tradeSeq mLN EYFP+
A.DeMartin
2025-05-12
Last updated: 2025-07-15
Checks: 6 1
Knit directory: LNdevMouse24.2/
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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(slingshot)
library(RColorBrewer)
library(tradeSeq)
library(scran)
library(clusterExperiment)
library(here)load sce slingshot mLN EYFP+
basedir <- here()
fileNam <- paste0(basedir, "/data/LNmLToRev_EYFP_mLNf3_slingshot_v2_sce.rds")
scemLNf3v2<- readRDS(fileNam)load object mLN EYFP+ fil
fileNam <- paste0(basedir, "/data/LNmLToRev_EYFP_mLNf3_seurat.rds")
seuratmLNf3 <- readRDS(fileNam)
table(seuratmLNf3$dataset)
380131_04-4_20250224_Cxcl13EYFP_P7_mLN_YFPpos 380131_05-5_20250224_Cxcl13EYFP_P7_mLN_YFPneg
3922 12
380131_07-7_20250225_Cxcl13EYFP_E18_mLN_YFPpos 380131_08-8_20250225_Cxcl13EYFP_E18_mLN_YFPneg
2392 5
380131_12-12_20250305_Mu_Cxcl13EYFP_Adult_mLN_FRC 382581_02-2_20250311_Mu_Cxcl13EYFP_E18_mLN_YFPpos
3798 3617
382581_03-3_20250311_Mu_Cxcl13EYFP_E18_mLN_YFPneg 382581_06-6_20250319_Mu_Cxcl13EYFP_P7_mLN_YFPpos
15 4274
382581_07-7_20250319_Mu_Cxcl13EYFP_P7_mLN_YFPneg 382581_09-9_20250320_Mu_Cxcl13EYFP_Adult_mLN_FRC
11 2711
382581_14-14_20250402_Mu_Cxcl13EYFP_3wk_mLN_fib1 382581_15-15_20250402_Mu_Cxcl13EYFP_3wk_mLN_fib2
3223 4289 colPal <- c("#DAF7A6", "#FFC300", "#FF5733", "#C70039", "#900C3F", "#b66e8d",
"#61a4ba", "#6178ba", "#54a87f", "#25328a",
"#b6856e", "#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF",
"#7AA6DCFF", "#003C67FF", "#8F7700FF", "#3B3B3BFF", "#A73030FF",
"#4A6990FF")[1:length(unique(seuratmLNf3$RNA_snn_res.0.4))]
names(colPal) <- unique(seuratmLNf3$RNA_snn_res.0.4)
coltimepoint <- c("#440154FF", "#3B528BFF", "#21908CFF", "#5DC863FF")
names(coltimepoint) <- c("E18", "P7", "3w", "8w")
collocation <- c("#61baba", "#ba6161")
names(collocation) <- c("iLN", "mLN")clustDat <- data.frame(clustCol=colPal) %>% rownames_to_column(., "cluster")
timepointDat <- data.frame(ageCol=coltimepoint) %>% rownames_to_column(., "timepoint")
colDat <- data.frame(cluster=scemLNf3v2$RNA_snn_res.0.4) %>%
mutate(timepoint=scemLNf3v2$timepoint) %>% left_join(., clustDat, by="cluster") %>%
left_join(., timepointDat, by="timepoint")plot(reducedDims(scemLNf3v2)$UMAP, col = colDat$clustCol, pch=16, asp = 1)
lines(SlingshotDataSet(scemLNf3v2), lwd=2, col='black')
tradeSeq
evaluate k
icMat <- evaluateK(counts = counts(scemLNf3v2), sds = SlingshotDataSet(scemLNf3v2), k = 3:10,
nGenes = 200, verbose = T)plot results from icMat
write.table(icMat, file=paste0(basedir,"/data/icMat.txt", sep = "\t", quote = FALSE, row.names = TRUE)
icMat_clean <- icMat[complete.cases(icMat), ]
## Convert to data frame
avg_df <- data.frame(
k = colnames(icMat_clean),
AvgAIC = colMeans(icMat_clean)
)
## Preserve gene order if needed
avg_df$k <- factor(avg_df$k, levels = avg_df$k)
ggplot(avg_df, aes(x = k, y = AvgAIC, group = 1)) +
geom_line(color = "steelblue") +
geom_point(color = "darkred", size = 1.5) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 6)
) +
labs(x = "k", y = "Average AIC")subsample sce
dim(scemLNf3v2)cellSub <- data.frame(cell=colnames(scemLNf3v2)) %>% sample_n(5000)
sceSub <- scemLNf3v2[,cellSub$cell]
dim(sceSub)load sce sub
fileNam <- paste0(basedir, "/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_sce.rds")
sceSub <- readRDS(fileNam)colDat <- data.frame(cluster=sceSub$RNA_snn_res.0.4) %>%
mutate(timepoint=sceSub$timepoint) %>% left_join(., clustDat, by="cluster") %>%
left_join(., timepointDat, by="timepoint")
plot(reducedDims(sceSub)$UMAP, col = colDat$clustCol, pch=16, asp = 1)
lines(SlingshotDataSet(sceSub), lwd=2, col='black')
plot(reducedDims(sceSub)$UMAP, col = colDat$ageCol, pch=16, asp = 1)
lines(SlingshotDataSet(sceSub), lwd=2, col='black')
fitGAM
## only hvg
dec.sceSub <- modelGeneVar(sceSub)
topHVG <- getTopHVGs(dec.sceSub, n=2000)
pseudotime <- slingPseudotime(SlingshotDataSet(scemLNf3v2), na = FALSE)
pseudotimeSub <- pseudotime[cellSub$cell,]
cellWeights <- slingCurveWeights(SlingshotDataSet(scemLNf3v2))
cellWeightsSub <- cellWeights[cellSub$cell,]
sceGAM <- fitGAM(counts = counts(sceSub), pseudotime = pseudotimeSub,
cellWeights = cellWeightsSub,
nknots = 8, verbose = T, parallel=T, genes=topHVG)## save
saveRDS(sceGAM, file = paste0(basedir,"/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_sceGAM.rds"))
saveRDS(sceSub, file = paste0(basedir,"/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_sce.rds"))
saveRDS(pseudotimeSub, file = paste0(basedir,"/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_pseudotime.rds"))
saveRDS(cellWeightsSub, file = paste0(basedir,"/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_cellweights.rds"))
saveRDS(topHVG, file = paste0(basedir, "/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_topHVG.rds"))load GAM
fileNam <- paste0(basedir, "/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_sceGAM.rds")
sceGAM <- readRDS(fileNam)
table(rowData(sceGAM)$tradeSeq$converged)
TRUE
2000 Between lineage comparison
patternRes <- patternTest(sceGAM, l2fc = log2(2))
oPat <- order(patternRes$waldStat, decreasing = TRUE)
head(rownames(patternRes)[oPat])[1] "ENSMUSG00000029675.Eln" "ENSMUSG00000022037.Clu" "ENSMUSG00000030605.Mfge8"
[4] "ENSMUSG00000060924.Csmd1" "ENSMUSG00000020186.Csrp2" "ENSMUSG00000002985.Apoe" colLin <- c("#42a071","#900C3F","#424671","#e3953d","#b6856e")
names(colLin) <- c("1", "2", "3", "4", "5")
rankGene <- rownames(patternRes)[oPat]
lapply(rankGene[1:50], function(selGene){
plotSmoothers(sceGAM, counts(sceGAM), gene = selGene, curvesCols=colLin) +
ggtitle(selGene) +
scale_color_manual(values=colLin)
})[[1]]
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cluster genes betweeen lineages
nPointsClus <- 100clusPat <- clusterExpressionPatterns(sceGAM, nPoints = nPointsClus,
genes = rankGene[1:500], nReducedDims=20)saveRDS(clusPat, file = paste0(basedir, "/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_clusPat.rds")load clusPat
fileNam <- paste0(basedir, "/data/tradeSEQ/LNmLToRev_EYFP_mLNf3_slingshot_v2_TSsub5000_clusPat.rds")
clusPat <- readRDS(fileNam)clusterLabels <- primaryCluster(clusPat$rsec)
cUniq <- unique(clusterLabels)
cUniq <- cUniq[!cUniq == -1] ## remove unclustered genes
for (xx in cUniq) {
cId <- which(clusterLabels == xx)
p <- ggplot(data = data.frame(x = 1:nPointsClus,
y = rep(range(clusPat$yhatScaled[cId, ]),
nPointsClus / 2)),
aes(x = x, y = y)) +
geom_point(alpha = 0) +
labs(title = paste0("Cluster ", xx), x = "Pseudotime", y = "Normalized expression") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
for (ii in 1:length(cId)) {
geneId <- rownames(clusPat$yhatScaled)[cId[ii]]
p <- p +
geom_line(data = data.frame(x = rep(1:nPointsClus, 5),
y = clusPat$yhatScaled[geneId, ],
lineage = rep(1:5, each = nPointsClus)),
aes(col = as.character(lineage), group = lineage), lwd = 1.5)
}
p <- p + guides(color = FALSE) +
scale_color_manual(values = colLin,
breaks = c("1", "2", "3", "4", "5"))
print(p)
}
clusterLabels <- primaryCluster(clusPat$rsec)
cUniq <- unique(clusterLabels)
cUniq <- cUniq[!cUniq == -1] ## remove unclustered genes
for (xx in cUniq) {
cId <- which(clusterLabels == xx)
p <- ggplot(data = data.frame(x = 1:nPointsClus,
y = rep(range(clusPat$yhatScaled[cId, ]),
nPointsClus / 2)),
aes(x = x, y = y)) +
geom_point(alpha = 0) +
labs(title = paste0("Cluster ", xx), x = "Pseudotime", y = "Normalized expression") +
theme_classic() +
theme(plot.title = element_text(hjust = 0.5))
for (ii in 1:length(cId)) {
geneId <- rownames(clusPat$yhatScaled)[cId[ii]]
p <- p +
geom_line(data = data.frame(x = rep(1:nPointsClus, 5),
y = clusPat$yhatScaled[geneId, ],
lineage = rep(1:5, each = nPointsClus)),
aes(col = as.character(lineage), group = lineage), lwd = 0.5)
}
p <- p + guides(color = FALSE) +
scale_color_manual(values = colLin,
breaks = c("1", "2", "3", "4", "5"))
print(p)
}
clustList <- lapply(cUniq, function(cl){
cId <- which(clusterLabels == cl)
genes <- rownames(clusPat$yhatScaled)[cId]
})
names(clustList) <- cUniq
## save
saveRDS(clustList, file=paste0(basedir,"/data/tradeSEQ/diffLinGeneCluster.rds"))session info
date()[1] "Tue Jul 15 12:24:22 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 clusterExperiment_2.24.0 scran_1.32.0
[4] tradeSeq_1.18.0 RColorBrewer_1.1-3 slingshot_2.12.0
[7] TrajectoryUtils_1.12.0 princurve_2.1.6 NCmisc_1.2.0
[10] VennDiagram_1.7.3 futile.logger_1.4.3 ggupset_0.4.1
[13] gridExtra_2.3 DOSE_3.30.5 enrichplot_1.24.4
[16] msigdbr_24.1.0 org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0
[19] clusterProfiler_4.12.6 multtest_2.60.0 metap_1.12
[22] scater_1.32.1 scuttle_1.14.0 destiny_3.18.0
[25] circlize_0.4.16 muscat_1.18.0 viridis_0.6.5
[28] viridisLite_0.4.2 lubridate_1.9.4 forcats_1.0.0
[31] stringr_1.5.1 purrr_1.0.4 readr_2.1.5
[34] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
[37] dplyr_1.1.4 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[40] Biobase_2.64.0 GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[43] IRanges_2.38.1 S4Vectors_0.42.1 BiocGenerics_0.50.0
[46] MatrixGenerics_1.16.0 matrixStats_1.5.0 pheatmap_1.0.13
[49] ggpubr_0.6.0 ggplot2_3.5.2 Seurat_5.3.0
[52] SeuratObject_5.1.0 sp_2.2-0 runSeurat3_0.1.0
[55] 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] rngtools_1.5.2 S4Arrays_1.4.1 RNeXML_2.4.11
[16] pbkrtest_0.5.4 parallel_4.4.0 png_0.1-8
[19] plotrix_3.8-4 registry_0.5-1 cli_3.6.5
[22] ggplotify_0.1.2 goftest_1.2-3 VIM_6.2.2
[25] variancePartition_1.34.0 kernlab_0.9-33 bluster_1.14.0
[28] BiocNeighbors_1.22.0 shadowtext_0.1.4 uwot_0.2.3
[31] curl_6.2.3 zinbwave_1.26.0 tidytree_0.4.6
[34] mime_0.13 evaluate_1.0.3 ComplexHeatmap_2.20.0
[37] stringi_1.8.7 backports_1.5.0 XML_3.99-0.18
[40] lmerTest_3.1-3 qqconf_1.3.2 httpuv_1.6.16
[43] magrittr_2.0.3 rappdirs_0.3.3 splines_4.4.0
[46] ggraph_2.2.1 sctransform_0.4.2 ggbeeswarm_0.7.2
[49] HDF5Array_1.32.1 DBI_1.2.3 genefilter_1.86.0
[52] jquerylib_0.1.4 smoother_1.3 withr_3.0.2
[55] git2r_0.36.2 corpcor_1.6.10 reformulas_0.4.1
[58] class_7.3-23 rprojroot_2.0.4 lmtest_0.9-40
[61] tidygraph_1.3.1 BiocManager_1.30.25 formatR_1.14
[64] colourpicker_1.3.0 htmlwidgets_1.6.4 fs_1.6.6
[67] ggrepel_0.9.6 labeling_0.4.3 fANCOVA_0.6-1
[70] SparseArray_1.4.8 DESeq2_1.44.0 ranger_0.17.0
[73] DEoptimR_1.1-3-1 rncl_0.8.7 annotate_1.82.0
[76] reticulate_1.42.0 hexbin_1.28.5 zoo_1.8-14
[79] XVector_0.44.0 knitr_1.50 ggplot.multistats_1.0.1
[82] UCSC.utils_1.0.0 RhpcBLASctl_0.23-42 timechange_0.3.0
[85] foreach_1.5.2 patchwork_1.3.0 caTools_1.18.3
[88] rhdf5_2.48.0 ggtree_3.12.0 data.table_1.17.4
[91] R.oo_1.27.1 RSpectra_0.16-2 irlba_2.3.5.1
[94] gridGraphics_0.5-1 fastDummies_1.7.5 ade4_1.7-23
[97] lazyeval_0.2.2 yaml_2.3.10 survival_3.8-3
[100] scattermore_1.2 crayon_1.5.3 RcppAnnoy_0.0.22
[103] progressr_0.15.1 tweenr_2.0.3 later_1.4.2
[106] ggridges_0.5.6 codetools_0.2-20 GlobalOptions_0.1.2
[109] aod_1.3.3 KEGGREST_1.44.1 Rtsne_0.17
[112] shape_1.4.6.1 limma_3.60.6 pkgconfig_2.0.3
[115] xml2_1.3.8 TMB_1.9.17 spatstat.univar_3.1-3
[118] mathjaxr_1.8-0 EnvStats_3.1.0 aplot_0.2.5
[121] scatterplot3d_0.3-44 gridBase_0.4-7 ape_5.8-1
[124] spatstat.sparse_3.1-0 xtable_1.8-4 car_3.1-3
[127] plyr_1.8.9 httr_1.4.7 rbibutils_2.3
[130] tools_4.4.0 globals_0.18.0 beeswarm_0.4.0
[133] broom_1.0.8 nlme_3.1-168 lambda.r_1.2.4
[136] assertthat_0.2.1 lme4_1.1-37 digest_0.6.37
[139] numDeriv_2016.8-1.1 Matrix_1.7-3 farver_2.1.2
[142] tzdb_0.5.0 remaCor_0.0.18 reshape2_1.4.4
[145] yulab.utils_0.2.0 glue_1.8.0 cachem_1.1.0
[148] polyclip_1.10-7 generics_0.1.4 Biostrings_2.72.1
[151] mvtnorm_1.3-3 parallelly_1.45.0 mnormt_2.1.1
[154] statmod_1.5.0 RcppHNSW_0.6.0 ScaledMatrix_1.12.0
[157] carData_3.0-5 minqa_1.2.8 pbapply_1.7-2
[160] httr2_1.1.2 spam_2.11-1 gson_0.1.0
[163] dqrng_0.4.1 graphlayouts_1.2.2 gtools_3.9.5
[166] softImpute_1.4-3 ggsignif_0.6.4 RcppEigen_0.3.4.0.2
[169] shiny_1.10.0 GenomeInfoDbData_1.2.12 glmmTMB_1.1.11
[172] rhdf5filters_1.16.0 R.utils_2.13.0 memoise_2.0.1
[175] rmarkdown_2.29 locfdr_1.1-8 scales_1.4.0
[178] R.methodsS3_1.8.2 phylobase_0.8.12 future_1.58.0
[181] RANN_2.6.2 Cairo_1.6-2 spatstat.data_3.1-6
[184] rstudioapi_0.17.1 cluster_2.1.8.1 whisker_0.4.1
[187] mutoss_0.1-13 spatstat.utils_3.1-4 hms_1.1.3
[190] fitdistrplus_1.2-2 cowplot_1.1.3 colorspace_2.1-1
[193] rlang_1.1.6 DelayedMatrixStats_1.26.0 sparseMatrixStats_1.16.0
[196] xts_0.14.1 dotCall64_1.2 shinydashboard_0.7.3
[199] ggforce_0.4.2 laeken_0.5.3 mgcv_1.9-3
[202] xfun_0.52 e1071_1.7-16 TH.data_1.1-3
[205] iterators_1.0.14 abind_1.4-8 GOSemSim_2.30.2
[208] treeio_1.28.0 Rhdf5lib_1.26.0 futile.options_1.0.1
[211] bitops_1.0-9 Rdpack_2.6.4 promises_1.3.3
[214] scatterpie_0.2.4 RSQLite_2.4.0 qvalue_2.36.0
[217] sandwich_3.1-1 fgsea_1.30.0 DelayedArray_0.30.1
[220] proxy_0.4-27 GO.db_3.19.1 compiler_4.4.0
[223] prettyunits_1.2.0 boot_1.3-31 beachmat_2.20.0
[226] listenv_0.9.1 Rcpp_1.0.14 edgeR_4.2.2
[229] workflowr_1.7.1 BiocSingular_1.20.0 tensor_1.5
[232] MASS_7.3-65 progress_1.2.3 uuid_1.2-1
[235] BiocParallel_1.38.0 babelgene_22.9 spatstat.random_3.4-1
[238] R6_2.6.1 fastmap_1.2.0 multcomp_1.4-28
[241] fastmatch_1.1-6 rstatix_0.7.2 vipor_0.4.7
[244] TTR_0.24.4 ROCR_1.0-11 TFisher_0.2.0
[247] rsvd_1.0.5 vcd_1.4-13 nnet_7.3-20
[250] gtable_0.3.6 KernSmooth_2.23-26 miniUI_0.1.2
[253] deldir_2.0-4 htmltools_0.5.8.1 ggthemes_5.1.0
[256] bit64_4.6.0-1 spatstat.explore_3.4-3 lifecycle_1.0.4
[259] blme_1.0-6 nloptr_2.2.1 sass_0.4.10
[262] vctrs_0.6.5 robustbase_0.99-4-1 spatstat.geom_3.4-1
[265] NMF_0.28 sn_2.1.1 ggfun_0.1.8
[268] future.apply_1.11.3 bslib_0.9.0 pillar_1.10.2
[271] gplots_3.2.0 pcaMethods_1.96.0 metapod_1.12.0
[274] 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 clusterExperiment_2.24.0 scran_1.32.0
[4] tradeSeq_1.18.0 RColorBrewer_1.1-3 slingshot_2.12.0
[7] TrajectoryUtils_1.12.0 princurve_2.1.6 NCmisc_1.2.0
[10] VennDiagram_1.7.3 futile.logger_1.4.3 ggupset_0.4.1
[13] gridExtra_2.3 DOSE_3.30.5 enrichplot_1.24.4
[16] msigdbr_24.1.0 org.Hs.eg.db_3.19.1 AnnotationDbi_1.66.0
[19] clusterProfiler_4.12.6 multtest_2.60.0 metap_1.12
[22] scater_1.32.1 scuttle_1.14.0 destiny_3.18.0
[25] circlize_0.4.16 muscat_1.18.0 viridis_0.6.5
[28] viridisLite_0.4.2 lubridate_1.9.4 forcats_1.0.0
[31] stringr_1.5.1 purrr_1.0.4 readr_2.1.5
[34] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
[37] dplyr_1.1.4 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[40] Biobase_2.64.0 GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
[43] IRanges_2.38.1 S4Vectors_0.42.1 BiocGenerics_0.50.0
[46] MatrixGenerics_1.16.0 matrixStats_1.5.0 pheatmap_1.0.13
[49] ggpubr_0.6.0 ggplot2_3.5.2 Seurat_5.3.0
[52] SeuratObject_5.1.0 sp_2.2-0 runSeurat3_0.1.0
[55] 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] rngtools_1.5.2 S4Arrays_1.4.1 RNeXML_2.4.11
[16] pbkrtest_0.5.4 parallel_4.4.0 png_0.1-8
[19] plotrix_3.8-4 registry_0.5-1 cli_3.6.5
[22] ggplotify_0.1.2 goftest_1.2-3 VIM_6.2.2
[25] variancePartition_1.34.0 kernlab_0.9-33 bluster_1.14.0
[28] BiocNeighbors_1.22.0 shadowtext_0.1.4 uwot_0.2.3
[31] curl_6.2.3 zinbwave_1.26.0 tidytree_0.4.6
[34] mime_0.13 evaluate_1.0.3 ComplexHeatmap_2.20.0
[37] stringi_1.8.7 backports_1.5.0 XML_3.99-0.18
[40] lmerTest_3.1-3 qqconf_1.3.2 httpuv_1.6.16
[43] magrittr_2.0.3 rappdirs_0.3.3 splines_4.4.0
[46] ggraph_2.2.1 sctransform_0.4.2 ggbeeswarm_0.7.2
[49] HDF5Array_1.32.1 DBI_1.2.3 genefilter_1.86.0
[52] jquerylib_0.1.4 smoother_1.3 withr_3.0.2
[55] git2r_0.36.2 corpcor_1.6.10 reformulas_0.4.1
[58] class_7.3-23 rprojroot_2.0.4 lmtest_0.9-40
[61] tidygraph_1.3.1 BiocManager_1.30.25 formatR_1.14
[64] colourpicker_1.3.0 htmlwidgets_1.6.4 fs_1.6.6
[67] ggrepel_0.9.6 labeling_0.4.3 fANCOVA_0.6-1
[70] SparseArray_1.4.8 DESeq2_1.44.0 ranger_0.17.0
[73] DEoptimR_1.1-3-1 rncl_0.8.7 annotate_1.82.0
[76] reticulate_1.42.0 hexbin_1.28.5 zoo_1.8-14
[79] XVector_0.44.0 knitr_1.50 ggplot.multistats_1.0.1
[82] UCSC.utils_1.0.0 RhpcBLASctl_0.23-42 timechange_0.3.0
[85] foreach_1.5.2 patchwork_1.3.0 caTools_1.18.3
[88] rhdf5_2.48.0 ggtree_3.12.0 data.table_1.17.4
[91] R.oo_1.27.1 RSpectra_0.16-2 irlba_2.3.5.1
[94] gridGraphics_0.5-1 fastDummies_1.7.5 ade4_1.7-23
[97] lazyeval_0.2.2 yaml_2.3.10 survival_3.8-3
[100] scattermore_1.2 crayon_1.5.3 RcppAnnoy_0.0.22
[103] progressr_0.15.1 tweenr_2.0.3 later_1.4.2
[106] ggridges_0.5.6 codetools_0.2-20 GlobalOptions_0.1.2
[109] aod_1.3.3 KEGGREST_1.44.1 Rtsne_0.17
[112] shape_1.4.6.1 limma_3.60.6 pkgconfig_2.0.3
[115] xml2_1.3.8 TMB_1.9.17 spatstat.univar_3.1-3
[118] mathjaxr_1.8-0 EnvStats_3.1.0 aplot_0.2.5
[121] scatterplot3d_0.3-44 gridBase_0.4-7 ape_5.8-1
[124] spatstat.sparse_3.1-0 xtable_1.8-4 car_3.1-3
[127] plyr_1.8.9 httr_1.4.7 rbibutils_2.3
[130] tools_4.4.0 globals_0.18.0 beeswarm_0.4.0
[133] broom_1.0.8 nlme_3.1-168 lambda.r_1.2.4
[136] assertthat_0.2.1 lme4_1.1-37 digest_0.6.37
[139] numDeriv_2016.8-1.1 Matrix_1.7-3 farver_2.1.2
[142] tzdb_0.5.0 remaCor_0.0.18 reshape2_1.4.4
[145] yulab.utils_0.2.0 glue_1.8.0 cachem_1.1.0
[148] polyclip_1.10-7 generics_0.1.4 Biostrings_2.72.1
[151] mvtnorm_1.3-3 parallelly_1.45.0 mnormt_2.1.1
[154] statmod_1.5.0 RcppHNSW_0.6.0 ScaledMatrix_1.12.0
[157] carData_3.0-5 minqa_1.2.8 pbapply_1.7-2
[160] httr2_1.1.2 spam_2.11-1 gson_0.1.0
[163] dqrng_0.4.1 graphlayouts_1.2.2 gtools_3.9.5
[166] softImpute_1.4-3 ggsignif_0.6.4 RcppEigen_0.3.4.0.2
[169] shiny_1.10.0 GenomeInfoDbData_1.2.12 glmmTMB_1.1.11
[172] rhdf5filters_1.16.0 R.utils_2.13.0 memoise_2.0.1
[175] rmarkdown_2.29 locfdr_1.1-8 scales_1.4.0
[178] R.methodsS3_1.8.2 phylobase_0.8.12 future_1.58.0
[181] RANN_2.6.2 Cairo_1.6-2 spatstat.data_3.1-6
[184] rstudioapi_0.17.1 cluster_2.1.8.1 whisker_0.4.1
[187] mutoss_0.1-13 spatstat.utils_3.1-4 hms_1.1.3
[190] fitdistrplus_1.2-2 cowplot_1.1.3 colorspace_2.1-1
[193] rlang_1.1.6 DelayedMatrixStats_1.26.0 sparseMatrixStats_1.16.0
[196] xts_0.14.1 dotCall64_1.2 shinydashboard_0.7.3
[199] ggforce_0.4.2 laeken_0.5.3 mgcv_1.9-3
[202] xfun_0.52 e1071_1.7-16 TH.data_1.1-3
[205] iterators_1.0.14 abind_1.4-8 GOSemSim_2.30.2
[208] treeio_1.28.0 Rhdf5lib_1.26.0 futile.options_1.0.1
[211] bitops_1.0-9 Rdpack_2.6.4 promises_1.3.3
[214] scatterpie_0.2.4 RSQLite_2.4.0 qvalue_2.36.0
[217] sandwich_3.1-1 fgsea_1.30.0 DelayedArray_0.30.1
[220] proxy_0.4-27 GO.db_3.19.1 compiler_4.4.0
[223] prettyunits_1.2.0 boot_1.3-31 beachmat_2.20.0
[226] listenv_0.9.1 Rcpp_1.0.14 edgeR_4.2.2
[229] workflowr_1.7.1 BiocSingular_1.20.0 tensor_1.5
[232] MASS_7.3-65 progress_1.2.3 uuid_1.2-1
[235] BiocParallel_1.38.0 babelgene_22.9 spatstat.random_3.4-1
[238] R6_2.6.1 fastmap_1.2.0 multcomp_1.4-28
[241] fastmatch_1.1-6 rstatix_0.7.2 vipor_0.4.7
[244] TTR_0.24.4 ROCR_1.0-11 TFisher_0.2.0
[247] rsvd_1.0.5 vcd_1.4-13 nnet_7.3-20
[250] gtable_0.3.6 KernSmooth_2.23-26 miniUI_0.1.2
[253] deldir_2.0-4 htmltools_0.5.8.1 ggthemes_5.1.0
[256] bit64_4.6.0-1 spatstat.explore_3.4-3 lifecycle_1.0.4
[259] blme_1.0-6 nloptr_2.2.1 sass_0.4.10
[262] vctrs_0.6.5 robustbase_0.99-4-1 spatstat.geom_3.4-1
[265] NMF_0.28 sn_2.1.1 ggfun_0.1.8
[268] future.apply_1.11.3 bslib_0.9.0 pillar_1.10.2
[271] gplots_3.2.0 pcaMethods_1.96.0 metapod_1.12.0
[274] locfit_1.5-9.12 jsonlite_2.0.0 GetoptLong_1.0.5