1             
 [47] generics_0.1.3              ggalluvial_0.12.5          
 [49] processx_3.8.2              babelgene_22.9             
 [51] curl_5.0.1                  S4Vectors_0.38.1           
 [53] zlibbioc_1.46.0             ggraph_2.1.0               
 [55] polyclip_1.10-4             GenomeInfoDbData_1.2.10    
 [57] SparseArray_1.2.4           xtable_1.8-4               
 [59] doParallel_1.0.17           evaluate_0.21              
 [61] S4Arrays_1.2.1              BiocFileCache_2.8.0        
 [63] hms_1.1.3                   GenomicRanges_1.52.0       
 [65] irlba_2.3.5.1               colorspace_2.1-0           
 [67] filelock_1.0.2              ggnetwork_0.5.12           
 [69] ROCR_1.0-11                 reticulate_1.36.1          
 [71] spatstat.data_3.0-1         lmtest_0.9-40              
 [73] later_1.3.1                 viridis_0.6.4              
 [75] ggtree_3.8.2                lattice_0.21-8             
 [77] spatstat.geom_3.2-4         NMF_0.26                   
 [79] future.apply_1.11.0         getPass_0.2-4              
 [81] scattermore_1.2             XML_3.99-0.14              
 [83] shadowtext_0.1.2            cowplot_1.1.1              
 [85] matrixStats_1.0.0           RcppAnnoy_0.0.21           
 [87] pillar_1.9.0                nlme_3.1-162               
 [89] sna_2.7-1                   iterators_1.0.14           
 [91] gridBase_0.4-7              compiler_4.3.1             
 [93] RSpectra_0.16-1             stringi_1.7.12             
 [95] tensor_1.5                  SummarizedExperiment_1.30.2
 [97] plyr_1.8.8                  crayon_1.5.2               
 [99] abind_1.4-5                 gridGraphics_0.5-1         
[101] sp_2.0-0                    graphlayouts_1.0.0         
[103] bit_4.0.5                   fastmatch_1.1-4            
[105] whisker_0.4.1               textshaping_0.3.6          
[107] codetools_0.2-19            bslib_0.5.0                
[109] GetoptLong_1.0.5            plotly_4.10.2              
[111] mime_0.12                   splines_4.3.1              
[113] Rcpp_1.0.11                 dbplyr_2.3.3               
[115] HDO.db_0.99.1               knitr_1.43                 
[117] blob_1.2.4                  utf8_1.2.3                 
[119] clue_0.3-64                 fs_1.6.3                   
[121] listenv_0.9.0               ggsignif_0.6.4             
[123] ggplotify_0.1.1             callr_3.7.3                
[125] svglite_2.1.1               tzdb_0.4.0                 
[127] network_1.18.1              tweenr_2.0.2               
[129] pkgconfig_2.0.3             pheatmap_1.0.12            
[131] tools_4.3.1                 cachem_1.0.8               
[133] RSQLite_2.3.1               viridisLite_0.4.2          
[135] DBI_1.1.3                   fastmap_1.1.1              
[137] rmarkdown_2.23              scales_1.2.1               
[139] grid_4.3.1                  ica_1.0-3                  
[141] broom_1.0.5                 sass_0.4.7                 
[143] coda_0.19-4                 FNN_1.1.3.2                
[145] BiocManager_1.30.21.1       Polychrome_1.5.1           
[147] carData_3.0-5               RANN_2.6.1                 
[149] farver_2.1.1                tidygraph_1.2.3            
[151] scatterpie_0.2.1            yaml_2.3.7                 
[153] MatrixGenerics_1.12.3       cli_3.6.1                  
[155] stats4_4.3.1                leiden_0.4.3               
[157] lifecycle_1.0.3             uwot_0.1.16                
[159] backports_1.4.1             BiocParallel_1.34.2        
[161] timechange_0.2.0            gtable_0.3.3               
[163] rjson_0.2.21                ggridges_0.5.4             
[165] progressr_0.13.0            limma_3.56.2               
[167] parallel_4.3.1              ape_5.7-1                  
[169] jsonlite_1.8.7              bitops_1.0-7               
[171] bit64_4.0.5                 Rtsne_0.16                 
[173] yulab.utils_0.0.6           spatstat.utils_3.1-0       
[175] BiocNeighbors_1.18.0        zip_2.3.0                  
[177] highr_0.10                  jquerylib_0.1.4            
[179] GOSemSim_2.26.1             lazyeval_0.2.2             
[181] shiny_1.7.4.1               htmltools_0.5.5            
[183] GO.db_3.17.0                sctransform_0.3.5          
[185] rappdirs_0.3.3              glue_1.6.2                 
[187] tcltk_4.3.1                 XVector_0.40.0             
[189] RCurl_1.98-1.12             rprojroot_2.0.3            
[191] treeio_1.24.3               R6_2.5.1                   
[193] SingleCellExperiment_1.22.0 labeling_0.4.2             
[195] cluster_2.1.4               rngtools_1.5.2             
[197] aplot_0.1.10                GenomeInfoDb_1.36.1        
[199] statnet.common_4.9.0        DelayedArray_0.28.0        
[201] tidyselect_1.2.0            ggforce_0.4.1              
[203] xml2_1.3.5                  car_3.1-2                  
[205] AnnotationDbi_1.62.2        future_1.33.0              
[207] munsell_0.5.0               KernSmooth_2.23-22         
[209] data.table_1.14.8           htmlwidgets_1.6.2          
[211] fgsea_1.26.0                ComplexHeatmap_2.16.0      
[213] RColorBrewer_1.1-3          rlang_1.1.1                
[215] spatstat.sparse_3.0-2       spatstat.explore_3.2-1     
[217] fansi_1.0.4                
date()
[1] "Wed Nov  6 00:17:56 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] CellChat_1.6.1        Biobase_2.60.0        BiocGenerics_0.46.0  
 [4] igraph_1.5.0.1        dittoSeq_1.12.1       enrichplot_1.20.0    
 [7] DOSE_3.26.1           openxlsx_4.2.5.2      dict_0.10.0          
[10] clusterProfiler_4.8.2 msigdbr_7.5.1         lubridate_1.9.2      
[13] forcats_1.0.0         readr_2.1.4           ggplot2_3.4.2        
[16] tidyverse_2.0.0       biomaRt_2.56.1        ggsci_3.0.0          
[19] gsubfn_0.7            proto_1.0.0           gridExtra_2.3        
[22] Matrix_1.6-0          SeuratObject_4.1.3    Seurat_4.3.0.1       
[25] patchwork_1.1.2       stringr_1.5.0         dplyr_1.1.2          
[28] purrr_1.0.1           here_1.0.1            magrittr_2.0.3       
[31] circlize_0.4.15       tidyr_1.3.0           tibble_3.2.1         
[34] workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] IRanges_2.34.1              progress_1.2.2             
  [3] goftest_1.2-3               Biostrings_2.68.1          
  [5] vctrs_0.6.3                 spatstat.random_3.1-5      
  [7] digest_0.6.33               png_0.1-8                  
  [9] shape_1.4.6                 registry_0.5-1             
 [11] git2r_0.33.0                ggrepel_0.9.3              
 [13] org.Mm.eg.db_3.17.0         deldir_1.0-9               
 [15] parallelly_1.36.0           MASS_7.3-60                
 [17] reshape2_1.4.4              httpuv_1.6.11              
 [19] foreach_1.5.2               qvalue_2.32.0              
 [21] withr_2.5.0                 xfun_0.39                  
 [23] ggfun_0.1.1                 ggpubr_0.6.0               
 [25] ellipsis_0.3.2              survival_3.5-5             
 [27] memoise_2.0.1               gson_0.1.0                 
 [29] systemfonts_1.0.4           ragg_1.2.5                 
 [31] tidytree_0.4.4              zoo_1.8-12                 
 [33] GlobalOptions_0.1.2         pbapply_1.7-2              
 [35] prettyunits_1.1.1           KEGGREST_1.40.0            
 [37] promises_1.2.0.1            scatterplot3d_0.3-44       
 [39] httr_1.4.6                  downloader_0.4             
 [41] rstatix_0.7.2               globals_0.16.2             
 [43] fitdistrplus_1.1-11         ps_1.7.5                   
 [45] rstudioapi_0.15.0           miniUI_0.1.1.1             
 [47] generics_0.1.3              ggalluvial_0.12.5          
 [49] processx_3.8.2              babelgene_22.9             
 [51] curl_5.0.1                  S4Vectors_0.38.1           
 [53] zlibbioc_1.46.0             ggraph_2.1.0               
 [55] polyclip_1.10-4             GenomeInfoDbData_1.2.10    
 [57] SparseArray_1.2.4           xtable_1.8-4               
 [59] doParallel_1.0.17           evaluate_0.21              
 [61] S4Arrays_1.2.1              BiocFileCache_2.8.0        
 [63] hms_1.1.3                   GenomicRanges_1.52.0       
 [65] irlba_2.3.5.1               colorspace_2.1-0           
 [67] filelock_1.0.2              ggnetwork_0.5.12           
 [69] ROCR_1.0-11                 reticulate_1.36.1          
 [71] spatstat.data_3.0-1         lmtest_0.9-40              
 [73] later_1.3.1                 viridis_0.6.4              
 [75] ggtree_3.8.2                lattice_0.21-8             
 [77] spatstat.geom_3.2-4         NMF_0.26                   
 [79] future.apply_1.11.0         getPass_0.2-4              
 [81] scattermore_1.2             XML_3.99-0.14              
 [83] shadowtext_0.1.2            cowplot_1.1.1              
 [85] matrixStats_1.0.0           RcppAnnoy_0.0.21           
 [87] pillar_1.9.0                nlme_3.1-162               
 [89] sna_2.7-1                   iterators_1.0.14           
 [91] gridBase_0.4-7              compiler_4.3.1             
 [93] RSpectra_0.16-1             stringi_1.7.12             
 [95] tensor_1.5                  SummarizedExperiment_1.30.2
 [97] plyr_1.8.8                  crayon_1.5.2               
 [99] abind_1.4-5                 gridGraphics_0.5-1         
[101] sp_2.0-0                    graphlayouts_1.0.0         
[103] bit_4.0.5                   fastmatch_1.1-4            
[105] whisker_0.4.1               textshaping_0.3.6          
[107] codetools_0.2-19            bslib_0.5.0                
[109] GetoptLong_1.0.5            plotly_4.10.2              
[111] mime_0.12                   splines_4.3.1              
[113] Rcpp_1.0.11                 dbplyr_2.3.3               
[115] HDO.db_0.99.1               knitr_1.43                 
[117] blob_1.2.4                  utf8_1.2.3                 
[119] clue_0.3-64                 fs_1.6.3                   
[121] listenv_0.9.0               ggsignif_0.6.4             
[123] ggplotify_0.1.1             callr_3.7.3                
[125] svglite_2.1.1               tzdb_0.4.0                 
[127] network_1.18.1              tweenr_2.0.2               
[129] pkgconfig_2.0.3             pheatmap_1.0.12            
[131] tools_4.3.1                 cachem_1.0.8               
[133] RSQLite_2.3.1               viridisLite_0.4.2          
[135] DBI_1.1.3                   fastmap_1.1.1              
[137] rmarkdown_2.23              scales_1.2.1               
[139] grid_4.3.1                  ica_1.0-3                  
[141] broom_1.0.5                 sass_0.4.7                 
[143] coda_0.19-4                 FNN_1.1.3.2                
[145] BiocManager_1.30.21.1       Polychrome_1.5.1           
[147] carData_3.0-5               RANN_2.6.1                 
[149] farver_2.1.1                tidygraph_1.2.3            
[151] scatterpie_0.2.1            yaml_2.3.7                 
[153] MatrixGenerics_1.12.3       cli_3.6.1                  
[155] stats4_4.3.1                leiden_0.4.3               
[157] lifecycle_1.0.3             uwot_0.1.16                
[159] backports_1.4.1             BiocParallel_1.34.2        
[161] timechange_0.2.0            gtable_0.3.3               
[163] rjson_0.2.21                ggridges_0.5.4             
[165] progressr_0.13.0            limma_3.56.2               
[167] parallel_4.3.1              ape_5.7-1                  
[169] jsonlite_1.8.7              bitops_1.0-7               
[171] bit64_4.0.5                 Rtsne_0.16                 
[173] yulab.utils_0.0.6           spatstat.utils_3.1-0       
[175] BiocNeighbors_1.18.0        zip_2.3.0                  
[177] highr_0.10                  jquerylib_0.1.4            
[179] GOSemSim_2.26.1             lazyeval_0.2.2             
[181] shiny_1.7.4.1               htmltools_0.5.5            
[183] GO.db_3.17.0                sctransform_0.3.5          
[185] rappdirs_0.3.3              glue_1.6.2                 
[187] tcltk_4.3.1                 XVector_0.40.0             
[189] RCurl_1.98-1.12             rprojroot_2.0.3            
[191] treeio_1.24.3               R6_2.5.1                   
[193] SingleCellExperiment_1.22.0 labeling_0.4.2             
[195] cluster_2.1.4               rngtools_1.5.2             
[197] aplot_0.1.10                GenomeInfoDb_1.36.1        
[199] statnet.common_4.9.0        DelayedArray_0.28.0        
[201] tidyselect_1.2.0            ggforce_0.4.1              
[203] xml2_1.3.5                  car_3.1-2                  
[205] AnnotationDbi_1.62.2        future_1.33.0              
[207] munsell_0.5.0               KernSmooth_2.23-22         
[209] data.table_1.14.8           htmlwidgets_1.6.2          
[211] fgsea_1.26.0                ComplexHeatmap_2.16.0      
[213] RColorBrewer_1.1-3          rlang_1.1.1                
[215] spatstat.sparse_3.0-2       spatstat.explore_3.2-1     
[217] fansi_1.0.4