Last updated: 2024-08-21
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Knit directory: CCL19_FRCs_lung_cancer/
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Rmd | 5a7acbb | Pchryssa | 2024-08-21 | NSCLC FRC TIL merge |
suppressPackageStartupMessages({
library(here)
library(purrr)
library(stringr)
library(patchwork)
library(Seurat)
library(Matrix)
library(dittoSeq)
library(gridExtra)
library(CellChat)
library(NMF)
library(pheatmap)
})
basedir <- here()
NSCLS_TIL_data <-readRDS(paste0(basedir,"/data/Human/NSCLC_TILs.rds"))
NSCLC_CCL19_FRCs <-readRDS(paste0(basedir,"/data/Human/NSCLC_CCL19_FRCs_CAFs.rds"))
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
#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"))
#saveRDS(merged_data, paste0(basedir,"/data/Human/NSCLC_TILs_SI4.rds"))
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")
triMean is used for calculating the average gene expression per cell group.
[1] ">>> Run CellChat on sc/snRNA-seq data <<< [2024-08-21 09:31:58.649619]"
[1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2024-08-21 09:33:33.020121]"
#saveRDS(cellchat,paste0(basedir,"/data/Human/CCL19_FRC_TIL_SIF4_cellchat.rds"))
gg <- netAnalysis_signalingRole_scatter(cellchat,color.use = palet)
gg <- gg + ggtitle("Interactome analysis (Cellchat)")
gg
selectK(cellchat, pattern = "outgoing")
selectK(cellchat, pattern = "incoming")
nPatterns <- 7
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns, color.use = palet)
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns, color.use = palet)
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)
NCLS_TIL_FRC_cd8 <-readRDS(paste0(basedir,"/data/Human/NSCLC_TILs_CD8_pop.rds"))
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
#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")
#saveRDS(merged_data, paste0(basedir,"/data/Human/NSCLC_TILs_CD8_pop_merge.rds"))
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")
triMean is used for calculating the average gene expression per cell group.
[1] ">>> Run CellChat on sc/snRNA-seq data <<< [2024-08-21 09:40:12.549866]"
[1] ">>> CellChat inference is done. Parameter values are stored in `object@options$parameter` <<< [2024-08-21 09:41:38.100548]"
#saveRDS(cellchat,paste0(basedir,"/data/Human/CCL19_FRC_TIL_F3E_cellchat.rds"))
gg <- netAnalysis_signalingRole_scatter(cellchat,color.use = palet)
gg <- gg + ggtitle("Interactome analysis (Cellchat)")
gg
selectK(cellchat, pattern = "outgoing")
selectK(cellchat, pattern = "incoming")
nPatterns <- 8
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "outgoing", k = nPatterns, color.use = palet)
cellchat <- identifyCommunicationPatterns(cellchat, pattern = "incoming", k = nPatterns, color.use = palet)
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"))
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)
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)
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)
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)
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)
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)
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)
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)
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)
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] 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] "Wed Aug 21 09:44:47 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] 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