Ccl19-expressing FRCs control anti-tumor T cell responses
Chrysa Papadopoulou
Last updated: 2024-09-23
Checks: 7 0
Knit directory: CCL19_FRCs_lung_cancer/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 73add44 | Pchryssa | 2024-09-23 | Modify figure order |
| html | 670ca3f | Pchryssa | 2024-08-21 | Build site. |
| Rmd | 6a1b87c | Pchryssa | 2024-08-21 | Tetra CD8 T cells |
Load packages
suppressPackageStartupMessages({
library(here)
library(purrr)
library(dplyr)
library(stringr)
library(patchwork)
library(Seurat)
library(Matrix)
library(gridExtra)
library(gsubfn)
library(ggsci)
library(biomaRt)
library(tidyverse)
library(msigdbr)
library(stats)
library(clusterProfiler)
library(dict)
library(openxlsx)
library(DOSE)
library(enrichplot)
library(dittoSeq)
})Set directory
basedir <- here()Read GP33/34⁺ CD8⁺ T cells sorted from LLC-gp33 bearing DTR⁺ and DTR\(^−\) lungs on day 23, after mCOV-Flt3l-gp33 immunization
Tetra_CD8 <- readRDS(paste0(basedir,"/data/Mouse/Tetra_CD8_EXH.rds"))Set color palette
cols <- c("#F8766D","#B79F00","#00C08B","#F564E3","#00B4F0")
names(cols) <-c("Cycling T cells","Eff. Mem. T cells","Effector T cells",paste0("Ccr7", "\U207A ","T cells"),"Exhausted T cells")CD8⁺GP33/34⁺ T cells (mCOV-FIt31-gp33) (Figure 7F)
Umap
DimPlot(Tetra_CD8, reduction = "umap", group.by = "annot", cols = cols)+
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("CD8", "\U207A ", "GP33/34" , "\U207A ", "T cells (mCOV-FIt31-g33)"))
| Version | Author | Date |
|---|---|---|
| 80d46cf | Pchryssa | 2024-08-26 |
DTR−
DTRneg <-subset(Tetra_CD8, depleted==paste0("DTR", expression("\u2212")))
DimPlot(DTRneg, reduction = "umap", group.by = "depleted",split.by = "depleted")+
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")
| Version | Author | Date |
|---|---|---|
| 80d46cf | Pchryssa | 2024-08-26 |
DTR\(^⁺\)
DTRpos <-subset(Tetra_CD8, depleted==paste0("DTR", expression("\u207A")))
DimPlot(DTRpos, reduction = "umap", group.by = "depleted",split.by = "depleted")+
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")
| Version | Author | Date |
|---|---|---|
| 80d46cf | Pchryssa | 2024-08-26 |
Dotplot CD8⁺ T cell subsets (Supplementary Figure 11E)
data_conv <-Tetra_CD8
data_conv <-Remove_ensebl_id(data_conv)
Idents(data_conv) <- data_conv$annot
levels(data_conv)<-levels(data_conv)[order(match(levels(data_conv),c(paste0("Ccr7", "\U207A ","T cells"),"Exhausted T cells","Effector T cells","Eff. Mem. T cells","Cycling T cells")))]
data_conv$cell_type <- factor(as.character(data_conv@active.ident), levels = rev(c(paste0("Ccr7", "\U207A ","T cells"),"Exhausted T cells","Effector T cells","Eff. Mem. T cells","Cycling T cells")))
gene_list <-c("Pclaf","Top2a","Mki67","Mcm7","Klrg1","Gzma","Lef1","Klf2","Ifng","Ccl4","Xcl1","Il2","Gzmb","Havcr2","Ctla4","Prf1",
"Lag3","Pdcd1","Tigit", "Tox","Ccr7","Tcf7","Tnfsf4","Cd83")
dittoDotPlot(data_conv, vars = gene_list, group.by = "cell_type", size = 5,legend.size.title = "Expression (%)",scale = TRUE,summary.fxn.color = mean) + ylab( " ")
| Version | Author | Date |
|---|---|---|
| 80d46cf | Pchryssa | 2024-08-26 |
Pathway analysis
Enriched in DTR\(^−\) exhausted T cells (Figure 7G)
# Step 1 : Set output directory
subDir <- "GSEA_EXH_T/"
saving_path <- paste0(basedir,"/output/")
final_dir <- file.path(saving_path, subDir)
dir.create(final_dir, showWarnings = FALSE,recursive = TRUE)
map_df <- ExtractMouseGeneSets(final_dir)
# Step 2: Customize parameters
httr::set_config(httr::config(ssl_verifypeer = FALSE))
organism <- "org.Mm.eg.db"
disease_phase <- "Depl_vs_NDepl"
datatype <- "SYMBOL"
Exh_T <-subset(Tetra_CD8, annot %in% "Exhausted T cells")
Idents(Exh_T) <- Exh_T$depleted
DEmarkers <-FindAllMarkers(Exh_T, only.pos=T, logfc.threshold = 0.1,
min.pct = 0.1)
Vec <-unique(Exh_T$depleted)
EnrichParameters_EXH_T <-customize_parameters(Vec,DEmarkers,organism,datatype,disease_phase,saving_path) [1] "Finish Enrichment_Analysis for GO DTR⁺"
[1] "Finish Enrichment_Analysis for GO DTR−"#Step 3: Plot gene-concept network
pathways <-c("lymphocyte mediated immunity","leukocyte cell-cell adhesion","leukocyte mediated cytotoxicity")
cnetplot(EnrichParameters_EXH_T$enrichcl_list[[2]], node_label="gene", layout = "kk", showCategory = pathways,
max.overlaps=Inf,color.params = list(gene ="black",
category = "red",
edge = TRUE),cex.params = list(category_label = 0.0000001,
label_gene = 0.000001, gene_label= 0.6)) +
theme(legend.text = element_text(size = 8))
| Version | Author | Date |
|---|---|---|
| 80d46cf | Pchryssa | 2024-08-26 |
Enriched in DTR\(^−\) cycling T cells (Figure 7H)
# Step 1 : Set output directory
subDir <- "GSEA_CYCL_T/"
saving_path <- paste0(basedir,"/output/")
final_dir <- file.path(saving_path, subDir)
dir.create(final_dir, showWarnings = FALSE,recursive = TRUE)
map_df <- ExtractMouseGeneSets(final_dir)
# Step 2: Customize parameters
httr::set_config(httr::config(ssl_verifypeer = FALSE))
organism <- "org.Mm.eg.db"
disease_phase <- "Depl_vs_NDepl"
datatype <- "SYMBOL"
Cycling_T <-subset(Tetra_CD8, annot %in% "Cycling T cells")
Idents(Cycling_T) <- Cycling_T$depleted
DEmarkers <-FindAllMarkers(Cycling_T, only.pos=T, logfc.threshold = 0.1,
min.pct = 0.1)
Vec <-unique(Cycling_T$depleted)
EnrichParameters_CYCL_T <-customize_parameters(Vec,DEmarkers,organism,datatype,disease_phase,saving_path) [1] "Finish Enrichment_Analysis for GO DTR⁺"
[1] "Finish Enrichment_Analysis for GO DTR−"#Step 3: Plot gene-concept network
pathways <-c("mitotic cell cycle phase transition","regulation of apoptotic signaling pathway","regulation of leukocyte differentiation")
cnetplot(EnrichParameters_CYCL_T$enrichcl_list[[2]], node_label="gene", layout = "kk", showCategory = pathways,
max.overlaps=Inf,color.params = list(gene ="black",
category = "red",
edge = TRUE),cex.params = list(category_label = 0.0000001,
label_gene = 0.000001, gene_label= 0.6)) +
theme(legend.text = element_text(size = 8))
| Version | Author | Date |
|---|---|---|
| 80d46cf | Pchryssa | 2024-08-26 |
Enriched in DTR\(^−\) Ccr7⁺ T cells (Supplementary Figure 11F)
# Step 1 : Set output directory
subDir <- "GSEA_CCR7_T/"
saving_path <- paste0(basedir,"/output/")
final_dir <- file.path(saving_path, subDir)
dir.create(final_dir, showWarnings = FALSE,recursive = TRUE)
map_df <- ExtractMouseGeneSets(final_dir)
# Step 2: Customize parameters
httr::set_config(httr::config(ssl_verifypeer = FALSE))
organism <- "org.Mm.eg.db"
disease_phase <- "Depl_vs_NDepl"
datatype <- "SYMBOL"
Ccr7_T <-subset(Tetra_CD8, annot %in% paste0("Ccr7", "\U207A ","T cells"))
Idents(Ccr7_T) <- Ccr7_T$depleted
DEmarkers <-FindAllMarkers(Ccr7_T, only.pos=T, logfc.threshold = 0.1,
min.pct = 0.1)
Vec <-unique(Ccr7_T$depleted)
EnrichParameters_CCR7_T <-customize_parameters(Vec,DEmarkers,organism,datatype,disease_phase,saving_path) [1] "Finish Enrichment_Analysis for GO DTR⁺"
[1] "Finish Enrichment_Analysis for GO DTR−"Enriched in Ccl19-EYFP (DTR\(^−\))
ccr7_data <-EnrichParameters_CCR7_T$enrichcl_list[[2]]@result
ccr7_data$Color <-rep("#F564E3",nrow(ccr7_data))
ccr7_data$CellType <-rep(paste0("Ccr7", "\U207A ","T cells"),nrow(ccr7_data))
exh_data <-EnrichParameters_EXH_T$enrichcl_list[[2]]@result
exh_data$Color <-rep("#00B4F0",nrow(exh_data))
exh_data$CellType <-rep("Exhausted T cells",nrow(exh_data))
cycl_data <-EnrichParameters_CYCL_T$enrichcl_list[[2]]@result
cycl_data$Color <-rep("#F8766D",nrow(cycl_data))
cycl_data$CellType <-rep("Cycling T cells",nrow(cycl_data))
df_list <-list(ccr7_data,exh_data,cycl_data)
new_df<-do.call("rbind", df_list)
# Filter on the most significant pathways (keep rows where p.adjust<= 0.05)
test <-new_df[new_df$p.adjust <= 0.05,]
pathways <- c("granzyme-mediated programmed cell death signaling pathway","T cell differentiation",
"cellular response to type II interferon","lymphocyte migration",
"chromatin remodeling" ,"alpha-beta T cell activation")
BarPlot(test,pathways,cols)
| Version | Author | Date |
|---|---|---|
| 80d46cf | Pchryssa | 2024-08-26 |
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] dittoSeq_1.12.1 enrichplot_1.20.0 DOSE_3.26.1
[4] openxlsx_4.2.5.2 dict_0.10.0 clusterProfiler_4.8.2
[7] msigdbr_7.5.1 lubridate_1.9.2 forcats_1.0.0
[10] readr_2.1.4 ggplot2_3.4.2 tidyverse_2.0.0
[13] biomaRt_2.56.1 ggsci_3.0.0 gsubfn_0.7
[16] proto_1.0.0 gridExtra_2.3 Matrix_1.6-0
[19] SeuratObject_4.1.3 Seurat_4.3.0.1 patchwork_1.1.2
[22] stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1
[25] here_1.0.1 magrittr_2.0.3 circlize_0.4.15
[28] tidyr_1.3.0 tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.3 matrixStats_1.0.0
[3] spatstat.sparse_3.0-2 bitops_1.0-7
[5] HDO.db_0.99.1 httr_1.4.6
[7] RColorBrewer_1.1-3 tools_4.3.1
[9] sctransform_0.3.5 utf8_1.2.3
[11] R6_2.5.1 lazyeval_0.2.2
[13] uwot_0.1.16 withr_2.5.0
[15] sp_2.0-0 prettyunits_1.1.1
[17] progressr_0.13.0 textshaping_0.3.6
[19] cli_3.6.1 Biobase_2.60.0
[21] spatstat.explore_3.2-1 scatterpie_0.2.1
[23] labeling_0.4.2 sass_0.4.7
[25] spatstat.data_3.0-1 ggridges_0.5.4
[27] pbapply_1.7-2 systemfonts_1.0.4
[29] yulab.utils_0.0.6 gson_0.1.0
[31] parallelly_1.36.0 limma_3.56.2
[33] rstudioapi_0.15.0 RSQLite_2.3.1
[35] generics_0.1.3 gridGraphics_0.5-1
[37] shape_1.4.6 ica_1.0-3
[39] spatstat.random_3.1-5 zip_2.3.0
[41] GO.db_3.17.0 fansi_1.0.4
[43] S4Vectors_0.38.1 abind_1.4-5
[45] lifecycle_1.0.3 whisker_0.4.1
[47] yaml_2.3.7 SummarizedExperiment_1.30.2
[49] SparseArray_1.2.4 qvalue_2.32.0
[51] BiocFileCache_2.8.0 Rtsne_0.16
[53] grid_4.3.1 blob_1.2.4
[55] promises_1.2.0.1 crayon_1.5.2
[57] miniUI_0.1.1.1 lattice_0.21-8
[59] cowplot_1.1.1 KEGGREST_1.40.0
[61] pillar_1.9.0 knitr_1.43
[63] GenomicRanges_1.52.0 fgsea_1.26.0
[65] tcltk_4.3.1 future.apply_1.11.0
[67] codetools_0.2-19 fastmatch_1.1-4
[69] leiden_0.4.3 glue_1.6.2
[71] getPass_0.2-4 downloader_0.4
[73] ggfun_0.1.1 data.table_1.14.8
[75] vctrs_0.6.3 png_0.1-8
[77] treeio_1.24.3 org.Mm.eg.db_3.17.0
[79] gtable_0.3.3 cachem_1.0.8
[81] xfun_0.39 S4Arrays_1.2.1
[83] mime_0.12 tidygraph_1.2.3
[85] survival_3.5-5 pheatmap_1.0.12
[87] SingleCellExperiment_1.22.0 ellipsis_0.3.2
[89] fitdistrplus_1.1-11 ROCR_1.0-11
[91] nlme_3.1-162 ggtree_3.8.2
[93] bit64_4.0.5 progress_1.2.2
[95] filelock_1.0.2 RcppAnnoy_0.0.21
[97] GenomeInfoDb_1.36.1 rprojroot_2.0.3
[99] bslib_0.5.0 irlba_2.3.5.1
[101] KernSmooth_2.23-22 colorspace_2.1-0
[103] BiocGenerics_0.46.0 DBI_1.1.3
[105] tidyselect_1.2.0 processx_3.8.2
[107] bit_4.0.5 compiler_4.3.1
[109] curl_5.0.1 git2r_0.33.0
[111] xml2_1.3.5 DelayedArray_0.28.0
[113] plotly_4.10.2 shadowtext_0.1.2
[115] scales_1.2.1 lmtest_0.9-40
[117] callr_3.7.3 rappdirs_0.3.3
[119] digest_0.6.33 goftest_1.2-3
[121] spatstat.utils_3.1-0 rmarkdown_2.23
[123] XVector_0.40.0 htmltools_0.5.5
[125] pkgconfig_2.0.3 MatrixGenerics_1.12.3
[127] highr_0.10 dbplyr_2.3.3
[129] fastmap_1.1.1 rlang_1.1.1
[131] GlobalOptions_0.1.2 htmlwidgets_1.6.2
[133] shiny_1.7.4.1 farver_2.1.1
[135] jquerylib_0.1.4 zoo_1.8-12
[137] jsonlite_1.8.7 BiocParallel_1.34.2
[139] GOSemSim_2.26.1 RCurl_1.98-1.12
[141] GenomeInfoDbData_1.2.10 ggplotify_0.1.1
[143] munsell_0.5.0 Rcpp_1.0.11
[145] ape_5.7-1 babelgene_22.9
[147] viridis_0.6.4 reticulate_1.36.1
[149] stringi_1.7.12 ggraph_2.1.0
[151] zlibbioc_1.46.0 MASS_7.3-60
[153] plyr_1.8.8 parallel_4.3.1
[155] listenv_0.9.0 ggrepel_0.9.3
[157] deldir_1.0-9 Biostrings_2.68.1
[159] graphlayouts_1.0.0 splines_4.3.1
[161] tensor_1.5 hms_1.1.3
[163] ps_1.7.5 igraph_1.5.0.1
[165] spatstat.geom_3.2-4 reshape2_1.4.4
[167] stats4_4.3.1 XML_3.99-0.14
[169] evaluate_0.21 tzdb_0.4.0
[171] tweenr_2.0.2 httpuv_1.6.11
[173] RANN_2.6.1 polyclip_1.10-4
[175] future_1.33.0 scattermore_1.2
[177] ggforce_0.4.1 xtable_1.8-4
[179] tidytree_0.4.4 later_1.3.1
[181] ragg_1.2.5 viridisLite_0.4.2
[183] aplot_0.1.10 memoise_2.0.1
[185] AnnotationDbi_1.62.2 IRanges_2.34.1
[187] cluster_2.1.4 timechange_0.2.0
[189] globals_0.16.2 date()[1] "Mon Sep 23 13:39:02 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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dittoSeq_1.12.1 enrichplot_1.20.0 DOSE_3.26.1
[4] openxlsx_4.2.5.2 dict_0.10.0 clusterProfiler_4.8.2
[7] msigdbr_7.5.1 lubridate_1.9.2 forcats_1.0.0
[10] readr_2.1.4 ggplot2_3.4.2 tidyverse_2.0.0
[13] biomaRt_2.56.1 ggsci_3.0.0 gsubfn_0.7
[16] proto_1.0.0 gridExtra_2.3 Matrix_1.6-0
[19] SeuratObject_4.1.3 Seurat_4.3.0.1 patchwork_1.1.2
[22] stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1
[25] here_1.0.1 magrittr_2.0.3 circlize_0.4.15
[28] tidyr_1.3.0 tibble_3.2.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.3 matrixStats_1.0.0
[3] spatstat.sparse_3.0-2 bitops_1.0-7
[5] HDO.db_0.99.1 httr_1.4.6
[7] RColorBrewer_1.1-3 tools_4.3.1
[9] sctransform_0.3.5 utf8_1.2.3
[11] R6_2.5.1 lazyeval_0.2.2
[13] uwot_0.1.16 withr_2.5.0
[15] sp_2.0-0 prettyunits_1.1.1
[17] progressr_0.13.0 textshaping_0.3.6
[19] cli_3.6.1 Biobase_2.60.0
[21] spatstat.explore_3.2-1 scatterpie_0.2.1
[23] labeling_0.4.2 sass_0.4.7
[25] spatstat.data_3.0-1 ggridges_0.5.4
[27] pbapply_1.7-2 systemfonts_1.0.4
[29] yulab.utils_0.0.6 gson_0.1.0
[31] parallelly_1.36.0 limma_3.56.2
[33] rstudioapi_0.15.0 RSQLite_2.3.1
[35] generics_0.1.3 gridGraphics_0.5-1
[37] shape_1.4.6 ica_1.0-3
[39] spatstat.random_3.1-5 zip_2.3.0
[41] GO.db_3.17.0 fansi_1.0.4
[43] S4Vectors_0.38.1 abind_1.4-5
[45] lifecycle_1.0.3 whisker_0.4.1
[47] yaml_2.3.7 SummarizedExperiment_1.30.2
[49] SparseArray_1.2.4 qvalue_2.32.0
[51] BiocFileCache_2.8.0 Rtsne_0.16
[53] grid_4.3.1 blob_1.2.4
[55] promises_1.2.0.1 crayon_1.5.2
[57] miniUI_0.1.1.1 lattice_0.21-8
[59] cowplot_1.1.1 KEGGREST_1.40.0
[61] pillar_1.9.0 knitr_1.43
[63] GenomicRanges_1.52.0 fgsea_1.26.0
[65] tcltk_4.3.1 future.apply_1.11.0
[67] codetools_0.2-19 fastmatch_1.1-4
[69] leiden_0.4.3 glue_1.6.2
[71] getPass_0.2-4 downloader_0.4
[73] ggfun_0.1.1 data.table_1.14.8
[75] vctrs_0.6.3 png_0.1-8
[77] treeio_1.24.3 org.Mm.eg.db_3.17.0
[79] gtable_0.3.3 cachem_1.0.8
[81] xfun_0.39 S4Arrays_1.2.1
[83] mime_0.12 tidygraph_1.2.3
[85] survival_3.5-5 pheatmap_1.0.12
[87] SingleCellExperiment_1.22.0 ellipsis_0.3.2
[89] fitdistrplus_1.1-11 ROCR_1.0-11
[91] nlme_3.1-162 ggtree_3.8.2
[93] bit64_4.0.5 progress_1.2.2
[95] filelock_1.0.2 RcppAnnoy_0.0.21
[97] GenomeInfoDb_1.36.1 rprojroot_2.0.3
[99] bslib_0.5.0 irlba_2.3.5.1
[101] KernSmooth_2.23-22 colorspace_2.1-0
[103] BiocGenerics_0.46.0 DBI_1.1.3
[105] tidyselect_1.2.0 processx_3.8.2
[107] bit_4.0.5 compiler_4.3.1
[109] curl_5.0.1 git2r_0.33.0
[111] xml2_1.3.5 DelayedArray_0.28.0
[113] plotly_4.10.2 shadowtext_0.1.2
[115] scales_1.2.1 lmtest_0.9-40
[117] callr_3.7.3 rappdirs_0.3.3
[119] digest_0.6.33 goftest_1.2-3
[121] spatstat.utils_3.1-0 rmarkdown_2.23
[123] XVector_0.40.0 htmltools_0.5.5
[125] pkgconfig_2.0.3 MatrixGenerics_1.12.3
[127] highr_0.10 dbplyr_2.3.3
[129] fastmap_1.1.1 rlang_1.1.1
[131] GlobalOptions_0.1.2 htmlwidgets_1.6.2
[133] shiny_1.7.4.1 farver_2.1.1
[135] jquerylib_0.1.4 zoo_1.8-12
[137] jsonlite_1.8.7 BiocParallel_1.34.2
[139] GOSemSim_2.26.1 RCurl_1.98-1.12
[141] GenomeInfoDbData_1.2.10 ggplotify_0.1.1
[143] munsell_0.5.0 Rcpp_1.0.11
[145] ape_5.7-1 babelgene_22.9
[147] viridis_0.6.4 reticulate_1.36.1
[149] stringi_1.7.12 ggraph_2.1.0
[151] zlibbioc_1.46.0 MASS_7.3-60
[153] plyr_1.8.8 parallel_4.3.1
[155] listenv_0.9.0 ggrepel_0.9.3
[157] deldir_1.0-9 Biostrings_2.68.1
[159] graphlayouts_1.0.0 splines_4.3.1
[161] tensor_1.5 hms_1.1.3
[163] ps_1.7.5 igraph_1.5.0.1
[165] spatstat.geom_3.2-4 reshape2_1.4.4
[167] stats4_4.3.1 XML_3.99-0.14
[169] evaluate_0.21 tzdb_0.4.0
[171] tweenr_2.0.2 httpuv_1.6.11
[173] RANN_2.6.1 polyclip_1.10-4
[175] future_1.33.0 scattermore_1.2
[177] ggforce_0.4.1 xtable_1.8-4
[179] tidytree_0.4.4 later_1.3.1
[181] ragg_1.2.5 viridisLite_0.4.2
[183] aplot_0.1.10 memoise_2.0.1
[185] AnnotationDbi_1.62.2 IRanges_2.34.1
[187] cluster_2.1.4 timechange_0.2.0
[189] globals_0.16.2