suppressPackageStartupMessages({
library(tidyverse)
library(Seurat)
library(magrittr)
library(dplyr)
library(purrr)
library(ggplot2)
library(here)
library(runSeurat3)
library(SingleCellExperiment)
library(RColorBrewer)
library(pheatmap)
library(scater)
library(scran)
library(ggsci)
})chracterize iLN all timepoints WT plus Ltbr floxed
load packages
heatmap function
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)
}set dir and load sample
basedir <- here()
seurat <- readRDS(paste0(basedir,
"/data/AllSamplesMerged_seurat.rds"))
seurat <- subset(seurat, location=="iLN")
table(seurat$EYFP, seurat$age)
3w 8w E17to7wk E18 P7
neg 565 6041 2872 21202 1601
pos 4398 8790 303 245 730table(seurat$cond)
LTbR WT
3046 43701 ## rerunSeurat
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)
res <- c(0.8,0.6,0.25,0.4)
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: 46747
Number of edges: 1494535
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8911
Number of communities: 24
Elapsed time: 13 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 46747
Number of edges: 1494535
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9057
Number of communities: 19
Elapsed time: 12 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 46747
Number of edges: 1494535
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9411
Number of communities: 13
Elapsed time: 13 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 46747
Number of edges: 1494535
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9227
Number of communities: 14
Elapsed time: 12 secondscolCond <- c("#446a7f", "#cb7457")
names(colCond) <- c("LTbR", "WT")
colAge <- c("#440154FF", "#3B528BFF", "#21908CFF", "#5DC863FF", "#FDE725FF")
names(colAge) <- c("E18" , "P7", "3w", "8w","E17to7wk")
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(seurat$RNA_snn_res.0.4))]
names(colPal) <- unique(seurat$RNA_snn_res.0.4)
colDat <- colDat <- c(pal_npg()(10),pal_futurama()(12), pal_aaas()(10),
pal_jama()(8))[1:length(unique(seurat$dataset))]
names(colDat) <- unique(seurat$dataset)DimPlot all
clustering
DimPlot(seurat, reduction = "umap", group.by = "RNA_snn_res.0.4",
cols = colPal)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
vis age
DimPlot(seurat, reduction = "umap", group.by = "age",
cols = colAge)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "age", pt.size=0.5,
cols = colAge)+
theme_void()
vis cond
DimPlot(seurat, reduction = "umap", group.by = "cond",
cols = colCond, shuffle=T)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "cond", pt.size=0.5,
cols = colCond, shuffle=T)+
theme_void()
vis age
DimPlot(seurat, reduction = "umap", group.by = "age", split.by = "EYFP",
cols = colAge)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "age", pt.size=0.5,
cols = colAge, split.by = "EYFP",)+
theme_void()
save seurat
saveRDS(seurat, file = paste0(basedir,
"/data/WT_allTime_iLNonly_WtplusLtbr",
"_seurat.rds"))subset eyfp+ only
table(seurat$EYFP, seurat$age)
3w 8w E17to7wk E18 P7
neg 565 6041 2872 21202 1601
pos 4398 8790 303 245 730seurat <- subset(seurat, EYFP=="pos")
table(seurat$cond, seurat$age)
3w 8w E17to7wk E18 P7
LTbR 0 530 106 0 0
WT 4398 8260 197 245 730table(seurat$age)
3w 8w E17to7wk E18 P7
4398 8790 303 245 730 ## rerunSeurat
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)
res <- c(0.8,0.6,0.25,0.4)
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: 14466
Number of edges: 462776
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8248
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: 14466
Number of edges: 462776
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8431
Number of communities: 15
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14466
Number of edges: 462776
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8959
Number of communities: 9
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14466
Number of edges: 462776
Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8699
Number of communities: 12
Elapsed time: 1 secondsDimPlot all
clustering
DimPlot(seurat, reduction = "umap", group.by = "RNA_snn_res.0.4",
cols = colPal)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
vis age
DimPlot(seurat, reduction = "umap", group.by = "age",
cols = colAge)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "age", pt.size=0.5,
cols = colAge)+
theme_void()
vis cond
DimPlot(seurat, reduction = "umap", group.by = "cond",
cols = colCond, shuffle=T)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "cond", pt.size=0.5,
cols = colCond, shuffle=T)+
theme_void()
save seurat
saveRDS(seurat, file = paste0(basedir,
"/data/WT_allTime_iLNonly_WtplusLtbr_EYFPonly",
"_seurat.rds"))transfer label adult
seuratLab <- readRDS(paste0(basedir,
"/data/WT_adultOnly_bothLabeled_integrated_",
"_seurat.rds"))
seuratLab <- subset(seuratLab, location=="iLN")
seuratLab <- subset(seuratLab, EYFP=="pos")
table(seuratLab$label)
actMedRC FDC/MRC MedRC MedRC/IFRC Pi16+RC PRC TBRC
156 113 1864 1079 10 505 1516
TRC VSMC
2309 72 labCells <- data.frame(label=seuratLab$label) %>% rownames_to_column(., "cell")
allCell <- data.frame(cell=colnames(seurat)) %>%
left_join(., labCells, by= "cell")
allCell$label[which(is.na(allCell$label))] <- "unassigned"
seurat$label <- allCell$label
colLab <- c("#42a071", "#900C3F","#b66e8d", "#61a4ba", "#424671", "#003C67FF",
"#e3953d", "#714542", "#b6856e", "#a4a4a4")
names(colLab) <- c("FDC/MRC", "TRC", "TBRC", "MedRC/IFRC", "MedRC" , "actMedRC",
"PRC", "Pi16+RC", "VSMC", "unassigned")
DimPlot(seurat, reduction = "umap", group.by = "label",
cols = colLab, shuffle=T)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
DimPlot(seurat, reduction = "umap", group.by = "label", pt.size=0.5,
cols = colLab, shuffle=F)+
theme_void()
save seurat
saveRDS(seurat, file = paste0(basedir,
"/data/WT_allTime_iLNonly_WtplusLtbr_EYFPonly_labelTrans",
"_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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggsci_3.0.1 scran_1.28.2
[3] scater_1.28.0 scuttle_1.10.3
[5] pheatmap_1.0.12 RColorBrewer_1.1-3
[7] SingleCellExperiment_1.22.0 SummarizedExperiment_1.30.2
[9] Biobase_2.60.0 GenomicRanges_1.52.1
[11] GenomeInfoDb_1.36.4 IRanges_2.36.0
[13] S4Vectors_0.40.1 BiocGenerics_0.48.0
[15] MatrixGenerics_1.12.3 matrixStats_1.2.0
[17] runSeurat3_0.1.0 here_1.0.1
[19] magrittr_2.0.3 Seurat_5.0.2
[21] SeuratObject_5.0.1 sp_2.1-3
[23] lubridate_1.9.3 forcats_1.0.0
[25] stringr_1.5.1 dplyr_1.1.4
[27] purrr_1.0.2 readr_2.1.5
[29] tidyr_1.3.1 tibble_3.2.1
[31] ggplot2_3.5.0 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.0
[3] later_1.3.2 bitops_1.0-7
[5] polyclip_1.10-6 fastDummies_1.7.3
[7] lifecycle_1.0.4 edgeR_3.42.4
[9] rprojroot_2.0.4 globals_0.16.2
[11] lattice_0.22-5 MASS_7.3-60.0.1
[13] limma_3.56.2 plotly_4.10.4
[15] rmarkdown_2.26 yaml_2.3.8
[17] metapod_1.8.0 httpuv_1.6.14
[19] sctransform_0.4.1 spam_2.10-0
[21] spatstat.sparse_3.0-3 reticulate_1.35.0
[23] cowplot_1.1.3 pbapply_1.7-2
[25] abind_1.4-5 zlibbioc_1.46.0
[27] Rtsne_0.17 RCurl_1.98-1.14
[29] GenomeInfoDbData_1.2.10 ggrepel_0.9.5
[31] irlba_2.3.5.1 listenv_0.9.1
[33] spatstat.utils_3.0-4 goftest_1.2-3
[35] RSpectra_0.16-1 dqrng_0.3.2
[37] spatstat.random_3.2-3 fitdistrplus_1.1-11
[39] parallelly_1.37.1 DelayedMatrixStats_1.22.6
[41] leiden_0.4.3.1 codetools_0.2-19
[43] DelayedArray_0.26.7 tidyselect_1.2.0
[45] farver_2.1.1 viridis_0.6.5
[47] ScaledMatrix_1.8.1 spatstat.explore_3.2-6
[49] jsonlite_1.8.8 BiocNeighbors_1.18.0
[51] ellipsis_0.3.2 progressr_0.14.0
[53] ggridges_0.5.6 survival_3.5-8
[55] tools_4.3.0 ica_1.0-3
[57] Rcpp_1.0.12 glue_1.7.0
[59] gridExtra_2.3 xfun_0.42
[61] withr_3.0.0 fastmap_1.1.1
[63] bluster_1.10.0 fansi_1.0.6
[65] digest_0.6.34 rsvd_1.0.5
[67] timechange_0.3.0 R6_2.5.1
[69] mime_0.12 colorspace_2.1-0
[71] scattermore_1.2 tensor_1.5
[73] spatstat.data_3.0-4 utf8_1.2.4
[75] generics_0.1.3 data.table_1.15.2
[77] httr_1.4.7 htmlwidgets_1.6.4
[79] S4Arrays_1.0.6 uwot_0.1.16
[81] pkgconfig_2.0.3 gtable_0.3.4
[83] lmtest_0.9-40 XVector_0.40.0
[85] htmltools_0.5.7 dotCall64_1.1-1
[87] scales_1.3.0 png_0.1-8
[89] knitr_1.45 rstudioapi_0.15.0
[91] tzdb_0.4.0 reshape2_1.4.4
[93] nlme_3.1-164 zoo_1.8-12
[95] KernSmooth_2.23-22 vipor_0.4.7
[97] parallel_4.3.0 miniUI_0.1.1.1
[99] pillar_1.9.0 grid_4.3.0
[101] vctrs_0.6.5 RANN_2.6.1
[103] promises_1.2.1 BiocSingular_1.16.0
[105] beachmat_2.16.0 xtable_1.8-4
[107] cluster_2.1.6 beeswarm_0.4.0
[109] evaluate_0.23 locfit_1.5-9.9
[111] cli_3.6.2 compiler_4.3.0
[113] rlang_1.1.3 crayon_1.5.2
[115] future.apply_1.11.1 labeling_0.4.3
[117] plyr_1.8.9 ggbeeswarm_0.7.2
[119] stringi_1.8.3 viridisLite_0.4.2
[121] deldir_2.0-4 BiocParallel_1.34.2
[123] munsell_0.5.0 lazyeval_0.2.2
[125] spatstat.geom_3.2-9 Matrix_1.6-5
[127] RcppHNSW_0.6.0 hms_1.1.3
[129] patchwork_1.2.0 sparseMatrixStats_1.12.2
[131] future_1.33.1 statmod_1.5.0
[133] shiny_1.8.0 ROCR_1.0-11
[135] igraph_2.0.2 date()[1] "Wed Apr 24 10:03:47 2024"