characterize adult mLN and iLN

Last updated: 2025-07-14

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Knit directory: LNdevMouse24.2/

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Rmd	0ff885c	angeldemartin	2025-07-11	july11

load packages

library(ExploreSCdataSeurat3)
library(runSeurat3)
library(Seurat)
library(ggpubr)
library(pheatmap)
library(SingleCellExperiment)
library(dplyr)
library(tidyverse)
library(viridis)
library(muscat)
library(circlize)
library(destiny)
library(scater)
library(metap)
library(multtest)
library(clusterProfiler)
library(org.Hs.eg.db)
library(msigdbr)
library(enrichplot)
library(DOSE)
library(grid)
library(gridExtra)
library(ggupset)
library(VennDiagram)
library(NCmisc)
library(here)

load int object adult

basedir <- here()
fileNam <- paste0(basedir, "/data/LNmLToRev_adultonly_seurat.integrated.rds")
seuratA.int <- readRDS(fileNam)

set color vectors

colLab <- c("#42a071", "#900C3F","#b66e8d", "#61a4ba", "#424671", "#003C67FF",
            "#e3953d", "#714542", "#b6856e")

names(colLab) <- c("FDC/MRC", "TRC", "TBRC", "MedRC/IFRC", "MedRC" , "actMedRC",
                   "PRC", "Pi16+RC", "VSMC")

coltimepoint <- c("#440154FF", "#3B528BFF", "#21908CFF", "#5DC863FF")
names(coltimepoint) <- c("E18", "P7", "3w", "8w")

collocation <- c("#61baba", "#ba6161")
names(collocation) <- c("iLN", "mLN")

colLoc <- c("#61baba", "#ba6161")
names(colLoc) <- unique(seuratA.int$location)

umap label

DimPlot(seuratA.int, reduction = "umap", group.by = "label", cols = colLab)+
  theme_bw() +
  theme(axis.text = element_blank(), axis.ticks = element_blank(), 
        panel.grid.minor = element_blank()) +
  xlab("UMAP1") +
  ylab("UMAP2")

counts

knitr::kable(table(seuratA.int$label, seuratA.int$location))

	iLN	mLN
actMedRC	2592	1428
FDC/MRC	449	843
MedRC	1960	411
MedRC/IFRC	2660	2208
Pi16+RC	46	554
PRC	849	1343
TBRC	2042	1799
TRC	1611	1228
VSMC	138	127

knitr::kable(table(seuratA.int$location))

Var1	Freq
iLN	12347
mLN	9941

seuratSub <- subset(seuratA.int, Rosa26eyfp.Rosa26eyfp>0)
eyfpPos <- colnames(seuratSub)

seuratA.int$EYFP <-"neg"
seuratA.int$EYFP[which(colnames(seuratA.int)%in%eyfpPos)] <- "pos"
table(seuratA.int$dataset, seuratA.int$EYFP)

                                                   
                                                     neg  pos
  380131_11-11_20250305_Mu_Cxcl13EYFP_Adult_pLN_FRC 1875 3378
  380131_12-12_20250305_Mu_Cxcl13EYFP_Adult_mLN_FRC 2634 3785
  382581_08-8_20250320_Mu_Cxcl13EYFP_Adult_pLN_FRC  2970 4124
  382581_09-9_20250320_Mu_Cxcl13EYFP_Adult_mLN_FRC   848 2674

knitr::kable(table( seuratA.int$label, seuratA.int$EYFP))

	neg	pos
actMedRC	1410	2610
FDC/MRC	301	991
MedRC	828	1543
MedRC/IFRC	2051	2817
Pi16+RC	329	271
PRC	1181	1011
TBRC	1280	2561
TRC	774	2065
VSMC	173	92

## relative abundance per location
clustCond <- data.frame(table(seuratA.int$location, seuratA.int$label))
colnames(clustCond) <- c("location", "intCluster", "cnt")
condTot <- data.frame(table(seuratA.int$location))
colnames(condTot) <- c("location", "tot")
colPaldat <- data.frame(col=colLab) %>%
  rownames_to_column(var = "intCluster")
clustDat2 <- clustCond %>%  left_join(., condTot, by = "location") %>% 
  mutate(relAb = cnt/tot * 100) %>%
  left_join(., colPaldat, by = "intCluster")

knitr::kable(clustDat2)

location	intCluster	cnt	tot	relAb	col
iLN	actMedRC	2592	12347	20.9929538	#003C67FF
mLN	actMedRC	1428	9941	14.3647520	#003C67FF
iLN	FDC/MRC	449	12347	3.6365109	#42a071
mLN	FDC/MRC	843	9941	8.4800322	#42a071
iLN	MedRC	1960	12347	15.8743014	#424671
mLN	MedRC	411	9941	4.1343929	#424671
iLN	MedRC/IFRC	2660	12347	21.5436948	#61a4ba
mLN	MedRC/IFRC	2208	9941	22.2110452	#61a4ba
iLN	Pi16+RC	46	12347	0.3725601	#714542
mLN	Pi16+RC	554	9941	5.5728800	#714542
iLN	PRC	849	12347	6.8761643	#e3953d
mLN	PRC	1343	9941	13.5097073	#e3953d
iLN	TBRC	2042	12347	16.5384304	#b66e8d
mLN	TBRC	1799	9941	18.0967709	#b66e8d
iLN	TRC	1611	12347	13.0477039	#900C3F
mLN	TRC	1228	9941	12.3528820	#900C3F
iLN	VSMC	138	12347	1.1176804	#b6856e
mLN	VSMC	127	9941	1.2775375	#b6856e

relative subset abundance

lapply(names(colLoc), function(co){
  clustDat2sel <- clustDat2 %>% filter(location==co)
  pie(clustDat2sel$relAb,
      labels = clustDat2sel$intCluster,
      col = clustDat2sel$col,
      main = paste0(co))
})

[[1]]
NULL

[[2]]
NULL

fraction EYFP+

across all

## across all
eyfpCnt <- data.frame(table(seuratA.int$label, seuratA.int$EYFP)) %>%
  spread(.,Var2 , Freq) %>% mutate(tot=pos+neg) %>% 
  mutate(freqPos=pos*100/tot) %>% mutate(freqNeg=neg*100/tot) 

eyfpCntDat <- eyfpCnt %>% dplyr::select(Var1, freqPos, freqNeg) %>% 
  gather(., eyfp, freq, freqPos:freqNeg)

p <- ggpubr::ggbarplot(eyfpCntDat, x="Var1", y="freq", fill="eyfp",
                  palette = c("#9d9f9e","#09983f"),
                  order = rev(names(colLab)),
                  xlab = "", ylab = "Frequency",
                  orientation = "horizontal") +
  theme(legend.position = "right")
p

individual LNs

LNvec <- unique(seuratA.int$location)

lapply(LNvec, function(ln){
  seuratSub <- subset(seuratA.int, location== ln)
  eyfpCnt <- data.frame(table(seuratSub$label, seuratSub$EYFP)) %>%
  spread(.,Var2 , Freq) %>% mutate(tot=pos+neg) %>% 
  mutate(freqPos=pos*100/tot) %>% mutate(freqNeg=neg*100/tot) 

  eyfpCntDat <- eyfpCnt %>% dplyr::select(Var1, freqPos, freqNeg) %>% 
    gather(., eyfp, freq, freqPos:freqNeg)

  p <- ggpubr::ggbarplot(eyfpCntDat, x="Var1", y="freq", fill="eyfp",
                  palette = c("#9d9f9e","#09983f"),
                  order = rev(names(colLab)),
                  xlab = "", ylab = "Frequency",
                  orientation = "horizontal") +
    theme(legend.position = "right") +
    ggtitle(paste0("Fraction EYFP+ cells - ", ln))
  p

})

[[1]]


[[2]]

avgHeatmap <- function(seurat, selGenes, colVecIdent, colVecCond=NULL,
                       ordVec=NULL, gapVecR=NULL, gapVecC=NULL,cc=FALSE,
                       cr=FALSE, condCol=FALSE){
  
  selGenes <- selGenes$gene
  
  ## assay data
  clusterAssigned <- as.data.frame(Idents(seurat)) %>%
  dplyr::mutate(cell=rownames(.))
  colnames(clusterAssigned)[1] <- "ident"
  seuratDat <- GetAssayData(seurat)
  
  ## genes of interest
  genes <- data.frame(gene=rownames(seurat)) %>% 
    mutate(geneID=gsub("^.*\\.", "", gene)) %>% filter(geneID %in% selGenes)

  ## matrix with averaged cnts per ident
  logNormExpres <- as.data.frame(t(as.matrix(
    seuratDat[which(rownames(seuratDat) %in% genes$gene),])))
  logNormExpres <- logNormExpres %>% dplyr::mutate(cell=rownames(.)) %>%
    dplyr::left_join(.,clusterAssigned, by=c("cell")) %>%
    dplyr::select(-cell) %>% dplyr::group_by(ident) %>%
    dplyr::summarise_all(mean)
  logNormExpresMa <- logNormExpres %>% dplyr::select(-ident) %>% as.matrix()
  rownames(logNormExpresMa) <- logNormExpres$ident
  logNormExpresMa <- t(logNormExpresMa)
  rownames(logNormExpresMa) <- gsub("^.*?\\.","",rownames(logNormExpresMa))
  
  ## remove genes if they are all the same in all groups
  ind <- apply(logNormExpresMa, 1, sd) == 0
  logNormExpresMa <- logNormExpresMa[!ind,]
  genes <- genes[!ind,]

  ## color columns according to cluster
  annotation_col <- as.data.frame(gsub("(^.*?_)","",
                                       colnames(logNormExpresMa)))%>%
    dplyr::mutate(celltype=gsub("(_.*$)","",colnames(logNormExpresMa)))
  colnames(annotation_col)[1] <- "col1"
  annotation_col <- annotation_col %>%
    dplyr::mutate(cond = gsub(".*_","",col1)) %>%
    dplyr::select(cond, celltype)
  rownames(annotation_col) <- colnames(logNormExpresMa) 

  ann_colors = list(
      cond = colVecCond,
      celltype=colVecIdent)
  if(is.null(ann_colors$cond)){
    annotation_col$cond <- NULL
  }
  
  ## adjust order
  logNormExpresMa <- logNormExpresMa[selGenes,]
  if(is.null(ordVec)){
    ordVec <- levels(seurat)
  }
  logNormExpresMa <- logNormExpresMa[,ordVec]

  ## scaled row-wise
  pheatmap(logNormExpresMa, scale="row" ,treeheight_row = 0, cluster_rows = cr, 
         cluster_cols = cc,
         color = colorRampPalette(c("#2166AC", "#F7F7F7", "#B2182B"))(50),
         annotation_col = annotation_col, cellwidth=15, cellheight=10,
         annotation_colors = ann_colors, gaps_row = gapVecR, gaps_col = gapVecC)
}

vis FRC marker

avg heatmap

seurat_markers <- data.frame(gene=c("Fcgr2b","Fcer2a","Cr2","Cxcl13",
                                    "Slc7a11", "Ccl19", 
                                    "Ccl21a", "Fmod", "Grem1", "Bmp4",
                                    "Tnfsf11", "Fbn2",
                                    "Pltp" ,"C1rb", "Lepr", "Ptn",
                                     "Nr4a1", "Cxcl10", "Cxcl9", 
                                    "F3", "Fbln1", "Gdf10", "Adamtsl1", 
                                    "Col15a1",  "Cd34",
                                    "Igfbp6", "Pi16", "Thy1", "Dpp4", "Sema3c", 
                                    "Acta2", "Myh11", "Mcam", "Itga7", "Esam", "Rgs4"
                                    ))

genes <- data.frame(geneID=rownames(seuratA.int)) %>%
  mutate(gene=gsub(".*\\.", "",  geneID)) 

markerAll <- seurat_markers %>% left_join(., genes, by="gene")

ordVec <- names(colLab)
Idents(seuratA.int) <- seuratA.int$label
pOut <- avgHeatmap(seurat = seuratA.int, selGenes = markerAll,
                  colVecIdent = colLab, 
                  ordVec=ordVec,
                  gapVecR=NULL, gapVecC=NULL,cc=F,
                  cr=F, condCol=F)

dotplot

## Dotplot all
seuratA.int$label <- factor(seuratA.int$label, levels = names(colLab))
Idents(seuratA.int) <- seuratA.int$label

DotPlot(seuratA.int, assay="RNA", features = rev(markerAll$geneID), scale =T,
        cluster.idents = F) +
  scale_color_viridis_c() +
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  scale_x_discrete(breaks=rev(markerAll$geneID), labels=rev(markerAll$gene)) +
  xlab("") + ylab("")

session info

date()

[1] "Mon Jul 14 16:20:46 2025"

sessionInfo()

R version 4.4.0 (2024-04-24)
Platform: x86_64-apple-darwin20
Running under: macOS Ventura 13.7.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

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] grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.1                  NCmisc_1.2.0                VennDiagram_1.7.3          
 [4] futile.logger_1.4.3         ggupset_0.4.1               gridExtra_2.3              
 [7] DOSE_3.30.5                 enrichplot_1.24.4           msigdbr_24.1.0             
[10] org.Hs.eg.db_3.19.1         AnnotationDbi_1.66.0        clusterProfiler_4.12.6     
[13] multtest_2.60.0             metap_1.12                  scater_1.32.1              
[16] scuttle_1.14.0              destiny_3.18.0              circlize_0.4.16            
[19] muscat_1.18.0               viridis_0.6.5               viridisLite_0.4.2          
[22] lubridate_1.9.4             forcats_1.0.0               stringr_1.5.1              
[25] purrr_1.0.4                 readr_2.1.5                 tidyr_1.3.1                
[28] tibble_3.2.1                tidyverse_2.0.0             dplyr_1.1.4                
[31] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0             
[34] GenomicRanges_1.56.2        GenomeInfoDb_1.40.1         IRanges_2.38.1             
[37] S4Vectors_0.42.1            BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
[40] matrixStats_1.5.0           pheatmap_1.0.13             ggpubr_0.6.0               
[43] ggplot2_3.5.2               Seurat_5.3.0                SeuratObject_5.1.0         
[46] sp_2.2-0                    runSeurat3_0.1.0            ExploreSCdataSeurat3_0.1.0 

loaded via a namespace (and not attached):
  [1] igraph_2.1.4              ica_1.0-3                 plotly_4.10.4            
  [4] Formula_1.2-5             zlibbioc_1.50.0           tidyselect_1.2.1         
  [7] bit_4.6.0                 doParallel_1.0.17         clue_0.3-66              
 [10] lattice_0.22-7            rjson_0.2.23              blob_1.2.4               
 [13] S4Arrays_1.4.1            pbkrtest_0.5.4            parallel_4.4.0           
 [16] png_0.1-8                 plotrix_3.8-4             cli_3.6.5                
 [19] ggplotify_0.1.2           goftest_1.2-3             VIM_6.2.2                
 [22] variancePartition_1.34.0  BiocNeighbors_1.22.0      shadowtext_0.1.4         
 [25] uwot_0.2.3                curl_6.2.3                tidytree_0.4.6           
 [28] mime_0.13                 evaluate_1.0.3            ComplexHeatmap_2.20.0    
 [31] stringi_1.8.7             backports_1.5.0           lmerTest_3.1-3           
 [34] qqconf_1.3.2              httpuv_1.6.16             magrittr_2.0.3           
 [37] rappdirs_0.3.3            splines_4.4.0             ggraph_2.2.1             
 [40] sctransform_0.4.2         ggbeeswarm_0.7.2          DBI_1.2.3                
 [43] jquerylib_0.1.4           smoother_1.3              withr_3.0.2              
 [46] git2r_0.36.2              corpcor_1.6.10            reformulas_0.4.1         
 [49] class_7.3-23              rprojroot_2.0.4           lmtest_0.9-40            
 [52] tidygraph_1.3.1           formatR_1.14              colourpicker_1.3.0       
 [55] htmlwidgets_1.6.4         fs_1.6.6                  ggrepel_0.9.6            
 [58] labeling_0.4.3            fANCOVA_0.6-1             SparseArray_1.4.8        
 [61] DESeq2_1.44.0             ranger_0.17.0             DEoptimR_1.1-3-1         
 [64] reticulate_1.42.0         hexbin_1.28.5             zoo_1.8-14               
 [67] XVector_0.44.0            knitr_1.50                ggplot.multistats_1.0.1  
 [70] UCSC.utils_1.0.0          RhpcBLASctl_0.23-42       timechange_0.3.0         
 [73] foreach_1.5.2             patchwork_1.3.0           caTools_1.18.3           
 [76] ggtree_3.12.0             data.table_1.17.4         R.oo_1.27.1              
 [79] RSpectra_0.16-2           irlba_2.3.5.1             gridGraphics_0.5-1       
 [82] fastDummies_1.7.5         lazyeval_0.2.2            yaml_2.3.10              
 [85] survival_3.8-3            scattermore_1.2           crayon_1.5.3             
 [88] RcppAnnoy_0.0.22          RColorBrewer_1.1-3        progressr_0.15.1         
 [91] tweenr_2.0.3              later_1.4.2               ggridges_0.5.6           
 [94] codetools_0.2-20          GlobalOptions_0.1.2       aod_1.3.3                
 [97] KEGGREST_1.44.1           Rtsne_0.17                shape_1.4.6.1            
[100] limma_3.60.6              pkgconfig_2.0.3           TMB_1.9.17               
[103] spatstat.univar_3.1-3     mathjaxr_1.8-0            EnvStats_3.1.0           
[106] aplot_0.2.5               scatterplot3d_0.3-44      ape_5.8-1                
[109] spatstat.sparse_3.1-0     xtable_1.8-4              car_3.1-3                
[112] plyr_1.8.9                httr_1.4.7                rbibutils_2.3            
[115] tools_4.4.0               globals_0.18.0            beeswarm_0.4.0           
[118] broom_1.0.8               nlme_3.1-168              lambda.r_1.2.4           
[121] assertthat_0.2.1          lme4_1.1-37               digest_0.6.37            
[124] numDeriv_2016.8-1.1       Matrix_1.7-3              farver_2.1.2             
[127] tzdb_0.5.0                remaCor_0.0.18            reshape2_1.4.4           
[130] yulab.utils_0.2.0         glue_1.8.0                cachem_1.1.0             
[133] polyclip_1.10-7           generics_0.1.4            Biostrings_2.72.1        
[136] mvtnorm_1.3-3             parallelly_1.45.0         mnormt_2.1.1             
[139] statmod_1.5.0             RcppHNSW_0.6.0            ScaledMatrix_1.12.0      
[142] carData_3.0-5             minqa_1.2.8               pbapply_1.7-2            
[145] httr2_1.1.2               spam_2.11-1               gson_0.1.0               
[148] graphlayouts_1.2.2        gtools_3.9.5              ggsignif_0.6.4           
[151] RcppEigen_0.3.4.0.2       shiny_1.10.0              GenomeInfoDbData_1.2.12  
[154] glmmTMB_1.1.11            R.utils_2.13.0            memoise_2.0.1            
[157] rmarkdown_2.29            scales_1.4.0              R.methodsS3_1.8.2        
[160] future_1.58.0             RANN_2.6.2                Cairo_1.6-2              
[163] spatstat.data_3.1-6       rstudioapi_0.17.1         cluster_2.1.8.1          
[166] whisker_0.4.1             mutoss_0.1-13             spatstat.utils_3.1-4     
[169] hms_1.1.3                 fitdistrplus_1.2-2        cowplot_1.1.3            
[172] colorspace_2.1-1          rlang_1.1.6               DelayedMatrixStats_1.26.0
[175] sparseMatrixStats_1.16.0  xts_0.14.1                dotCall64_1.2            
[178] shinydashboard_0.7.3      ggforce_0.4.2             laeken_0.5.3             
[181] mgcv_1.9-3                xfun_0.52                 e1071_1.7-16             
[184] TH.data_1.1-3             iterators_1.0.14          abind_1.4-8              
[187] GOSemSim_2.30.2           treeio_1.28.0             futile.options_1.0.1     
[190] bitops_1.0-9              Rdpack_2.6.4              promises_1.3.3           
[193] scatterpie_0.2.4          RSQLite_2.4.0             qvalue_2.36.0            
[196] sandwich_3.1-1            fgsea_1.30.0              DelayedArray_0.30.1      
[199] proxy_0.4-27              GO.db_3.19.1              compiler_4.4.0           
[202] prettyunits_1.2.0         boot_1.3-31               beachmat_2.20.0          
[205] listenv_0.9.1             Rcpp_1.0.14               edgeR_4.2.2              
[208] workflowr_1.7.1           BiocSingular_1.20.0       tensor_1.5               
[211] MASS_7.3-65               progress_1.2.3            BiocParallel_1.38.0      
[214] babelgene_22.9            spatstat.random_3.4-1     R6_2.6.1                 
[217] fastmap_1.2.0             multcomp_1.4-28           fastmatch_1.1-6          
[220] rstatix_0.7.2             vipor_0.4.7               TTR_0.24.4               
[223] ROCR_1.0-11               TFisher_0.2.0             rsvd_1.0.5               
[226] vcd_1.4-13                nnet_7.3-20               gtable_0.3.6             
[229] KernSmooth_2.23-26        miniUI_0.1.2              deldir_2.0-4             
[232] htmltools_0.5.8.1         ggthemes_5.1.0            bit64_4.6.0-1            
[235] spatstat.explore_3.4-3    lifecycle_1.0.4           blme_1.0-6               
[238] nloptr_2.2.1              sass_0.4.10               vctrs_0.6.5              
[241] robustbase_0.99-4-1       spatstat.geom_3.4-1       sn_2.1.1                 
[244] ggfun_0.1.8               future.apply_1.11.3       bslib_0.9.0              
[247] pillar_1.10.2             gplots_3.2.0              pcaMethods_1.96.0        
[250] locfit_1.5-9.12           jsonlite_2.0.0            GetoptLong_1.0.5

sessionInfo()

R version 4.4.0 (2024-04-24)
Platform: x86_64-apple-darwin20
Running under: macOS Ventura 13.7.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

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] grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.1                  NCmisc_1.2.0                VennDiagram_1.7.3          
 [4] futile.logger_1.4.3         ggupset_0.4.1               gridExtra_2.3              
 [7] DOSE_3.30.5                 enrichplot_1.24.4           msigdbr_24.1.0             
[10] org.Hs.eg.db_3.19.1         AnnotationDbi_1.66.0        clusterProfiler_4.12.6     
[13] multtest_2.60.0             metap_1.12                  scater_1.32.1              
[16] scuttle_1.14.0              destiny_3.18.0              circlize_0.4.16            
[19] muscat_1.18.0               viridis_0.6.5               viridisLite_0.4.2          
[22] lubridate_1.9.4             forcats_1.0.0               stringr_1.5.1              
[25] purrr_1.0.4                 readr_2.1.5                 tidyr_1.3.1                
[28] tibble_3.2.1                tidyverse_2.0.0             dplyr_1.1.4                
[31] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0             
[34] GenomicRanges_1.56.2        GenomeInfoDb_1.40.1         IRanges_2.38.1             
[37] S4Vectors_0.42.1            BiocGenerics_0.50.0         MatrixGenerics_1.16.0      
[40] matrixStats_1.5.0           pheatmap_1.0.13             ggpubr_0.6.0               
[43] ggplot2_3.5.2               Seurat_5.3.0                SeuratObject_5.1.0         
[46] sp_2.2-0                    runSeurat3_0.1.0            ExploreSCdataSeurat3_0.1.0 

loaded via a namespace (and not attached):
  [1] igraph_2.1.4              ica_1.0-3                 plotly_4.10.4            
  [4] Formula_1.2-5             zlibbioc_1.50.0           tidyselect_1.2.1         
  [7] bit_4.6.0                 doParallel_1.0.17         clue_0.3-66              
 [10] lattice_0.22-7            rjson_0.2.23              blob_1.2.4               
 [13] S4Arrays_1.4.1            pbkrtest_0.5.4            parallel_4.4.0           
 [16] png_0.1-8                 plotrix_3.8-4             cli_3.6.5                
 [19] ggplotify_0.1.2           goftest_1.2-3             VIM_6.2.2                
 [22] variancePartition_1.34.0  BiocNeighbors_1.22.0      shadowtext_0.1.4         
 [25] uwot_0.2.3                curl_6.2.3                tidytree_0.4.6           
 [28] mime_0.13                 evaluate_1.0.3            ComplexHeatmap_2.20.0    
 [31] stringi_1.8.7             backports_1.5.0           lmerTest_3.1-3           
 [34] qqconf_1.3.2              httpuv_1.6.16             magrittr_2.0.3           
 [37] rappdirs_0.3.3            splines_4.4.0             ggraph_2.2.1             
 [40] sctransform_0.4.2         ggbeeswarm_0.7.2          DBI_1.2.3                
 [43] jquerylib_0.1.4           smoother_1.3              withr_3.0.2              
 [46] git2r_0.36.2              corpcor_1.6.10            reformulas_0.4.1         
 [49] class_7.3-23              rprojroot_2.0.4           lmtest_0.9-40            
 [52] tidygraph_1.3.1           formatR_1.14              colourpicker_1.3.0       
 [55] htmlwidgets_1.6.4         fs_1.6.6                  ggrepel_0.9.6            
 [58] labeling_0.4.3            fANCOVA_0.6-1             SparseArray_1.4.8        
 [61] DESeq2_1.44.0             ranger_0.17.0             DEoptimR_1.1-3-1         
 [64] reticulate_1.42.0         hexbin_1.28.5             zoo_1.8-14               
 [67] XVector_0.44.0            knitr_1.50                ggplot.multistats_1.0.1  
 [70] UCSC.utils_1.0.0          RhpcBLASctl_0.23-42       timechange_0.3.0         
 [73] foreach_1.5.2             patchwork_1.3.0           caTools_1.18.3           
 [76] ggtree_3.12.0             data.table_1.17.4         R.oo_1.27.1              
 [79] RSpectra_0.16-2           irlba_2.3.5.1             gridGraphics_0.5-1       
 [82] fastDummies_1.7.5         lazyeval_0.2.2            yaml_2.3.10              
 [85] survival_3.8-3            scattermore_1.2           crayon_1.5.3             
 [88] RcppAnnoy_0.0.22          RColorBrewer_1.1-3        progressr_0.15.1         
 [91] tweenr_2.0.3              later_1.4.2               ggridges_0.5.6           
 [94] codetools_0.2-20          GlobalOptions_0.1.2       aod_1.3.3                
 [97] KEGGREST_1.44.1           Rtsne_0.17                shape_1.4.6.1            
[100] limma_3.60.6              pkgconfig_2.0.3           TMB_1.9.17               
[103] spatstat.univar_3.1-3     mathjaxr_1.8-0            EnvStats_3.1.0           
[106] aplot_0.2.5               scatterplot3d_0.3-44      ape_5.8-1                
[109] spatstat.sparse_3.1-0     xtable_1.8-4              car_3.1-3                
[112] plyr_1.8.9                httr_1.4.7                rbibutils_2.3            
[115] tools_4.4.0               globals_0.18.0            beeswarm_0.4.0           
[118] broom_1.0.8               nlme_3.1-168              lambda.r_1.2.4           
[121] assertthat_0.2.1          lme4_1.1-37               digest_0.6.37            
[124] numDeriv_2016.8-1.1       Matrix_1.7-3              farver_2.1.2             
[127] tzdb_0.5.0                remaCor_0.0.18            reshape2_1.4.4           
[130] yulab.utils_0.2.0         glue_1.8.0                cachem_1.1.0             
[133] polyclip_1.10-7           generics_0.1.4            Biostrings_2.72.1        
[136] mvtnorm_1.3-3             parallelly_1.45.0         mnormt_2.1.1             
[139] statmod_1.5.0             RcppHNSW_0.6.0            ScaledMatrix_1.12.0      
[142] carData_3.0-5             minqa_1.2.8               pbapply_1.7-2            
[145] httr2_1.1.2               spam_2.11-1               gson_0.1.0               
[148] graphlayouts_1.2.2        gtools_3.9.5              ggsignif_0.6.4           
[151] RcppEigen_0.3.4.0.2       shiny_1.10.0              GenomeInfoDbData_1.2.12  
[154] glmmTMB_1.1.11            R.utils_2.13.0            memoise_2.0.1            
[157] rmarkdown_2.29            scales_1.4.0              R.methodsS3_1.8.2        
[160] future_1.58.0             RANN_2.6.2                Cairo_1.6-2              
[163] spatstat.data_3.1-6       rstudioapi_0.17.1         cluster_2.1.8.1          
[166] whisker_0.4.1             mutoss_0.1-13             spatstat.utils_3.1-4     
[169] hms_1.1.3                 fitdistrplus_1.2-2        cowplot_1.1.3            
[172] colorspace_2.1-1          rlang_1.1.6               DelayedMatrixStats_1.26.0
[175] sparseMatrixStats_1.16.0  xts_0.14.1                dotCall64_1.2            
[178] shinydashboard_0.7.3      ggforce_0.4.2             laeken_0.5.3             
[181] mgcv_1.9-3                xfun_0.52                 e1071_1.7-16             
[184] TH.data_1.1-3             iterators_1.0.14          abind_1.4-8              
[187] GOSemSim_2.30.2           treeio_1.28.0             futile.options_1.0.1     
[190] bitops_1.0-9              Rdpack_2.6.4              promises_1.3.3           
[193] scatterpie_0.2.4          RSQLite_2.4.0             qvalue_2.36.0            
[196] sandwich_3.1-1            fgsea_1.30.0              DelayedArray_0.30.1      
[199] proxy_0.4-27              GO.db_3.19.1              compiler_4.4.0           
[202] prettyunits_1.2.0         boot_1.3-31               beachmat_2.20.0          
[205] listenv_0.9.1             Rcpp_1.0.14               edgeR_4.2.2              
[208] workflowr_1.7.1           BiocSingular_1.20.0       tensor_1.5               
[211] MASS_7.3-65               progress_1.2.3            BiocParallel_1.38.0      
[214] babelgene_22.9            spatstat.random_3.4-1     R6_2.6.1                 
[217] fastmap_1.2.0             multcomp_1.4-28           fastmatch_1.1-6          
[220] rstatix_0.7.2             vipor_0.4.7               TTR_0.24.4               
[223] ROCR_1.0-11               TFisher_0.2.0             rsvd_1.0.5               
[226] vcd_1.4-13                nnet_7.3-20               gtable_0.3.6             
[229] KernSmooth_2.23-26        miniUI_0.1.2              deldir_2.0-4             
[232] htmltools_0.5.8.1         ggthemes_5.1.0            bit64_4.6.0-1            
[235] spatstat.explore_3.4-3    lifecycle_1.0.4           blme_1.0-6               
[238] nloptr_2.2.1              sass_0.4.10               vctrs_0.6.5              
[241] robustbase_0.99-4-1       spatstat.geom_3.4-1       sn_2.1.1                 
[244] ggfun_0.1.8               future.apply_1.11.3       bslib_0.9.0              
[247] pillar_1.10.2             gplots_3.2.0              pcaMethods_1.96.0        
[250] locfit_1.5-9.12           jsonlite_2.0.0            GetoptLong_1.0.5