suppressPackageStartupMessages({
library(tidyverse)
library(Seurat)
library(magrittr)
library(dplyr)
library(purrr)
library(ggplot2)
library(here)
library(runSeurat3)
library(ggsci)
})Integrate human LN immune cells
load packages
set dir
basedir <- here()
seurat <- readRDS(file=paste0(basedir,
"/data/AllPatWithoutCM_IMMMerged_seurat.rds"))visualize data
clustering
colPal <- c(pal_nejm()(8),pal_futurama()(12))[1:length(levels(seurat))]
names(colPal) <- levels(seurat)
## visualize input data
DimPlot(seurat, reduction = "umap", cols=colPal, raster = F)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
patient
colPat <- c(pal_jco()(10),pal_futurama()(12))[1:length(unique(seurat$patient))]
names(colPat) <- unique(seurat$patient)
## visualize input data
DimPlot(seurat, reduction = "umap", cols=colPat, group.by = "patient",
raster = F)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
condition
colCond <- pal_igv()(length(unique(seurat$cond)))
names(colCond) <- unique(seurat$cond)
## visualize input data
DimPlot(seurat, reduction = "umap", cols=colCond, group.by = "cond", raster = F)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
origin
colOrig <- pal_aaas()(length(unique(seurat$origin)))
names(colCond) <- unique(seurat$cond)
## visualize input data
DimPlot(seurat, reduction = "umap", cols=colOrig, group.by = "origin", raster = F)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
integrate across patient
Idents(seurat) <- seurat$patient
seurat.list <- SplitObject(object = seurat, split.by = "patient")
for (i in 1:length(x = seurat.list)) {
seurat.list[[i]] <- NormalizeData(object = seurat.list[[i]],
verbose = FALSE)
seurat.list[[i]] <- FindVariableFeatures(object = seurat.list[[i]],
selection.method = "vst", nfeatures = 2000, verbose = FALSE)
}
seurat.anchors <- FindIntegrationAnchors(object.list = seurat.list, dims = 1:15)
seurat.int <- IntegrateData(anchorset = seurat.anchors, dims = 1:15)
DefaultAssay(object = seurat.int) <- "integrated"
# rerun seurat
seurat.int <- ScaleData(object = seurat.int, verbose = FALSE,
features = rownames(seurat.int))
seurat.int <- RunPCA(object = seurat.int, npcs = 20, verbose = FALSE)
seurat.int <- RunTSNE(object = seurat.int, reduction = "pca", dims = 1:20)
seurat.int <- RunUMAP(object = seurat.int, reduction = "pca", dims = 1:20)
seurat.int <- FindNeighbors(object = seurat.int, reduction = "pca", dims = 1:20)
res <- c(0.6,0.8,0.4,0.25)
for(i in 1:length(res)){
seurat.int <- FindClusters(object = seurat.int, resolution = res[i],
random.seed = 1234)
}
DefaultAssay(object = seurat.int) <- "RNA"
seurat.int$intCluster <- seurat.int$integrated_snn_res.0.25
Idents(seurat.int) <- seurat.int$intClustervisualize int data
clustering
seurat.int <- readRDS(file=paste0(basedir,
"/data/AllPatWithoutCM_IMMMerged_integrated_seurat.rds"))
Idents(seurat.int) <- seurat.int$intCluster
colPal <- c(pal_nejm()(8),pal_futurama()(12))[1:length(levels(seurat.int))]
names(colPal) <- levels(seurat.int)
## visualize input data
DimPlot(seurat.int, reduction = "umap", cols=colPal, raster = F)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
patient
colPat <- c(pal_jco()(10),
pal_futurama()(12))[1:length(unique(seurat.int$patient))]
names(colPat) <- unique(seurat.int$patient)
## visualize input data
DimPlot(seurat.int, reduction = "umap", cols=colPat, group.by = "patient",
raster = F)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
cond
colTon <- pal_igv()(length(unique(seurat.int$cond)))
names(colTon) <- unique(seurat.int$cond)
## visualize input data
DimPlot(seurat.int, reduction = "umap", cols=colTon, group.by = "cond",
raster = F)+
theme_bw() +
theme(axis.text = element_blank(), axis.ticks = element_blank(),
panel.grid.minor = element_blank()) +
xlab("UMAP1") +
ylab("UMAP2")
save seurat object
saveRDS(seurat.int, file=paste0(basedir,
"/data/AllPatWithoutCM_IMMMerged_integrated_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] ggsci_3.0.1 runSeurat3_0.1.0 here_1.0.1 magrittr_2.0.3 Seurat_5.0.2
[6] SeuratObject_5.0.1 sp_2.1-3 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[11] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[16] ggplot2_3.5.0 tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0 jsonlite_1.8.8
[4] spatstat.utils_3.0-4 farver_2.1.1 rmarkdown_2.26
[7] zlibbioc_1.46.0 vctrs_0.6.5 ROCR_1.0-11
[10] spatstat.explore_3.2-6 RCurl_1.98-1.14 S4Arrays_1.0.6
[13] htmltools_0.5.7 sctransform_0.4.1 parallelly_1.37.1
[16] KernSmooth_2.23-22 htmlwidgets_1.6.4 ica_1.0-3
[19] plyr_1.8.9 plotly_4.10.4 zoo_1.8-12
[22] igraph_2.0.2 mime_0.12 lifecycle_1.0.4
[25] pkgconfig_2.0.3 Matrix_1.6-5 R6_2.5.1
[28] fastmap_1.1.1 GenomeInfoDbData_1.2.10 MatrixGenerics_1.12.3
[31] fitdistrplus_1.1-11 future_1.33.1 shiny_1.8.0
[34] digest_0.6.34 colorspace_2.1-0 S4Vectors_0.40.1
[37] patchwork_1.2.0 rprojroot_2.0.4 tensor_1.5
[40] RSpectra_0.16-1 irlba_2.3.5.1 GenomicRanges_1.52.1
[43] labeling_0.4.3 progressr_0.14.0 fansi_1.0.6
[46] spatstat.sparse_3.0-3 timechange_0.3.0 httr_1.4.7
[49] polyclip_1.10-6 abind_1.4-5 compiler_4.3.0
[52] withr_3.0.0 fastDummies_1.7.3 MASS_7.3-60.0.1
[55] DelayedArray_0.26.7 tools_4.3.0 lmtest_0.9-40
[58] httpuv_1.6.14 future.apply_1.11.1 goftest_1.2-3
[61] glue_1.7.0 nlme_3.1-164 promises_1.2.1
[64] grid_4.3.0 Rtsne_0.17 cluster_2.1.6
[67] reshape2_1.4.4 generics_0.1.3 gtable_0.3.4
[70] spatstat.data_3.0-4 tzdb_0.4.0 data.table_1.15.2
[73] hms_1.1.3 XVector_0.40.0 utf8_1.2.4
[76] BiocGenerics_0.48.0 spatstat.geom_3.2-9 RcppAnnoy_0.0.22
[79] ggrepel_0.9.5 RANN_2.6.1 pillar_1.9.0
[82] spam_2.10-0 RcppHNSW_0.6.0 later_1.3.2
[85] splines_4.3.0 lattice_0.22-5 survival_3.5-8
[88] deldir_2.0-4 tidyselect_1.2.0 SingleCellExperiment_1.22.0
[91] miniUI_0.1.1.1 pbapply_1.7-2 knitr_1.45
[94] gridExtra_2.3 IRanges_2.36.0 SummarizedExperiment_1.30.2
[97] scattermore_1.2 stats4_4.3.0 xfun_0.42
[100] Biobase_2.60.0 matrixStats_1.2.0 pheatmap_1.0.12
[103] stringi_1.8.3 lazyeval_0.2.2 yaml_2.3.8
[106] evaluate_0.23 codetools_0.2-19 cli_3.6.2
[109] uwot_0.1.16 xtable_1.8-4 reticulate_1.35.0
[112] munsell_0.5.0 GenomeInfoDb_1.36.4 Rcpp_1.0.12
[115] globals_0.16.2 spatstat.random_3.2-3 png_0.1-8
[118] parallel_4.3.0 ellipsis_0.3.2 dotCall64_1.1-1
[121] bitops_1.0-7 listenv_0.9.1 viridisLite_0.4.2
[124] scales_1.3.0 ggridges_0.5.6 crayon_1.5.2
[127] leiden_0.4.3.1 rlang_1.1.3 cowplot_1.1.3 date()[1] "Wed Mar 13 22:09:31 2024"