suppressPackageStartupMessages({
library(tidyverse)
library(Seurat)
library(magrittr)
library(dplyr)
library(purrr)
library(ggplot2)
library(here)
library(runSeurat3)
})Merge all samples
Load packages
set dir
basedir <- here()
metaDat <- read_tsv(paste0(basedir, "/data/metadata.txt"), col_names = T)load and assign samples
assignSamples <- function(smpNam, basedirSmp, smpCond, smpAge, smpLoc){
smpNamFull <- list.files(path = paste0(basedirSmp, "/data/samples/"),
pattern = paste0(smpNam, ".*_seurat.rds"))
seuratSmp <- readRDS(paste0(basedirSmp, "/data/samples/", smpNamFull))
seuratSmp$cond <- smpCond
seuratSmp$age <- smpAge
seuratSmp$location <- smpLoc
return(seuratSmp)
}
####################################################################
for(i in 1:length(metaDat$dataset)){
seuratX <- assignSamples(smpNam = metaDat$dataset[i],
basedirSmp = basedir,
smpCond = metaDat$cond[i],
smpAge = metaDat$age[i],
smpLoc = metaDat$location[i])
if(exists("seurat")){
seurat <- merge(x = seurat, y = seuratX, project = "LNdev")
}else{
seurat <- seuratX
}
}
remove(seuratX)filter cells
dim(seurat)[1] 31899 115092seurat <- subset(seurat, subset = ENSMUSG00000026395.Ptprc == 0)
seurat <- subset(seurat, subset = ENSMUSG00000003379.Cd79a == 0)
seurat <- subset(seurat, subset = ENSMUSG00000032093.Cd3e == 0)
seurat <- subset(seurat, subset = ENSMUSG00000031391.L1cam == 0)
dim(seurat)[1] 31899 108387## reprocess
res <- c(0.8,0.6,0.25,0.4)
seurat <- NormalizeData(object = seurat)
seurat <- FindVariableFeatures(object = seurat)
seurat <- ScaleData(object = seurat, verbose = FALSE)
seurat <- RunPCA(object = seurat, npcs = 30, verbose = FALSE)
seurat <- RunTSNE(object = seurat, reduction = "pca", dims = 1:20)
seurat <- RunUMAP(object = seurat, reduction = "pca", dims = 1:20)
seurat <- FindNeighbors(object = seurat, reduction = "pca",
dims = 1:20)
for (i in 1:length(res)) {
seurat <- FindClusters(object = seurat, resolution = res[i],
random.seed = 1234)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 108387
Number of edges: 3407294
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9161
Number of communities: 27
Elapsed time: 43 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 108387
Number of edges: 3407294
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9275
Number of communities: 23
Elapsed time: 40 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 108387
Number of edges: 3407294
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9535
Number of communities: 16
Elapsed time: 41 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 108387
Number of edges: 3407294
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9416
Number of communities: 20
Elapsed time: 38 secondsseuratSub <- subset(seurat, Rosa26eyfp.Rosa26eyfp>0)
eyfpPos <- colnames(seuratSub)
seurat$EYFP <- "neg"
seurat$EYFP[which(colnames(seurat)%in%eyfpPos)] <- "pos"
(table(seurat$dataset, seurat$EYFP))
neg pos
1_20210401_Mu_Ccl19tTA_EYFP_E18_iLN_fibroblasts_V3 4459 60
1_20210721_Mu_Ccl19tTA_EYFP_E18_iLN_fibroblasts_V3 10002 121
10_20210120_Ccl19tTA_EYFP_P7_iLN_fibroblasts_V3 1571 716
11_20210120_Ccl19tTA_EYFP_P7_mLN_fibroblasts_V3 5787 762
12_20210121_Ccl19tTA_EYFP_adult_iLN_fibroblasts_V3 825 1165
12_20210628_Mu_Ccl19tTA_EYFP_P7_mLN_EYFPpos_V3 123 1002
13_20210121_Ccl19tTA_EYFP_adult_mLN_fibroblasts_V3 3118 665
13_20210628_Mu_Ccl19tTA_EYFP_P7_iLN_EYFPpos_and_neg_V3 30 14
2_20201027_Ccl19tTA_EYFP_LTbR_8w_iLN_fibroblasts_V3 474 530
2_20210216_Mu_Ccl19tTA_EYFP_Fatemap_E17to7wk_iLN_fibroblasts_V3 340 72
2_20210401_Mu_Ccl19tTA_EYFP_E18_mLN_fibroblasts_V3 4456 759
2_20210721_Mu_Ccl19tTA_EYFP_E18_mLN_fibroblasts_V3 5490 1751
3_20201027_Ccl19tTA_EYFP_LTbR_8w_mLN_fibroblasts_V3 1977 1071
3_20210216_Mu_Ccl19tTA_EYFP_Fatemap_E17to7wk_mLN_fibroblasts_V3 2882 837
3_20210722_Mu_Ccl19tTA_LTbR_Fatemap_E18to7wk_iLN_fibroblasts_V3 1936 106
3_20211108_Mu_Ccl19tTA_EYFP_E18_iLN_fib_V3 128 6
316781_05-5_20230427_Mu_Ccl19-EYFP_iLN_stroma 843 1981
316781_06-6_20230427_Mu_Ccl19-EYFP_periLN_stroma 2487 3585
4_20210216_Mu_Ccl19tTA_LTbR_Fatemap_E17to7wk_mLN_fibroblasts_V3 3330 141
4_20210722_Mu_Ccl19tTA_LTbR_Fatemap_E18to7wk_mLN_fibroblasts_V3 4227 575
6_20210518_Mu_Ccl19-tTA_EYFP_LTbR_8w_mLN_fibroblasts_V3 3875 2579
o27244_1_05-5_20211214_Mu_Ccl19tTA_EYFP_3wk_iLN_fib_V3 565 4398
o27244_1_06-6_20211214_Mu_Ccl19tTA_EYFP_3wk_mLN_fib_V3 1087 6241
o27936_1_1-1_20220215_Mu_Ccl19iEYFP_FM18_Ad_mLN_GEM 3036 1482
o27936_1_2-2_20220215_Mu_Ccl19iEYFP_FM18_Ad_iLN_GEM 596 125
o28429_1_03-3_20220405_Mu_Ccl19iEYFP_8wk_mLN_V3 1009 3378
o28429_1_04-4_20220405_Mu_Ccl19iEYFP_8wk_iLN_V3 772 928
o298321_14-14_20221025_Mu_Ccl19iEYFP_E18_pLN_GEM 6613 58
o304531_11-11_20221221_Mu_Ccl19iEYFP_Adult_iLN_EYFPposneg_GEM 640 601save seurat
saveRDS(seurat, file=paste0(basedir, "/data/AllSamplesMerged_seurat.rds"))EYFP only
seurat <- subset(seurat, EYFP == "pos")
## reprocess
res <- c(0.8,0.6,0.25,0.4)
seurat <- NormalizeData(object = seurat)
seurat <- FindVariableFeatures(object = seurat)
seurat <- ScaleData(object = seurat, verbose = FALSE)
seurat <- RunPCA(object = seurat, npcs = 30, verbose = FALSE)
seurat <- RunTSNE(object = seurat, reduction = "pca", dims = 1:20)
seurat <- RunUMAP(object = seurat, reduction = "pca", dims = 1:20)
seurat <- FindNeighbors(object = seurat, reduction = "pca",
dims = 1:20)
for (i in 1:length(res)) {
seurat <- FindClusters(object = seurat, resolution = res[i],
random.seed = 1234)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 35709
Number of edges: 1157449
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8891
Number of communities: 21
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 35709
Number of edges: 1157449
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9043
Number of communities: 17
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 35709
Number of edges: 1157449
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9400
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: 35709
Number of edges: 1157449
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9232
Number of communities: 14
Elapsed time: 7 secondssave eyfp only
saveRDS(seurat, file = paste0(basedir, "/data/EYFPonly_seurat.rds"))Session info
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/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/Berlin
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] runSeurat3_0.1.0 here_1.0.1 magrittr_2.0.3 Seurat_5.0.2
[5] SeuratObject_5.0.1 sp_2.1-3 lubridate_1.9.3 forcats_1.0.0
[9] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[13] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.0 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0
[3] jsonlite_1.8.8 spatstat.utils_3.0-4
[5] rmarkdown_2.26 zlibbioc_1.46.0
[7] vctrs_0.6.5 ROCR_1.0-11
[9] spatstat.explore_3.2-6 RCurl_1.98-1.14
[11] S4Arrays_1.0.6 htmltools_0.5.7
[13] sctransform_0.4.1 parallelly_1.37.1
[15] KernSmooth_2.23-22 htmlwidgets_1.6.4
[17] ica_1.0-3 plyr_1.8.9
[19] plotly_4.10.4 zoo_1.8-12
[21] igraph_2.0.2 mime_0.12
[23] lifecycle_1.0.4 pkgconfig_2.0.3
[25] Matrix_1.6-5 R6_2.5.1
[27] fastmap_1.1.1 GenomeInfoDbData_1.2.10
[29] MatrixGenerics_1.12.3 fitdistrplus_1.1-11
[31] future_1.33.1 shiny_1.8.0
[33] digest_0.6.34 colorspace_2.1-0
[35] S4Vectors_0.40.1 patchwork_1.2.0
[37] rprojroot_2.0.4 tensor_1.5
[39] RSpectra_0.16-1 irlba_2.3.5.1
[41] GenomicRanges_1.52.1 progressr_0.14.0
[43] fansi_1.0.6 spatstat.sparse_3.0-3
[45] timechange_0.3.0 httr_1.4.7
[47] polyclip_1.10-6 abind_1.4-5
[49] compiler_4.3.0 bit64_4.0.5
[51] withr_3.0.0 fastDummies_1.7.3
[53] MASS_7.3-60.0.1 DelayedArray_0.26.7
[55] tools_4.3.0 lmtest_0.9-40
[57] httpuv_1.6.14 future.apply_1.11.1
[59] goftest_1.2-3 glue_1.7.0
[61] nlme_3.1-164 promises_1.2.1
[63] grid_4.3.0 Rtsne_0.17
[65] cluster_2.1.6 reshape2_1.4.4
[67] generics_0.1.3 gtable_0.3.4
[69] spatstat.data_3.0-4 tzdb_0.4.0
[71] data.table_1.15.2 hms_1.1.3
[73] XVector_0.40.0 utf8_1.2.4
[75] BiocGenerics_0.48.0 spatstat.geom_3.2-9
[77] RcppAnnoy_0.0.22 ggrepel_0.9.5
[79] RANN_2.6.1 pillar_1.9.0
[81] vroom_1.6.5 spam_2.10-0
[83] RcppHNSW_0.6.0 later_1.3.2
[85] splines_4.3.0 lattice_0.22-5
[87] bit_4.0.5 survival_3.5-8
[89] deldir_2.0-4 tidyselect_1.2.0
[91] SingleCellExperiment_1.22.0 miniUI_0.1.1.1
[93] pbapply_1.7-2 knitr_1.45
[95] gridExtra_2.3 IRanges_2.36.0
[97] SummarizedExperiment_1.30.2 scattermore_1.2
[99] stats4_4.3.0 xfun_0.42
[101] Biobase_2.60.0 matrixStats_1.2.0
[103] pheatmap_1.0.12 stringi_1.8.3
[105] lazyeval_0.2.2 yaml_2.3.8
[107] evaluate_0.23 codetools_0.2-19
[109] cli_3.6.2 uwot_0.1.16
[111] xtable_1.8-4 reticulate_1.35.0
[113] munsell_0.5.0 GenomeInfoDb_1.36.4
[115] Rcpp_1.0.12 globals_0.16.2
[117] spatstat.random_3.2-3 png_0.1-8
[119] parallel_4.3.0 ellipsis_0.3.2
[121] dotCall64_1.1-1 bitops_1.0-7
[123] listenv_0.9.1 viridisLite_0.4.2
[125] scales_1.3.0 ggridges_0.5.6
[127] crayon_1.5.2 leiden_0.4.3.1
[129] rlang_1.1.3 cowplot_1.1.3 date()[1] "Wed Apr 3 11:47:31 2024"