NSCLC stroma and CCL19 cells

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Last updated: 2024-11-05

Checks: 7 0

Knit directory: CCL19_FRCs_lung_cancer/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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    Ignored:    .DS_Store
    Ignored:    analysis/.DS_Store
    Ignored:    data/Final_submission/
    Ignored:    data/Human/
    Ignored:    data/Mouse/
    Ignored:    data/Public/
    Ignored:    output/GSEA_AdvFB_SULF1/
    Ignored:    output/GSEA_AdvFB_TLS/
    Ignored:    output/GSEA_CCR7_T/
    Ignored:    output/GSEA_CD8_T/
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Untracked files:
    Untracked:  README.html
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    Untracked:  analysis/NSCLC_PDAC_CAFs.Rmd
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/NSCLC_stroma_CCCL19_FRC.Rmd) and HTML (docs/NSCLC_stroma_CCCL19_FRC.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	6cb934f	Pchryssa	2024-11-05	Correct figure ordering
html	f0fd3ea	Pchryssa	2024-09-23	Build site.
Rmd	673cd8d	Pchryssa	2024-09-23	Modify figure order
html	957f2fa	Pchryssa	2024-08-22	Build site.
Rmd	3ff85ba	Pchryssa	2024-08-22	NSCLC CCL19 FRCs
html	d4abf9c	Pchryssa	2024-08-21	Build site.
Rmd	56cd3c3	Pchryssa	2024-08-21	NSCLC stroma CCCL19 FRC

---

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)
})

Stroma cells in NSCLC

Set directory

basedir <- here()

Read Stroma cell data

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

Define color palette

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

NSCLC Stroma cells (Supplementary Figure 1K-1L)

Patients

# Total fibroblasts and endothelial cells across NSCLC patients
colors_pID <-c("#F8766D","#00C08B","#00B4F0","#0ADD08","#B79F00")
names(colors_pID) <-c("NSCLC#2","NSCLC#3","NSCLC#4","NSCLC#6","NSCLC#7")

DimPlot(data, reduction = "tsne", group.by = "patient", cols=colors_pID)+ 
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  xlab("tSNE1") +
  ylab("tSNE2") + ggtitle("Patients")

Past versions of "umap per patient-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Origin

# Total fibroblasts and endothelial cells isolated from SM, CM and unaffected lung (LU)
colors_origin <-c("red","blue" ,"#33CC00FF") 
names(colors_origin) <- c("Subpleural Margin","Lung (unaffected)","Central Margin")

DimPlot(data, reduction = "tsne", group.by = "origin", cols=colors_origin )+ 
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  xlab("tSNE1") +
  ylab("tSNE2") + ggtitle("Origin")

Past versions of "umap per origin-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

COL1A2

FeaturePlot(data, reduction = "tsne", 
          features = get_full_gene_name('COL1A2',data),raster=FALSE,
          cols=c("lightgrey", "darkred")) + ggtitle("CAF/FB(COL1A2)")

Past versions of "feature plot COL1A2-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

PECAM1

FeaturePlot(data, reduction = "tsne", 
          features = get_full_gene_name('PECAM1',data),raster=FALSE,
          cols=c("lightgrey", "darkred")) + ggtitle("EC(PECAM1)")

Past versions of "feature plot PECAM1-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Cell type

palet <- cols[4:10]
names(palet) <- c("CAF2","CAF1", "EC", "FB" ,"Meso","SMC/PC")

DimPlot(data, reduction = "tsne", 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("TSNE1") +
ylab("TSNE2") 

Past versions of "umap per cell type-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Dotplots (Supplementary Figure 1M and 1P)

Cell type assignment

data_conv <-data
data_conv <-Remove_ensebl_id(data_conv)

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

gene_list <-c("COL1A2","POSTN","MMP2","PDPN","PDGFRA","PDGFRB","ACTA2","RGS5","KRT19","PECAM1")

gg <- dittoDotPlot(data_conv, vars = gene_list, group.by = "cell_type", size = 9,legend.size.title = "% expressed",scale = FALSE,summary.fxn.color = mean, max = 6.5, min = 0 , min.color = "#D1E5F0" , max.color = "#631879FF")

gg + ggtitle("Celltype assignment")

Past versions of "dotplot stroma-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Chemokines

gene_list <-c("CCL19","CCL21","CCL3","CCL5","CCL8","CXCL10","CXCL3","CXCL9")

gg <- dittoDotPlot(data_conv, vars = gene_list, group.by = "cell_type", size = 9,legend.size.title = "% expressed",scale = FALSE,summary.fxn.color = mean, min.percent = 0.02, max.percent = 0.7, max = 1.6, min = 0 ,min.color = "#D1E5F0" , max.color = "#631879FF")

gg + ggtitle("Chemokines")

Past versions of "dotplot chemokines-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Stacked Barplots (Supplementary Figure 1N and 1O)

Number of cells per patient

df <-data@meta.data %>% count(patient, cell_type) %>%    # Group by patient and cell_type, then count number in each group
         mutate(pct=n/sum(n))                            # Calculate percent within each patient

df$cell_type <- factor(df$cell_type, levels=names(palet))
df$patient <- factor(df$patient, levels=c("NSCLC#2", "NSCLC#3" ,"NSCLC#4" ,"NSCLC#6" ,"NSCLC#7"))

ggplot(df, aes(patient, n, fill=cell_type)) +
  geom_bar(stat="identity") +
  theme(  axis.line = element_line(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background = element_blank()) +
  scale_y_continuous(expand = c(0, 0))+
  labs(y= "Cells", x= " ") +
  scale_fill_manual(values = palet)

Past versions of "stacked barplot cells per patient-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Number of cells per origin

df <-data@meta.data %>% count(origin, cell_type) %>%    # Group by orign and cell_type, then count number in each group
         mutate(pct=n/sum(n))                           # Calculate percent within each patient

df$cell_type <- factor(df$cell_type, levels=names(palet))
df$origin <- factor(df$origin, levels=c("Lung (unaffected)", "Subpleural Margin", "Central Margin"))

ggplot(df,aes(origin, n, fill=cell_type)) +
  geom_bar(stat="identity") +
  theme(  axis.line = element_line(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background = element_blank()) +
  scale_y_continuous(expand = c(0, 0))+
  labs(y= "Cells", x= " ") +
  scale_fill_manual(values = palet)

Past versions of "stacked barplot cells per origin-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

CCL19⁺ FRCs in NSCLC

Read CCL19⁺ FRC data

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

CCL19⁺ fibroblasts (Figure 1K)

#Define color palet
palet_CCL19_FRC <- c("#1B9E77", "#54B0E4","#E3BE00", "#E41A1C")
names(palet_CCL19_FRC) <- c("CAF2/TRC","CAF1/PRC","AdvFB" ,"SMC/PC")

palet_CCL19_FRC <- palet_CCL19_FRC[names(palet_CCL19_FRC) %in% unique(NSCLC_CCL19_data$cell_type)]

DimPlot(NSCLC_CCL19_data, reduction = "umap", group.by = "cell_type",cols = palet_CCL19_FRC)+
  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", "\U207A ", "fibroblasts"))

Past versions of "umap CCL19 FRCs)-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Dotplot (Figure 1L)

data_conv <-NSCLC_CCL19_data

data_conv <-Remove_ensebl_id(data_conv)

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

gene_list <-c("CCL19","CCL21","PDPN","FAP","POSTN","CLU","LEPR","CD34","SULF1","DPT","ICAM1","VCAM1","ACTA2","MYH11",
"MCAM","NOTCH3","RGS5","DES","AIFM2")

dittoDotPlot(data_conv, vars = gene_list, group.by = "cell_type", size = 8,legend.size.title = "Expression (%)",scale = FALSE) + ylab(" ") + ggtitle(paste0("CCL19", expression("\u207A"), " fibroblasts"))

Past versions of dotplot-1.png

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Signatures

SLO-PRC (Figure 1M)

SLO_PRC <-list("CCL19","CCL21","ITGA1","ITGA7","MCAM","CNN1","NOTCH3","ACTA2","PDGFRB","ANGPT2")
object <- AddModuleScore(object = data_conv, features = SLO_PRC, name = "SLO_PRC_signature",ctrl = 20)
FeaturePlot(object = object, features = "SLO_PRC_signature10",min.cutoff = -1, max.cutoff = 2.5) + ggtitle("SLO-PRC signature")

Past versions of "sign SLO-PRC-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

SLO-TRC (Figure 1N)

SLO_TRC <-c("CCL19","CCL21","PDPN","ICAM1","VCAM1","LUM","PDGFRA","TNFSF13B")
object <- AddModuleScore(object = data_conv, features = SLO_TRC, name = "SLO_TRC_signature",ctrl = 20)
FeaturePlot(object = object, features = "SLO_TRC_signature8",min.cutoff = -1, max.cutoff = 2.5) + ggtitle("SLO-TRC signature")

Past versions of "sign SLO-TRC-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Signatures (Supplementary Figure 1Q)

CCL21

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('CCL21',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("CCL21")

Past versions of "feature plot CCL21-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

ACTA2

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('ACTA2',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("ACTA2")

Past versions of "feature plot ACTA2-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

ITGA1

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('ITGA1',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("ITGA1")

Past versions of "feature plot ITGA1-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

NOTCH3

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('NOTCH3',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("NOTCH3")

Past versions of "feature plot NOTCH3-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

MCAM

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('MCAM',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("MCAM")

Past versions of "feature plot MCAM-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

CCL19

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('CCL19',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("CCL19")

Past versions of "feature plot CCL19-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

PDPN

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('PDPN',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("PDPN")

Past versions of "feature plot PDPN-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

ICAM1

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('ICAM1',NSCLC_CCL19_data)[2],raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("ICAM1")

Past versions of "feature plot ICAM1-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

VCAM1

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('VCAM1',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("VCAM1")

Past versions of "feature plot VCAM1-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

LUM

FeaturePlot(NSCLC_CCL19_data, reduction = "umap", 
          features = get_full_gene_name('LUM',NSCLC_CCL19_data),raster=FALSE,
          cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("LUM")

Past versions of "feature plot LUM-1.png"

Version	Author	Date
80d46cf	Pchryssa	2024-08-26

Extract NSCLC TRC and PRC

NCLS_FRCS <- subset(NSCLC_CCL19_data, cell_type %in% c("CAF2/TRC","CAF1/PRC"))

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

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9474
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: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9089
Number of communities: 8
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8838
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: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8560
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: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8432
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: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8315
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: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8227
Number of communities: 16
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8139
Number of communities: 17
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7990
Number of communities: 17
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7839
Number of communities: 18
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7705
Number of communities: 21
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7591
Number of communities: 22
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 5023
Number of edges: 172182

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7483
Number of communities: 23
Elapsed time: 0 seconds

CCL19⁺ TRC and PRC

Save NSCLC CCL19⁺ TRC PRC data

#saveRDS(NCLS_FRCS, paste0(basedir,"/data/Human/NSCLC_CCL19_TRC_PRC_CAFs.rds"))

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] ggsci_3.0.0        gsubfn_0.7         proto_1.0.0        gridExtra_2.3     
 [5] dittoSeq_1.12.1    ggplot2_3.4.2      Matrix_1.6-0       SeuratObject_4.1.3
 [9] Seurat_4.3.0.1     patchwork_1.1.2    stringr_1.5.0      dplyr_1.1.2       
[13] purrr_1.0.1        here_1.0.1         magrittr_2.0.3     circlize_0.4.15   
[17] tidyr_1.3.0        tibble_3.2.1       workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.15.0          
  [3] jsonlite_1.8.7              shape_1.4.6                
  [5] spatstat.utils_3.1-0        farver_2.1.1               
  [7] rmarkdown_2.23              ragg_1.2.5                 
  [9] zlibbioc_1.46.0             GlobalOptions_0.1.2        
 [11] fs_1.6.3                    vctrs_0.6.3                
 [13] ROCR_1.0-11                 spatstat.explore_3.2-1     
 [15] RCurl_1.98-1.12             S4Arrays_1.2.1             
 [17] htmltools_0.5.5             SparseArray_1.2.4          
 [19] sass_0.4.7                  sctransform_0.3.5          
 [21] parallelly_1.36.0           KernSmooth_2.23-22         
 [23] bslib_0.5.0                 htmlwidgets_1.6.2          
 [25] ica_1.0-3                   plyr_1.8.8                 
 [27] plotly_4.10.2               zoo_1.8-12                 
 [29] cachem_1.0.8                whisker_0.4.1              
 [31] igraph_1.5.0.1              mime_0.12                  
 [33] lifecycle_1.0.3             pkgconfig_2.0.3            
 [35] R6_2.5.1                    fastmap_1.1.1              
 [37] GenomeInfoDbData_1.2.10     MatrixGenerics_1.12.3      
 [39] fitdistrplus_1.1-11         future_1.33.0              
 [41] shiny_1.7.4.1               digest_0.6.33              
 [43] colorspace_2.1-0            S4Vectors_0.38.1           
 [45] ps_1.7.5                    rprojroot_2.0.3            
 [47] tensor_1.5                  irlba_2.3.5.1              
 [49] textshaping_0.3.6           GenomicRanges_1.52.0       
 [51] labeling_0.4.2              progressr_0.13.0           
 [53] fansi_1.0.4                 spatstat.sparse_3.0-2      
 [55] httr_1.4.6                  polyclip_1.10-4            
 [57] abind_1.4-5                 compiler_4.3.1             
 [59] withr_2.5.0                 highr_0.10                 
 [61] MASS_7.3-60                 DelayedArray_0.28.0        
 [63] tools_4.3.1                 lmtest_0.9-40              
 [65] httpuv_1.6.11               future.apply_1.11.0        
 [67] goftest_1.2-3               glue_1.6.2                 
 [69] callr_3.7.3                 nlme_3.1-162               
 [71] promises_1.2.0.1            grid_4.3.1                 
 [73] Rtsne_0.16                  getPass_0.2-4              
 [75] cluster_2.1.4               reshape2_1.4.4             
 [77] generics_0.1.3              gtable_0.3.3               
 [79] spatstat.data_3.0-1         data.table_1.14.8          
 [81] XVector_0.40.0              sp_2.0-0                   
 [83] utf8_1.2.3                  BiocGenerics_0.46.0        
 [85] spatstat.geom_3.2-4         RcppAnnoy_0.0.21           
 [87] ggrepel_0.9.3               RANN_2.6.1                 
 [89] pillar_1.9.0                later_1.3.1                
 [91] splines_4.3.1               lattice_0.21-8             
 [93] survival_3.5-5              deldir_1.0-9               
 [95] tidyselect_1.2.0            SingleCellExperiment_1.22.0
 [97] miniUI_0.1.1.1              pbapply_1.7-2              
 [99] knitr_1.43                  git2r_0.33.0               
[101] IRanges_2.34.1              SummarizedExperiment_1.30.2
[103] scattermore_1.2             stats4_4.3.1               
[105] xfun_0.39                   Biobase_2.60.0             
[107] matrixStats_1.0.0           pheatmap_1.0.12            
[109] stringi_1.7.12              lazyeval_0.2.2             
[111] yaml_2.3.7                  evaluate_0.21              
[113] codetools_0.2-19            tcltk_4.3.1                
[115] cli_3.6.1                   uwot_0.1.16                
[117] systemfonts_1.0.4           xtable_1.8-4               
[119] reticulate_1.36.1           munsell_0.5.0              
[121] processx_3.8.2              jquerylib_0.1.4            
[123] GenomeInfoDb_1.36.1         Rcpp_1.0.11                
[125] globals_0.16.2              spatstat.random_3.1-5      
[127] png_0.1-8                   parallel_4.3.1             
[129] ellipsis_0.3.2              bitops_1.0-7               
[131] listenv_0.9.0               viridisLite_0.4.2          
[133] scales_1.2.1                ggridges_0.5.4             
[135] crayon_1.5.2                leiden_0.4.3               
[137] rlang_1.1.1                 cowplot_1.1.1              

date()

[1] "Tue Nov  5 21:26:11 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] ggsci_3.0.0        gsubfn_0.7         proto_1.0.0        gridExtra_2.3     
 [5] dittoSeq_1.12.1    ggplot2_3.4.2      Matrix_1.6-0       SeuratObject_4.1.3
 [9] Seurat_4.3.0.1     patchwork_1.1.2    stringr_1.5.0      dplyr_1.1.2       
[13] purrr_1.0.1        here_1.0.1         magrittr_2.0.3     circlize_0.4.15   
[17] tidyr_1.3.0        tibble_3.2.1       workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3          rstudioapi_0.15.0          
  [3] jsonlite_1.8.7              shape_1.4.6                
  [5] spatstat.utils_3.1-0        farver_2.1.1               
  [7] rmarkdown_2.23              ragg_1.2.5                 
  [9] zlibbioc_1.46.0             GlobalOptions_0.1.2        
 [11] fs_1.6.3                    vctrs_0.6.3                
 [13] ROCR_1.0-11                 spatstat.explore_3.2-1     
 [15] RCurl_1.98-1.12             S4Arrays_1.2.1             
 [17] htmltools_0.5.5             SparseArray_1.2.4          
 [19] sass_0.4.7                  sctransform_0.3.5          
 [21] parallelly_1.36.0           KernSmooth_2.23-22         
 [23] bslib_0.5.0                 htmlwidgets_1.6.2          
 [25] ica_1.0-3                   plyr_1.8.8                 
 [27] plotly_4.10.2               zoo_1.8-12                 
 [29] cachem_1.0.8                whisker_0.4.1              
 [31] igraph_1.5.0.1              mime_0.12                  
 [33] lifecycle_1.0.3             pkgconfig_2.0.3            
 [35] R6_2.5.1                    fastmap_1.1.1              
 [37] GenomeInfoDbData_1.2.10     MatrixGenerics_1.12.3      
 [39] fitdistrplus_1.1-11         future_1.33.0              
 [41] shiny_1.7.4.1               digest_0.6.33              
 [43] colorspace_2.1-0            S4Vectors_0.38.1           
 [45] ps_1.7.5                    rprojroot_2.0.3            
 [47] tensor_1.5                  irlba_2.3.5.1              
 [49] textshaping_0.3.6           GenomicRanges_1.52.0       
 [51] labeling_0.4.2              progressr_0.13.0           
 [53] fansi_1.0.4                 spatstat.sparse_3.0-2      
 [55] httr_1.4.6                  polyclip_1.10-4            
 [57] abind_1.4-5                 compiler_4.3.1             
 [59] withr_2.5.0                 highr_0.10                 
 [61] MASS_7.3-60                 DelayedArray_0.28.0        
 [63] tools_4.3.1                 lmtest_0.9-40              
 [65] httpuv_1.6.11               future.apply_1.11.0        
 [67] goftest_1.2-3               glue_1.6.2                 
 [69] callr_3.7.3                 nlme_3.1-162               
 [71] promises_1.2.0.1            grid_4.3.1                 
 [73] Rtsne_0.16                  getPass_0.2-4              
 [75] cluster_2.1.4               reshape2_1.4.4             
 [77] generics_0.1.3              gtable_0.3.3               
 [79] spatstat.data_3.0-1         data.table_1.14.8          
 [81] XVector_0.40.0              sp_2.0-0                   
 [83] utf8_1.2.3                  BiocGenerics_0.46.0        
 [85] spatstat.geom_3.2-4         RcppAnnoy_0.0.21           
 [87] ggrepel_0.9.3               RANN_2.6.1                 
 [89] pillar_1.9.0                later_1.3.1                
 [91] splines_4.3.1               lattice_0.21-8             
 [93] survival_3.5-5              deldir_1.0-9               
 [95] tidyselect_1.2.0            SingleCellExperiment_1.22.0
 [97] miniUI_0.1.1.1              pbapply_1.7-2              
 [99] knitr_1.43                  git2r_0.33.0               
[101] IRanges_2.34.1              SummarizedExperiment_1.30.2
[103] scattermore_1.2             stats4_4.3.1               
[105] xfun_0.39                   Biobase_2.60.0             
[107] matrixStats_1.0.0           pheatmap_1.0.12            
[109] stringi_1.7.12              lazyeval_0.2.2             
[111] yaml_2.3.7                  evaluate_0.21              
[113] codetools_0.2-19            tcltk_4.3.1                
[115] cli_3.6.1                   uwot_0.1.16                
[117] systemfonts_1.0.4           xtable_1.8-4               
[119] reticulate_1.36.1           munsell_0.5.0              
[121] processx_3.8.2              jquerylib_0.1.4            
[123] GenomeInfoDb_1.36.1         Rcpp_1.0.11                
[125] globals_0.16.2              spatstat.random_3.1-5      
[127] png_0.1-8                   parallel_4.3.1             
[129] ellipsis_0.3.2              bitops_1.0-7               
[131] listenv_0.9.0               viridisLite_0.4.2          
[133] scales_1.2.1                ggridges_0.5.4             
[135] crayon_1.5.2                leiden_0.4.3               
[137] rlang_1.1.1                 cowplot_1.1.1