pacman::p_load(Seurat, tidyverse, qs2, cowplot)
knitr::opts_knit$set(root.dir = "/home/han/Desktop/guoqiang/stroke_bm/stroke_bm_Rproj")
setwd("/home/han/Desktop/guoqiang/stroke_bm/stroke_bm_Rproj")
source("codes/functions.R")Marker Plot
用经典 marker 核对各大类在图上的位置。点大、颜色深表示该基因更常见、表达更高。
res_path <- "results/marker_umap"
if (!dir.exists(res_path)) {
dir.create(res_path, recursive = TRUE)
}alpha <- .03
pt_size <- .03
raster <- FALSE
raster_dpi <- c(3000, 3000)
min_cutoff <- "q10"
max_cutoff <- "q90"1. Marker Plot
每个基因一张图。应对得上教科书位置，例如 CD3D 在 T、MS4A1 在 B、FCGR3B 或 S100A8 在中性粒。
seurat_object <-
qs_read(
"data/rdata/dim_reduction/seurat_object_clustered.qs2",
nthreads = 10
)
DefaultAssay(seurat_object)[1] "RNA"Layers(seurat_object)[1] "counts" "data" "scale.data"HSC
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CD34", "PROM1", "SPINK2", "AVP", "CRHBP", "HLF"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("HSC", ".pdf")),
width = 18, height = 10
)CMP
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("PRSS57", "FLT3", "RNASE2",
"CD34", "IL3RA", "KIT",
"SPI1", "MYB", "GATA1"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("CMP", ".pdf")),
width = 18, height = 15
)MDP
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CSF1R", "KLF4", "SAMHD1", "LGALS1", "LSP1", "FLT3"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("MDP", ".pdf")),
width = 18, height = 10
)MP
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CEBPD", "VCAN", "CD36", "FCGR1A", "CSF1R", "CD14"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("MP", ".pdf")),
width = 18, height = 10
)CLP
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("IL7R", "FLT3", "DNTT", "MME", "SPINK2"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("CLP", ".pdf")),
width = 18, height = 10
)Pro-B
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CD34", "VPREB1", "IGLL1", "EBF1", "PAX5", "DNTT"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("ProB", ".pdf")),
width = 18, height = 10
)Large Pre-B
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("VPREB1", "IGLL1", "CD19", "MKI67", "TOP2A", "PCNA"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Large_PreB", ".pdf")),
width = 18, height = 10
)Small Pre-B
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 2,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("RAG1", "RAG2", "CD19", "CD38"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Small_PreB", ".pdf")),
width = 12, height = 10
)GMP
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("AZU1", "MPO", "ELANE", "CTSG", "PRTN3", "CSF3R"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("GMP", ".pdf")),
width = 18, height = 10
)Promyelocyte
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("DEFA1", "DEFA3", "DEFA4",
"AZU1", "MPO", "ELANE", "CTSG", "PRTN3"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Promyelocyte", ".pdf")),
width = 18, height = 15
)NP
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("LTF", "LCN2", "CAMP", "MNDA", "RETN", "FCGR3B"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("NP", ".pdf")),
width = 18, height = 10
)Neutrophil
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 2,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("NAMPT", "CSF3R", "S100A8", "S100A9"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Neutrophil", ".pdf")),
width = 12, height = 10
)Monocyte
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CD14", "CD68", "VCAN", "LYZ", "FCGR3A"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Monocyte", ".pdf")),
width = 18, height = 10
)Macrophage
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 2,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CD68", "C1QA", "C1QB", "C1QC"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Macrophage", ".pdf")),
width = 12, height = 10
)DC
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CLEC9A", "XCR1", "BATF3", # cDC1
"CD1C", "FCER1A", "CLEC10A", # cDC2
"CCR7", "LAMP3", "CCL19"), # migDC
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("DC", ".pdf")),
width = 18, height = 15
)pDC
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 2,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CLEC4C", "IL3RA", "LILRA4", "GZMB"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("pDC", ".pdf")),
width = 12, height = 10
)T cell
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CD3D", "CD3E", "CD3G", "CD4", "CD8A", "CD8B"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("T_cell", ".pdf")),
width = 18, height = 10
)B cell
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 2,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CD79A", "CD79B", "CD19", "MS4A1"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("B_cell", ".pdf")),
width = 12, height = 10
)Plasma cell
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 2,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("MZB1", "SDC1", "JCHAIN", "IGHG1"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Plasma_cell", ".pdf")),
width = 12, height = 10
)NK cell
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 3,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("KLRD1", "NCAM1", "XCL1", "CCL5", "NKG7", "GNLY"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("NK_cell", ".pdf")),
width = 18, height = 10
)Erythrocyte
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 4,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("CD36", "GYPA", "AHSP", "CA1",
"HBB", "HBA1", "HBA2", "ALAS2",
"SLC4A1", "TFRC", "KLF1", "GATA1"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Erythrocyte", ".pdf")),
width = 24, height = 15
)Megakaryocyte
FeaturePlot(
seurat_object, reduction = "umap_harmony", ncol = 2,
min.cutoff = min_cutoff, max.cutoff = max_cutoff,
features = c("GP9", "PPBP", "PF4", "PECAM1"),
pt.size = pt_size, alpha = alpha, raster = raster, raster.dpi = raster_dpi
)
ggsave(
file.path(res_path, paste0("Megakaryocyte", ".pdf")),
width = 12, height = 10
)sessionInfo()R version 4.4.3 (2025-02-28)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Asia/Shanghai
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.1.3 qs2_0.1.7 lubridate_1.9.3 forcats_1.0.0
[5] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[9] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[13] Seurat_5.2.1 SeuratObject_5.0.2 sp_2.1-4
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 jsonlite_1.8.9 magrittr_2.0.3
[4] spatstat.utils_3.1-1 farver_2.1.2 rmarkdown_2.29
[7] ragg_1.3.3 vctrs_0.6.5 ROCR_1.0-11
[10] spatstat.explore_3.3-3 htmltools_0.5.8.1 sctransform_0.4.1
[13] parallelly_1.39.0 KernSmooth_2.23-26 htmlwidgets_1.6.4
[16] ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[19] zoo_1.8-12 igraph_2.1.2 mime_0.12
[22] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.7-3
[25] R6_2.5.1 fastmap_1.2.0 fitdistrplus_1.2-1
[28] future_1.34.0 shiny_1.9.1 digest_0.6.37
[31] colorspace_2.1-1 patchwork_1.3.0 tensor_1.5
[34] RSpectra_0.16-2 irlba_2.3.5.1 textshaping_0.4.0
[37] labeling_0.4.3 progressr_0.15.1 fansi_1.0.6
[40] spatstat.sparse_3.1-0 timechange_0.3.0 httr_1.4.7
[43] polyclip_1.10-7 abind_1.4-8 compiler_4.4.3
[46] withr_3.0.2 fastDummies_1.7.4 MASS_7.3-65
[49] tools_4.4.3 lmtest_0.9-40 httpuv_1.6.15
[52] future.apply_1.11.3 goftest_1.2-3 glue_1.8.0
[55] nlme_3.1-166 promises_1.3.2 grid_4.4.3
[58] Rtsne_0.17 cluster_2.1.8.1 reshape2_1.4.4
[61] generics_0.1.3 gtable_0.3.6 spatstat.data_3.1-4
[64] tzdb_0.4.0 data.table_1.16.2 hms_1.1.3
[67] stringfish_0.18.0 utf8_1.2.4 spatstat.geom_3.3-4
[70] RcppAnnoy_0.0.22 ggrepel_0.9.6 RANN_2.6.2
[73] pillar_1.9.0 spam_2.11-0 RcppHNSW_0.6.0
[76] later_1.4.1 splines_4.4.3 lattice_0.22-6
[79] survival_3.8-3 deldir_2.0-4 tidyselect_1.2.1
[82] miniUI_0.1.1.1 pbapply_1.7-2 knitr_1.49
[85] gridExtra_2.3 scattermore_1.2 xfun_0.49
[88] matrixStats_1.4.1 stringi_1.8.4 lazyeval_0.2.2
[91] yaml_2.3.10 pacman_0.5.1 evaluate_1.0.1
[94] codetools_0.2-20 cli_3.6.3 uwot_0.2.2
[97] RcppParallel_5.1.9 systemfonts_1.1.0 xtable_1.8-4
[100] reticulate_1.42.0 munsell_0.5.1 Rcpp_1.0.13-1
[103] globals_0.16.3 spatstat.random_3.3-2 png_0.1-8
[106] spatstat.univar_3.1-1 parallel_4.4.3 dotCall64_1.2
[109] listenv_0.9.1 viridisLite_0.4.2 scales_1.3.0
[112] ggridges_0.5.6 rlang_1.1.4