NSCLC stroma and CCL19 cells
Chrysa Papadopoulou
Last updated: 2024-08-21
Checks: 7 0
Knit directory: CCL19_FRCs_lung_cancer/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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 2A-2B)
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")
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")
COL1A2
FeaturePlot(data, reduction = "tsne",
features = get_full_gene_name('COL1A2',data),raster=FALSE,
cols=c("lightgrey", "darkred")) + ggtitle("CAF/FB(COL1A2)")
PECAM1
FeaturePlot(data, reduction = "tsne",
features = get_full_gene_name('PECAM1',data),raster=FALSE,
cols=c("lightgrey", "darkred")) + ggtitle("EC(PECAM1)")
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") 
Dotplots (Supplementary Figure 2C and 2F)
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")
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")
Stacked Barplots (Supplementary Figure 2D and 2E)
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)
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)
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$cell_type)]
DimPlot(NSCLC_CCL19_data, reduction = "umap", group.by = "cell_type",cols = palet_CCL19_FRC)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2") + ggtitle(paste0("CCL19", "\U207A ", "fibroblasts"))-1.png)
Dotplot (Figure 1L)
data_conv <-NSCLC_CCL19_data
data_conv <-Remove_ensebl_id(data_conv)
Idents(data_conv) <- data_conv$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"))
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")
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")
Signatures (Supplementary Figure 2G)
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")
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")
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")
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")
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")
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")
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")
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")
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")
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")
Unaffected Lung vs Tumor (Supplementary Figure 5A)
Umap
DimPlot(NSCLC_CCL19_data, reduction = "umap", group.by = "type", split.by = "type")+
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")
DES
FeaturePlot(NSCLC_CCL19_data, reduction = "umap", split.by = "type",
features = get_full_gene_name('DES',NSCLC_CCL19_data),raster=FALSE,
cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5)
Signatures (Supplementary Figure 5B)
CD34
FeaturePlot(NSCLC_CCL19_data, reduction = "umap",
features = get_full_gene_name('CD34',NSCLC_CCL19_data),raster=FALSE,
cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("CD34")
LEPR
FeaturePlot(NSCLC_CCL19_data, reduction = "umap",
features = get_full_gene_name('LEPR',NSCLC_CCL19_data),raster=FALSE,
cols=c("lightgrey", "darkred"), min.cutoff = 0, max.cutoff = 4.5) + ggtitle("LEPR")
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 secondsCCL19⁺ 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 13.6.7
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] "Wed Aug 21 08:58:20 2024"
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.7
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