Characterization of cancer-associated fibroblasts (CAFs) and tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer
Chrysa Papadopoulou
Last updated: 2025-10-01
Checks: 7 0
Knit directory: CCL19_FRCs_lung_cancer/
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Load packages
suppressPackageStartupMessages({
library(here)
library(purrr)
library(Seurat)
library(dittoSeq)
library(CellChat)
library(harmony)
library(ggsci)
library(bigmds)
})Set directory
basedir <- here()Characterization of cancer-associated fibroblasts (CAFs) in NSCLC
Read Stroma cell data
NSCLC_data <- readRDS(paste0(basedir,"/data/Human/NSCLC_stroma_total.rds"))Subset on CAFs only
CAFs_NSCLC <- subset(NSCLC_data, cell_type %in% c("CAF2", "CAF1"))
#Do preprocessing again without re normalization
resolution <- c(0.1, 0.25, 0.4, 0.6, 0.8, 1., 1.2, 1.4, 1.6, 2., 2.2, 2.4)
CAFs_NSCLC <- FindVariableFeatures(CAFs_NSCLC, selection.method = "vst", nfeatures = 2000)
CAFs_NSCLC <- ScaleData(CAFs_NSCLC)
CAFs_NSCLC <- RunPCA(object = CAFs_NSCLC, npcs = 30, verbose = FALSE,seed.use = 8734)
CAFs_NSCLC <- RunTSNE(object = CAFs_NSCLC, reduction = "pca", dims = 1:20, seed.use = 8734)
CAFs_NSCLC <- RunUMAP(object = CAFs_NSCLC, reduction = "pca", dims = 1:20, seed.use = 8734)
CAFs_NSCLC <- FindNeighbors(object = CAFs_NSCLC, reduction = "pca", dims = 1:20, seed.use = 8734)
for(k in 1:length(resolution)){
CAFs_NSCLC <- FindClusters(object = CAFs_NSCLC, resolution = resolution[k], random.seed = 8734)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9577
Number of communities: 6
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9283
Number of communities: 11
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9120
Number of communities: 13
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8958
Number of communities: 20
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8832
Number of communities: 21
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8708
Number of communities: 24
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8601
Number of communities: 26
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8505
Number of communities: 27
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8410
Number of communities: 29
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8245
Number of communities: 36
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8183
Number of communities: 36
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1278142
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8112
Number of communities: 42
Elapsed time: 7 secondsData integration with Harmony
experiment.harmonized <- RunHarmony(CAFs_NSCLC,
group.by.vars = c("patient"),
reduction = "pca", reduction.save = "harmony", plot_convergence = TRUE)
harmony_embeddings <- Embeddings(experiment.harmonized, 'harmony')
experiment.harmonized <- RunUMAP(experiment.harmonized, dims = 1:30,seed.use = 1753,reduction = "harmony")
experiment.harmonized <- FindNeighbors(experiment.harmonized, dims = 1:30,seed.use = 1753, reduction = "harmony")
resolution <- c(0.1, 0.15, 0.125, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45 , 0.5, 0.6, 0.8, 1., 1.2, 1.4, 1.6, 1.8, 2., 2.2, 2.5)
for(k in 1:length(resolution)){
experiment.harmonized <- FindClusters(object = experiment.harmonized, resolution = resolution[k], algorithm = 1,random.seed = 1753)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9550
Number of communities: 6
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9404
Number of communities: 8
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9479
Number of communities: 7
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9285
Number of communities: 8
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9177
Number of communities: 8
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9081
Number of communities: 10
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9001
Number of communities: 11
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8912
Number of communities: 11
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8844
Number of communities: 12
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8774
Number of communities: 12
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8661
Number of communities: 13
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8475
Number of communities: 16
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8340
Number of communities: 21
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8223
Number of communities: 24
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8127
Number of communities: 27
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8033
Number of communities: 26
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7946
Number of communities: 31
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7866
Number of communities: 33
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7782
Number of communities: 34
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 40477
Number of edges: 1311218
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.7667
Number of communities: 36
Elapsed time: 7 secondsCancer associated fibroblasts - CAFs subsets (Figure 3A)
cols<- pal_igv()(51)
names(cols) <- c(0:50)
palet <- cols[4:6]
names(palet) <- c("CAF2","CAF1")
DimPlot(experiment.harmonized, 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("Cancer associated fibroblasts")
CCL19 expression in CAFs of NSCLC (Figure 4A)
FeaturePlot(experiment.harmonized, reduction = "umap",
features = get_full_gene_name('CCL19',experiment.harmonized),raster=FALSE,
cols=c("lightgrey", "red"))
CAF gene signatures (Figure 3B)
data_conv <- experiment.harmonized
data_conv <-Remove_ensebl_id(data_conv)
gene_list <- c("CCL19","CCL21","PDPN","FAP","POSTN","CLU","LEPR","CD34","SULF1","DPT","ICAM1",
"VCAM1","CXCL12","ALDH1A1","MMP2","ACTA2","MYH11","MCAM","NOTCH3","RGS5",
"DES","AIFM2","CSPG4", "KCNJ8", "ITGA7")
dittoDotPlot(data_conv, vars = gene_list, group.by = "cell_type", size = 8,legend.size.title = "Expression (%)",scale = FALSE) + ylab(" ") 
Characterization of tumor-infiltrating lymphocytes (TILs) in NSCLC
Read NSCLC TIL data
NSCLS_TIL_data <- readRDS(paste0(basedir,"/data/Human/NSCLC_TILs.rds"))Tumor-infiltrating lymphocytes (TILS) subsets (Figure 3C)
#Define color palet
palet <- c("#5050FFFF", "#CE3D32FF", "#4DAF4A","#FB9A99","#377EB8","#A65628","#222F75")
names(palet) <- c( "CAF2/TRC","CAF1/PRC", paste0("CD4", "\u207A ", "T cells"), paste0("CD8", "\u207A ", "T cells"), "B cells", "Regulatory T cells",paste0("Cycling CD8", "\u207A ", "T cells"))
DimPlot(NSCLS_TIL_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("Tumor infiltrating lymphocytes"))
TIL gene signatures (Figure 3D)
data_conv <-Remove_ensebl_id(NSCLS_TIL_data)
genes <- c("CD79A","IGHM","IGHD","JCHAIN","CD4","CCR7","SELL","FOXP3","IKZF4","IL2RA","CTLA4","CD8A","CX3CR1","FCGR3A","KIR3DL2","KLRF1", "NKG7","GZMB","GNLY","TIGIT","TOP2A","MKI67","PCLAF")
data_conv$cell_type <- factor(data_conv$cell_type, levels = c("B cells",paste0("CD4", "\u207A ", "T cells"),"Regulatory T cells",paste0("CD8", "\u207A ", "T cells"), paste0("Cycling CD8", "\u207A ", "T cells")))
gg <-dittoDotPlot(data_conv, vars = genes, group.by = "cell_type", size = 4)
gg + coord_fixed(ratio=0.8) + ylab("")
Session info
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6.1
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] bigmds_3.0.0 ggsci_3.0.0 harmony_1.2.0
[4] Rcpp_1.0.11 CellChat_1.6.1 Biobase_2.60.0
[7] BiocGenerics_0.46.0 igraph_1.5.0.1 dplyr_1.1.2
[10] dittoSeq_1.12.1 ggplot2_3.4.2 SeuratObject_5.1.0
[13] Seurat_4.3.0.1 purrr_1.0.1 here_1.0.1
[16] magrittr_2.0.3 circlize_0.4.15 tidyr_1.3.0
[19] tibble_3.2.1 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] doParallel_1.0.17 rprojroot_2.0.3
[11] globals_0.16.2 processx_3.8.2
[13] lattice_0.21-8 MASS_7.3-60
[15] backports_1.4.1 plotly_4.10.2
[17] sass_0.4.7 rmarkdown_2.23
[19] jquerylib_0.1.4 yaml_2.3.7
[21] httpuv_1.6.11 NMF_0.26
[23] sctransform_0.4.1 spam_2.10-0
[25] sp_2.0-0 spatstat.sparse_3.0-2
[27] reticulate_1.36.1 cowplot_1.1.1
[29] pbapply_1.7-2 RColorBrewer_1.1-3
[31] abind_1.4-5 zlibbioc_1.46.0
[33] Rtsne_0.16 GenomicRanges_1.52.0
[35] RCurl_1.98-1.12 pracma_2.4.4
[37] git2r_0.33.0 GenomeInfoDbData_1.2.10
[39] IRanges_2.34.1 S4Vectors_0.38.1
[41] svd_0.5.5 ggrepel_0.9.3
[43] irlba_2.3.5.1 listenv_0.9.0
[45] spatstat.utils_3.1-0 pheatmap_1.0.12
[47] RSpectra_0.16-1 goftest_1.2-3
[49] spatstat.random_3.1-5 fitdistrplus_1.1-11
[51] parallelly_1.36.0 svglite_2.1.1
[53] leiden_0.4.3 codetools_0.2-19
[55] DelayedArray_0.28.0 tidyselect_1.2.0
[57] shape_1.4.6 farver_2.1.1
[59] matrixStats_1.0.0 stats4_4.3.1
[61] spatstat.explore_3.2-1 jsonlite_1.8.7
[63] GetoptLong_1.0.5 BiocNeighbors_1.18.0
[65] ellipsis_0.3.2 progressr_0.13.0
[67] ggalluvial_0.12.5 ggridges_0.5.4
[69] survival_3.5-5 iterators_1.0.14
[71] systemfonts_1.0.4 foreach_1.5.2
[73] tools_4.3.1 ragg_1.2.5
[75] sna_2.7-1 ica_1.0-3
[77] glue_1.6.2 gridExtra_2.3
[79] SparseArray_1.2.4 xfun_0.39
[81] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.1
[83] withr_2.5.0 BiocManager_1.30.21.1
[85] fastmap_1.1.1 fansi_1.0.4
[87] callr_3.7.3 digest_0.6.33
[89] R6_2.5.1 mime_0.12
[91] textshaping_0.3.6 colorspace_2.1-0
[93] scattermore_1.2 tensor_1.5
[95] spatstat.data_3.0-1 RhpcBLASctl_0.23-42
[97] utf8_1.2.3 generics_0.1.3
[99] data.table_1.14.8 FNN_1.1.3.2
[101] httr_1.4.6 htmlwidgets_1.6.2
[103] S4Arrays_1.2.1 whisker_0.4.1
[105] uwot_0.1.16 pkgconfig_2.0.3
[107] gtable_0.3.3 registry_0.5-1
[109] ComplexHeatmap_2.16.0 lmtest_0.9-40
[111] SingleCellExperiment_1.22.0 XVector_0.40.0
[113] htmltools_0.5.5 carData_3.0-5
[115] dotCall64_1.1-1 clue_0.3-64
[117] scales_1.2.1 png_0.1-8
[119] knitr_1.43 rstudioapi_0.15.0
[121] rjson_0.2.21 reshape2_1.4.4
[123] coda_0.19-4 statnet.common_4.9.0
[125] nlme_3.1-162 cachem_1.0.8
[127] zoo_1.8-12 GlobalOptions_0.1.2
[129] stringr_1.5.0 KernSmooth_2.23-22
[131] parallel_4.3.1 miniUI_0.1.1.1
[133] pillar_1.9.0 grid_4.3.1
[135] vctrs_0.6.3 RANN_2.6.1
[137] ggpubr_0.6.0 promises_1.2.0.1
[139] car_3.1-2 xtable_1.8-4
[141] cluster_2.1.4 evaluate_0.21
[143] cli_3.6.1 compiler_4.3.1
[145] rlang_1.1.1 crayon_1.5.2
[147] rngtools_1.5.2 ggsignif_0.6.4
[149] future.apply_1.11.0 labeling_0.4.2
[151] ps_1.7.5 getPass_0.2-4
[153] plyr_1.8.8 fs_1.6.3
[155] stringi_1.7.12 network_1.18.1
[157] BiocParallel_1.34.2 viridisLite_0.4.2
[159] deldir_1.0-9 gridBase_0.4-7
[161] munsell_0.5.0 lazyeval_0.2.2
[163] spatstat.geom_3.2-4 Matrix_1.6-4
[165] patchwork_1.1.2 future_1.33.0
[167] shiny_1.7.4.1 highr_0.10
[169] SummarizedExperiment_1.30.2 ROCR_1.0-11
[171] broom_1.0.5 bslib_0.5.0 date()[1] "Wed Oct 1 17:19:19 2025"
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6.1
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] bigmds_3.0.0 ggsci_3.0.0 harmony_1.2.0
[4] Rcpp_1.0.11 CellChat_1.6.1 Biobase_2.60.0
[7] BiocGenerics_0.46.0 igraph_1.5.0.1 dplyr_1.1.2
[10] dittoSeq_1.12.1 ggplot2_3.4.2 SeuratObject_5.1.0
[13] Seurat_4.3.0.1 purrr_1.0.1 here_1.0.1
[16] magrittr_2.0.3 circlize_0.4.15 tidyr_1.3.0
[19] tibble_3.2.1 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] doParallel_1.0.17 rprojroot_2.0.3
[11] globals_0.16.2 processx_3.8.2
[13] lattice_0.21-8 MASS_7.3-60
[15] backports_1.4.1 plotly_4.10.2
[17] sass_0.4.7 rmarkdown_2.23
[19] jquerylib_0.1.4 yaml_2.3.7
[21] httpuv_1.6.11 NMF_0.26
[23] sctransform_0.4.1 spam_2.10-0
[25] sp_2.0-0 spatstat.sparse_3.0-2
[27] reticulate_1.36.1 cowplot_1.1.1
[29] pbapply_1.7-2 RColorBrewer_1.1-3
[31] abind_1.4-5 zlibbioc_1.46.0
[33] Rtsne_0.16 GenomicRanges_1.52.0
[35] RCurl_1.98-1.12 pracma_2.4.4
[37] git2r_0.33.0 GenomeInfoDbData_1.2.10
[39] IRanges_2.34.1 S4Vectors_0.38.1
[41] svd_0.5.5 ggrepel_0.9.3
[43] irlba_2.3.5.1 listenv_0.9.0
[45] spatstat.utils_3.1-0 pheatmap_1.0.12
[47] RSpectra_0.16-1 goftest_1.2-3
[49] spatstat.random_3.1-5 fitdistrplus_1.1-11
[51] parallelly_1.36.0 svglite_2.1.1
[53] leiden_0.4.3 codetools_0.2-19
[55] DelayedArray_0.28.0 tidyselect_1.2.0
[57] shape_1.4.6 farver_2.1.1
[59] matrixStats_1.0.0 stats4_4.3.1
[61] spatstat.explore_3.2-1 jsonlite_1.8.7
[63] GetoptLong_1.0.5 BiocNeighbors_1.18.0
[65] ellipsis_0.3.2 progressr_0.13.0
[67] ggalluvial_0.12.5 ggridges_0.5.4
[69] survival_3.5-5 iterators_1.0.14
[71] systemfonts_1.0.4 foreach_1.5.2
[73] tools_4.3.1 ragg_1.2.5
[75] sna_2.7-1 ica_1.0-3
[77] glue_1.6.2 gridExtra_2.3
[79] SparseArray_1.2.4 xfun_0.39
[81] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.1
[83] withr_2.5.0 BiocManager_1.30.21.1
[85] fastmap_1.1.1 fansi_1.0.4
[87] callr_3.7.3 digest_0.6.33
[89] R6_2.5.1 mime_0.12
[91] textshaping_0.3.6 colorspace_2.1-0
[93] scattermore_1.2 tensor_1.5
[95] spatstat.data_3.0-1 RhpcBLASctl_0.23-42
[97] utf8_1.2.3 generics_0.1.3
[99] data.table_1.14.8 FNN_1.1.3.2
[101] httr_1.4.6 htmlwidgets_1.6.2
[103] S4Arrays_1.2.1 whisker_0.4.1
[105] uwot_0.1.16 pkgconfig_2.0.3
[107] gtable_0.3.3 registry_0.5-1
[109] ComplexHeatmap_2.16.0 lmtest_0.9-40
[111] SingleCellExperiment_1.22.0 XVector_0.40.0
[113] htmltools_0.5.5 carData_3.0-5
[115] dotCall64_1.1-1 clue_0.3-64
[117] scales_1.2.1 png_0.1-8
[119] knitr_1.43 rstudioapi_0.15.0
[121] rjson_0.2.21 reshape2_1.4.4
[123] coda_0.19-4 statnet.common_4.9.0
[125] nlme_3.1-162 cachem_1.0.8
[127] zoo_1.8-12 GlobalOptions_0.1.2
[129] stringr_1.5.0 KernSmooth_2.23-22
[131] parallel_4.3.1 miniUI_0.1.1.1
[133] pillar_1.9.0 grid_4.3.1
[135] vctrs_0.6.3 RANN_2.6.1
[137] ggpubr_0.6.0 promises_1.2.0.1
[139] car_3.1-2 xtable_1.8-4
[141] cluster_2.1.4 evaluate_0.21
[143] cli_3.6.1 compiler_4.3.1
[145] rlang_1.1.1 crayon_1.5.2
[147] rngtools_1.5.2 ggsignif_0.6.4
[149] future.apply_1.11.0 labeling_0.4.2
[151] ps_1.7.5 getPass_0.2-4
[153] plyr_1.8.8 fs_1.6.3
[155] stringi_1.7.12 network_1.18.1
[157] BiocParallel_1.34.2 viridisLite_0.4.2
[159] deldir_1.0-9 gridBase_0.4-7
[161] munsell_0.5.0 lazyeval_0.2.2
[163] spatstat.geom_3.2-4 Matrix_1.6-4
[165] patchwork_1.1.2 future_1.33.0
[167] shiny_1.7.4.1 highr_0.10
[169] SummarizedExperiment_1.30.2 ROCR_1.0-11
[171] broom_1.0.5 bslib_0.5.0