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Knit directory: CCL19_FRCs_lung_cancer/

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Rmd 683e44a Pchryssa 2024-11-05 Correct figure ordering
html 79d9795 Pchryssa 2024-09-23 Build site.
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Rmd 5a7acbb Pchryssa 2024-08-21 NSCLC FRC TIL merge

Load packages

suppressPackageStartupMessages({
  library(here)
  library(purrr)
  library(stringr)
  library(patchwork)
  library(Seurat)
  library(Matrix)
  library(dittoSeq)
  library(gridExtra)
  library(CellChat)
  library(NMF)
  library(pheatmap)
})

Infiltrating lymphocytes in NSCLC

Set directory

basedir <- here()

Merge CCL19⁺ FRCs and Infiltrating lymphocytes in NSCLC

Read NSCLC TIL and FRC data

NSCLS_TIL_data <-readRDS(paste0(basedir,"/data/Human/NSCLC_TILs.rds"))
NSCLC_CCL19_FRCs <-readRDS(paste0(basedir,"/data/Human/NSCLC_CCL19_FRCs_CAFs.rds"))

Merge NSCLS CCL19⁺ FRCs and NSCLS TILs

same_columns <- intersect(colnames(NSCLS_TIL_data@meta.data),colnames(NSCLC_CCL19_FRCs@meta.data))

NSCLS_TIL_data@meta.data <-NSCLS_TIL_data@meta.data[,same_columns]
NSCLC_CCL19_FRCs@meta.data <-NSCLC_CCL19_FRCs@meta.data[,same_columns]

merged_data<- merge(NSCLS_TIL_data, y = c(NSCLC_CCL19_FRCs),
             add.cell.ids = c('NSCLS_TIL_data','NSCLC_CCL19_FRCs'),
             project = "NSCLC_FRC_TIL")

resolution <- c(0.1, 0.25, 0.4, 0.6, 0.8, 1.,1.2,1.4,1.6,2.)
merged_data  <- preprocessing(merged_data,resolution)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9696
Number of communities: 7
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9427
Number of communities: 11
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9254
Number of communities: 13
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9050
Number of communities: 14
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8897
Number of communities: 19
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8772
Number of communities: 22
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8652
Number of communities: 23
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8546
Number of communities: 26
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8448
Number of communities: 27
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 12217
Number of edges: 420063

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8272
Number of communities: 30
Elapsed time: 1 seconds

NSCLS CCL19⁺ FRCs and NSCLS TILs

#Define color palet
palet <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C", "#4DAF4A","#FB9A99","#377EB8","#A65628","#222F75")
names(palet) <- c( "CAF2/TRC","CAF1/PRC","AdvFB" ,"SMC/PC", paste0("CD4", "\u207A ", "T cells"), paste0("CD8", "\u207A ", "T cells"), "B cells", "Regulatory T cells",paste0("Cycling CD8", "\u207A ", "T cells"))

DimPlot(merged_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("NSCLC CCL19", "\U207A ", "FRCs", " and TILs"))

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Save merged data

#saveRDS(merged_data, paste0(basedir,"/data/Human/NSCLC_TILs_SI4.rds"))

Interactome Analysis with Cellchat (Suoqin Jin et al., 2021)

Convert seurat object to cellchat object

cellchat <- Cellchat_Analysis(merged_data)
[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  AdvFB B cells CAF1/PRC CAF2/TRC CD4⁺ T cells CD8⁺ T cells Cycling CD8⁺ T cells Regulatory T cells SMC/PC 
cellchat <-CellChatDownstreamAnalysis(cellchat,"human",thresh = 0.05)
triMean is used for calculating the average gene expression per cell group. 
[1] ">>> Run CellChat on sc/snRNA-seq data <<< [2024-11-05 21:52:19.838019]"
[1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2024-11-05 21:53:53.902528]"

Save cellchat data

#saveRDS(cellchat,paste0(basedir,"/data/Human/CCL19_FRC_TIL_SIF5_cellchat.rds"))

Interactome analysis (Supplementary Figure 3D)

gg <- netAnalysis_signalingRole_scatter(cellchat,color.use = palet)
gg <- gg + ggtitle("Interactome analysis (Cellchat)")
gg

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Outgoing signaling patterns

selectK(cellchat, pattern = "outgoing")

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Incoming signaling patterns

selectK(cellchat, pattern = "incoming")

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Incoming and outgoing patterns

nPatterns <- 7
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns, color.use = palet)
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns, color.use = palet)

Joint dotplot for incoming and outcoming communication patterns (Supplementary Figure 3E)

pathways <-c("CXCL","CCL","ICAM","VCAM","MK","FGF","NECTIN","TIGIT","SELL","ITGB2","NOTCH","BAFF","SEMA3","SEMA4","LIGHT","EGF","IFN-II","CD226","IGF","FASLG")

order_list <-c("CAF2/TRC","CAF1/PRC","SMC/PC","AdvFB","Regulatory T cells", "CD4⁺ T cells","CD8⁺ T cells","Cycling CD8⁺ T cells","B cells")

netAnalysis_joint_dot(cellchat,color.use = palet,font.size = 12,pathways = pathways, order_list = order_list)

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Merge CCL19⁺ FRCs and CD8⁺ T cell subsets in NSCLC

Read CD8⁺ T cell data

NCLS_TIL_FRC_cd8 <-readRDS(paste0(basedir,"/data/Human/NSCLC_TILs_CD8_pop.rds"))

Merge NSCLS CCL19⁺ FRCs and NSCLS CD8⁺ T cell subsets

same_columns <- intersect(colnames(NCLS_TIL_FRC_cd8@meta.data),colnames(NSCLC_CCL19_FRCs@meta.data))

NCLS_TIL_FRC_cd8@meta.data <-NCLS_TIL_FRC_cd8@meta.data[,same_columns]
NSCLC_CCL19_FRCs@meta.data <-NSCLC_CCL19_FRCs@meta.data[,same_columns]

merged_data<- merge(NCLS_TIL_FRC_cd8, y = c(NSCLC_CCL19_FRCs),
             add.cell.ids = c('NCLS_TIL_FRC_cd8','NSCLC_CCL19_FRCs'),
             project = "NSCLC_FRC_TIL_CD8")

resolution <- c(0.1, 0.25, 0.4, 0.6, 0.8, 1.,1.2,1.4,1.6,2.)
merged_data  <- preprocessing(merged_data,resolution)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9616
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: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9304
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: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9108
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: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8875
Number of communities: 16
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8721
Number of communities: 20
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8585
Number of communities: 22
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8473
Number of communities: 24
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8361
Number of communities: 23
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8245
Number of communities: 24
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 10459
Number of edges: 356757

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8046
Number of communities: 30
Elapsed time: 0 seconds

NSCLS CCL19⁺ FRCs and NSCLS CD8⁺ T cell subsets

#Extend palet for CD8 T cell subsets
palet <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C","#F8766D","#00C08B","#7CAE00","#00B4F0","#F564E3")
names(palet) <- c("CAF2/TRC","CAF1/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(merged_data$cell_type)]

DimPlot(merged_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")

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NSCLS CCL19⁺ FRCs and NSCLS CD8⁺ T cell data

#saveRDS(merged_data, paste0(basedir,"/data/Human/NSCLC_TILs_CD8_pop_merge.rds"))

Interactome Analysis with Cellchat (Suoqin Jin et al., 2021)

Convert seurat object to cellchat object

cellchat <- Cellchat_Analysis(merged_data)
[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  AdvFB CAF1/PRC CAF2/TRC Cycling CD8⁺ T cells Effector CD8⁺ T cells Effector-memory CD8⁺ T cells Exhausted CD8⁺ T cells SMC/PC Stem-like/Naive CD8⁺ T cells 
cellchat <-CellChatDownstreamAnalysis(cellchat,"human",thresh=0.08)
triMean is used for calculating the average gene expression per cell group. 
[1] ">>> Run CellChat on sc/snRNA-seq data <<< [2024-11-05 22:00:33.638104]"
[1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2024-11-05 22:02:02.135343]"

Save cellchat data

#saveRDS(cellchat,paste0(basedir,"/data/Human/CCL19_FRC_TIL_F3E_cellchat.rds"))

Interactome analysis (Figure 3E)

gg <- netAnalysis_signalingRole_scatter(cellchat,color.use = palet)
gg <- gg + ggtitle("Interactome analysis (Cellchat)")
gg

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Outgoing signaling patterns

selectK(cellchat, pattern = "outgoing")

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Incoming signaling patterns

selectK(cellchat, pattern = "incoming")

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Incoming and outgoing patterns

nPatterns <- 8
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns, color.use = palet)
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns, color.use = palet)

Joint dotplot for incoming and outcoming communication patterns (Figure 3F)

pathways <-c("MHC-II","LAMININ","FN1","CXCL","CCL","VCAM","ICAM","ITGB2","LIGHT","NECTIN","TIGIT","CD226","EGF","FASLG","SEMA4","IFN-II","IL16","GDF")

order_list <-c("CAF2/TRC","CAF1/PRC",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"))

netAnalysis_joint_dot(cellchat,color.use = palet,font.size = 8,pathways = pathways, order_list = order_list, exclude = c("AdvFB" ,"SMC/PC"))

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Attraction and adhesion (Figure 3G)

Heatmap stroma

Selgenes <- c("VCAM1","CXCL16", "CD34","CXCL12")
order_list <-c("CAF1/PRC","SMC/PC", "CAF2/TRC","AdvFB")
data_subset <- subset(merged_data, cell_type  %in% order_list)
  
Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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Heatmap CD8⁺ T cell

Selgenes <- c("ITGA4","ITGB1","ITGB7","CXCR6","SELL","CXCR4")
order_list <-c(paste0("Stem-like/Naive CD8", "\u207A ", "T cells"), 
               paste0("Effector-memory CD8", "\u207A ", "T cells"),
               paste0("Effector CD8", "\u207A ", "T cells"),
               paste0("Exhausted CD8", "\u207A ", "T cells"),
               paste0("Cycling CD8", "\u207A ", "T cells") )
data_subset <- subset(merged_data, cell_type  %in% order_list)

Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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T cell reactivity (Figure 3H)

Heatmap stroma

Selgenes <- c("NECTIN3","NECTIN2","EGFR","FAS","IFNGR1","IFNGR2")
order_list <-c("CAF1/PRC","SMC/PC", "CAF2/TRC","AdvFB")
data_subset <- subset(merged_data, cell_type  %in% order_list)
  
Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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Heatmap CD8⁺ T cell

Selgenes <- c("TIGIT","CD226","AREG","FASLG","IFNG")
order_list <-c(paste0("Stem-like/Naive CD8", "\u207A ", "T cells"), 
               paste0("Effector-memory CD8", "\u207A ", "T cells"),
               paste0("Effector CD8", "\u207A ", "T cells"),
               paste0("Exhausted CD8", "\u207A ", "T cells"),
               paste0("Cycling CD8", "\u207A ", "T cells") )
data_subset <- subset(merged_data, cell_type  %in% order_list)

Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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Niche formation (Figure 3I)

Heatmap stroma

Selgenes <- c("LTBR","TNFRSF14","NOTCH3","JAG1")
order_list <-c("CAF1/PRC","SMC/PC", "CAF2/TRC","AdvFB")
data_subset <- subset(merged_data, cell_type  %in% order_list)
  
Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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Heatmap CD8⁺ T cell

Selgenes <- c("LTA","LTB","TNFSF14","DLL1","JAG1")
order_list <-c(paste0("Stem-like/Naive CD8", "\u207A ", "T cells"), 
               paste0("Effector-memory CD8", "\u207A ", "T cells"),
               paste0("Effector CD8", "\u207A ", "T cells"),
               paste0("Exhausted CD8", "\u207A ", "T cells"),
               paste0("Cycling CD8", "\u207A ", "T cells") )
data_subset <- subset(merged_data, cell_type  %in% order_list)

Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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Heatmaps (Figure 3J)

LTBR regulated

Selgenes <- c("CLU","TNFSF13B","MFGE8")
order_list <-c("CAF1/PRC","SMC/PC", "CAF2/TRC","AdvFB")
data_subset <- subset(merged_data, cell_type  %in% order_list)
  
Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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TNFRSF14 regulated

Selgenes <- c("DKK1", "IL32", "IL34")
order_list <-c("CAF1/PRC","SMC/PC", "CAF2/TRC","AdvFB")
data_subset <- subset(merged_data, cell_type  %in% order_list)
  
Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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NOTCH3 regulated

Selgenes <- c("HEY1", "HEY2", "HEYL")
order_list <-c("CAF1/PRC","SMC/PC", "CAF2/TRC","AdvFB")
data_subset <- subset(merged_data, cell_type  %in% order_list)
  
Heatmap(data_subset, Selgenes, order_list = order_list, palet = palet)

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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] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] doParallel_1.0.17   iterators_1.0.14    foreach_1.5.2      
 [4] pheatmap_1.0.12     NMF_0.26            cluster_2.1.4      
 [7] rngtools_1.5.2      registry_0.5-1      CellChat_1.6.1     
[10] Biobase_2.60.0      BiocGenerics_0.46.0 igraph_1.5.0.1     
[13] dplyr_1.1.2         gridExtra_2.3       dittoSeq_1.12.1    
[16] ggplot2_3.4.2       Matrix_1.6-0        SeuratObject_4.1.3 
[19] Seurat_4.3.0.1      patchwork_1.1.2     stringr_1.5.0      
[22] purrr_1.0.1         here_1.0.1          magrittr_2.0.3     
[25] circlize_0.4.15     tidyr_1.3.0         tibble_3.2.1       
[28] 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             ggnetwork_0.5.12           
  [7] lifecycle_1.0.3             rstatix_0.7.2              
  [9] rprojroot_2.0.3             globals_0.16.2             
 [11] processx_3.8.2              lattice_0.21-8             
 [13] MASS_7.3-60                 backports_1.4.1            
 [15] plotly_4.10.2               sass_0.4.7                 
 [17] rmarkdown_2.23              jquerylib_0.1.4            
 [19] yaml_2.3.7                  httpuv_1.6.11              
 [21] sctransform_0.3.5           sp_2.0-0                   
 [23] spatstat.sparse_3.0-2       reticulate_1.36.1          
 [25] cowplot_1.1.1               pbapply_1.7-2              
 [27] RColorBrewer_1.1-3          abind_1.4-5                
 [29] zlibbioc_1.46.0             Rtsne_0.16                 
 [31] GenomicRanges_1.52.0        RCurl_1.98-1.12            
 [33] git2r_0.33.0                GenomeInfoDbData_1.2.10    
 [35] IRanges_2.34.1              S4Vectors_0.38.1           
 [37] ggrepel_0.9.3               irlba_2.3.5.1              
 [39] listenv_0.9.0               spatstat.utils_3.1-0       
 [41] RSpectra_0.16-1             goftest_1.2-3              
 [43] spatstat.random_3.1-5       fitdistrplus_1.1-11        
 [45] parallelly_1.36.0           svglite_2.1.1              
 [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] GetoptLong_1.0.5            BiocNeighbors_1.18.0       
 [59] ellipsis_0.3.2              progressr_0.13.0           
 [61] ggridges_0.5.4              ggalluvial_0.12.5          
 [63] survival_3.5-5              systemfonts_1.0.4          
 [65] tools_4.3.1                 ragg_1.2.5                 
 [67] sna_2.7-1                   ica_1.0-3                  
 [69] Rcpp_1.0.11                 glue_1.6.2                 
 [71] SparseArray_1.2.4           xfun_0.39                  
 [73] MatrixGenerics_1.12.3       GenomeInfoDb_1.36.1        
 [75] withr_2.5.0                 BiocManager_1.30.21.1      
 [77] fastmap_1.1.1               fansi_1.0.4                
 [79] callr_3.7.3                 digest_0.6.33              
 [81] R6_2.5.1                    mime_0.12                  
 [83] textshaping_0.3.6           colorspace_2.1-0           
 [85] Cairo_1.6-1                 scattermore_1.2            
 [87] tensor_1.5                  spatstat.data_3.0-1        
 [89] utf8_1.2.3                  generics_0.1.3             
 [91] data.table_1.14.8           FNN_1.1.3.2                
 [93] httr_1.4.6                  htmlwidgets_1.6.2          
 [95] S4Arrays_1.2.1              whisker_0.4.1              
 [97] uwot_0.1.16                 pkgconfig_2.0.3            
 [99] gtable_0.3.3                ComplexHeatmap_2.16.0      
[101] lmtest_0.9-40               SingleCellExperiment_1.22.0
[103] XVector_0.40.0              htmltools_0.5.5            
[105] carData_3.0-5               clue_0.3-64                
[107] scales_1.2.1                png_0.1-8                  
[109] knitr_1.43                  rstudioapi_0.15.0          
[111] rjson_0.2.21                reshape2_1.4.4             
[113] coda_0.19-4                 statnet.common_4.9.0       
[115] nlme_3.1-162                cachem_1.0.8               
[117] zoo_1.8-12                  GlobalOptions_0.1.2        
[119] KernSmooth_2.23-22          miniUI_0.1.1.1             
[121] pillar_1.9.0                grid_4.3.1                 
[123] vctrs_0.6.3                 RANN_2.6.1                 
[125] ggpubr_0.6.0                promises_1.2.0.1           
[127] car_3.1-2                   xtable_1.8-4               
[129] evaluate_0.21               cli_3.6.1                  
[131] compiler_4.3.1              rlang_1.1.1                
[133] crayon_1.5.2                ggsignif_0.6.4             
[135] future.apply_1.11.0         labeling_0.4.2             
[137] ps_1.7.5                    forcats_1.0.0              
[139] getPass_0.2-4               plyr_1.8.8                 
[141] fs_1.6.3                    stringi_1.7.12             
[143] network_1.18.1              BiocParallel_1.34.2        
[145] viridisLite_0.4.2           deldir_1.0-9               
[147] gridBase_0.4-7              munsell_0.5.0              
[149] lazyeval_0.2.2              spatstat.geom_3.2-4        
[151] future_1.33.0               shiny_1.7.4.1              
[153] highr_0.10                  SummarizedExperiment_1.30.2
[155] ROCR_1.0-11                 broom_1.0.5                
[157] bslib_0.5.0                
date()
[1] "Tue Nov  5 22:05:11 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] parallel  stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] doParallel_1.0.17   iterators_1.0.14    foreach_1.5.2      
 [4] pheatmap_1.0.12     NMF_0.26            cluster_2.1.4      
 [7] rngtools_1.5.2      registry_0.5-1      CellChat_1.6.1     
[10] Biobase_2.60.0      BiocGenerics_0.46.0 igraph_1.5.0.1     
[13] dplyr_1.1.2         gridExtra_2.3       dittoSeq_1.12.1    
[16] ggplot2_3.4.2       Matrix_1.6-0        SeuratObject_4.1.3 
[19] Seurat_4.3.0.1      patchwork_1.1.2     stringr_1.5.0      
[22] purrr_1.0.1         here_1.0.1          magrittr_2.0.3     
[25] circlize_0.4.15     tidyr_1.3.0         tibble_3.2.1       
[28] 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             ggnetwork_0.5.12           
  [7] lifecycle_1.0.3             rstatix_0.7.2              
  [9] rprojroot_2.0.3             globals_0.16.2             
 [11] processx_3.8.2              lattice_0.21-8             
 [13] MASS_7.3-60                 backports_1.4.1            
 [15] plotly_4.10.2               sass_0.4.7                 
 [17] rmarkdown_2.23              jquerylib_0.1.4            
 [19] yaml_2.3.7                  httpuv_1.6.11              
 [21] sctransform_0.3.5           sp_2.0-0                   
 [23] spatstat.sparse_3.0-2       reticulate_1.36.1          
 [25] cowplot_1.1.1               pbapply_1.7-2              
 [27] RColorBrewer_1.1-3          abind_1.4-5                
 [29] zlibbioc_1.46.0             Rtsne_0.16                 
 [31] GenomicRanges_1.52.0        RCurl_1.98-1.12            
 [33] git2r_0.33.0                GenomeInfoDbData_1.2.10    
 [35] IRanges_2.34.1              S4Vectors_0.38.1           
 [37] ggrepel_0.9.3               irlba_2.3.5.1              
 [39] listenv_0.9.0               spatstat.utils_3.1-0       
 [41] RSpectra_0.16-1             goftest_1.2-3              
 [43] spatstat.random_3.1-5       fitdistrplus_1.1-11        
 [45] parallelly_1.36.0           svglite_2.1.1              
 [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] GetoptLong_1.0.5            BiocNeighbors_1.18.0       
 [59] ellipsis_0.3.2              progressr_0.13.0           
 [61] ggridges_0.5.4              ggalluvial_0.12.5          
 [63] survival_3.5-5              systemfonts_1.0.4          
 [65] tools_4.3.1                 ragg_1.2.5                 
 [67] sna_2.7-1                   ica_1.0-3                  
 [69] Rcpp_1.0.11                 glue_1.6.2                 
 [71] SparseArray_1.2.4           xfun_0.39                  
 [73] MatrixGenerics_1.12.3       GenomeInfoDb_1.36.1        
 [75] withr_2.5.0                 BiocManager_1.30.21.1      
 [77] fastmap_1.1.1               fansi_1.0.4                
 [79] callr_3.7.3                 digest_0.6.33              
 [81] R6_2.5.1                    mime_0.12                  
 [83] textshaping_0.3.6           colorspace_2.1-0           
 [85] Cairo_1.6-1                 scattermore_1.2            
 [87] tensor_1.5                  spatstat.data_3.0-1        
 [89] utf8_1.2.3                  generics_0.1.3             
 [91] data.table_1.14.8           FNN_1.1.3.2                
 [93] httr_1.4.6                  htmlwidgets_1.6.2          
 [95] S4Arrays_1.2.1              whisker_0.4.1              
 [97] uwot_0.1.16                 pkgconfig_2.0.3            
 [99] gtable_0.3.3                ComplexHeatmap_2.16.0      
[101] lmtest_0.9-40               SingleCellExperiment_1.22.0
[103] XVector_0.40.0              htmltools_0.5.5            
[105] carData_3.0-5               clue_0.3-64                
[107] scales_1.2.1                png_0.1-8                  
[109] knitr_1.43                  rstudioapi_0.15.0          
[111] rjson_0.2.21                reshape2_1.4.4             
[113] coda_0.19-4                 statnet.common_4.9.0       
[115] nlme_3.1-162                cachem_1.0.8               
[117] zoo_1.8-12                  GlobalOptions_0.1.2        
[119] KernSmooth_2.23-22          miniUI_0.1.1.1             
[121] pillar_1.9.0                grid_4.3.1                 
[123] vctrs_0.6.3                 RANN_2.6.1                 
[125] ggpubr_0.6.0                promises_1.2.0.1           
[127] car_3.1-2                   xtable_1.8-4               
[129] evaluate_0.21               cli_3.6.1                  
[131] compiler_4.3.1              rlang_1.1.1                
[133] crayon_1.5.2                ggsignif_0.6.4             
[135] future.apply_1.11.0         labeling_0.4.2             
[137] ps_1.7.5                    forcats_1.0.0              
[139] getPass_0.2-4               plyr_1.8.8                 
[141] fs_1.6.3                    stringi_1.7.12             
[143] network_1.18.1              BiocParallel_1.34.2        
[145] viridisLite_0.4.2           deldir_1.0-9               
[147] gridBase_0.4-7              munsell_0.5.0              
[149] lazyeval_0.2.2              spatstat.geom_3.2-4        
[151] future_1.33.0               shiny_1.7.4.1              
[153] highr_0.10                  SummarizedExperiment_1.30.2
[155] ROCR_1.0-11                 broom_1.0.5                
[157] bslib_0.5.0