Figure 2
Jovana Maksimovic
February 20, 2025
Last updated: 2025-02-20
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Knit directory: paed-inflammation-CITEseq/
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Load libraries.
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(edgeR)
library(tidyverse)
library(ggplot2)
library(Seurat)
library(glmGamPoi)
library(dittoSeq)
library(here)
library(clustree)
library(patchwork)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(glue)
library(speckle)
library(tidyHeatmap)
library(paletteer)
library(dsb)
library(ggh4x)
library(readxl)
})
source(here("code/utility.R"))Load data
files <- list.files(here("data/C133_Neeland_merged"),
pattern = "C133_Neeland_full_clean.*(macrophages|t_cells|other_cells)_annotated_full.SEU.rds",
full.names = TRUE)
seuLst <- lapply(files[2:4], function(f) readRDS(f))
seuLst[[1]]
An object of class Seurat
41892 features across 13687 samples within 5 assays
Active assay: RNA (19973 features, 0 variable features)
4 other assays present: ADT, SCT, integrated, ADT.dsb
2 dimensional reductions calculated: pca, umap
[[2]]
An object of class Seurat
38775 features across 15511 samples within 5 assays
Active assay: RNA (19973 features, 0 variable features)
4 other assays present: ADT, SCT, integrated, ADT.dsb
2 dimensional reductions calculated: pca, umap
[[3]]
An object of class Seurat
46108 features across 165209 samples within 5 assays
Active assay: RNA (21568 features, 0 variable features)
4 other assays present: ADT, SCT, integrated, ADT.dsb
2 dimensional reductions calculated: pca, umapMacrophage cells figure panels
options(ggrepel.max.overlaps = Inf)
cluster_pal <- "ggsci::category20_d3"
draw_umap_with_labels(seuLst[[3]],
"ann_level_3",
cluster_pal) -> f2a
f2a
# markers <- readRDS(here("data/cluster_annotations/seurat_markers_macrophages.rds"))
#
# draw_marker_gene_dotplot(seuLst[[3]],
# markers,
# "ann_level_3",
# cluster_pal)
labels <- read_excel(here("data",
"cluster_annotations",
"marker_genes_macrophages_figure_2.xlsx"))
#"macrophages_26.06.24.xlsx"))
unnest(enframe(setNames(str_split(labels$`non-overlapping marker genes`, ", "),
labels$`cell label`),
value = "gene",
name = "cluster"),
cols = gene) %>%
arrange(cluster) %>%
distinct() -> markers
markers <- markers[markers$gene %in% rownames(seuLst[[3]]),]
draw_marker_gene_dotplot(seuLst[[3]],
markers,
"ann_level_3",
cluster_pal,
direction = 1,
num = 5) -> f2b
f2b
draw_cell_type_proportions_barplot(seuLst[[3]],
"ann_level_3",
cluster_pal) -> f2c
f2c
T/NK cells figure panels
cluster_pal <- "ggsci::category20b_d3"
draw_umap_with_labels(seuLst[[2]],
"ann_level_3",
cluster_pal,
direction = -1) -> f2d
f2d
# markers <- readRDS(here("data/cluster_annotations/seurat_markers_TNK_cells.rds"))
#
# draw_marker_gene_dotplot(seuLst[[2]],
# markers,
# "ann_level_3",
# cluster_pal,
# direction = -1)
labels <- read_excel(here("data",
"cluster_annotations",
#"T-NK_ambientRNAremoval_21.03.24.xlsx"),
"marker_genes_TNK_figure_2.xlsx"))
#skip = 1)
unnest(enframe(setNames(str_split(labels$`non-overlapping marker genes`, ", "),
labels$`cell label`),
value = "gene",
name = "cluster"),
cols = gene) %>%
arrange(cluster) %>%
distinct() -> markers
markers <- markers[markers$gene %in% rownames(seuLst[[2]]),]
draw_marker_gene_dotplot(seuLst[[2]],
markers,
"ann_level_3",
cluster_pal,
direction = -1,
num = 5) -> f2e
f2e
draw_cell_type_proportions_barplot(seuLst[[2]],
"ann_level_3",
cluster_pal,
direction = -1) -> f2f
f2f
Rare cells figure panels
cluster_pal <- "ggsci::category20c_d3"
draw_umap_with_labels(seuLst[[1]],
"ann_level_3",
cluster_pal) -> f2g
f2g
# markers <- readRDS(here("data/cluster_annotations/seurat_markers_other_cells.rds"))
#
# draw_marker_gene_dotplot(seuLst[[1]],
# markers,
# "ann_level_3",
# cluster_pal)
labels <- read_excel(here("data",
"cluster_annotations",
#"others_ambientRNAremoval_21.03.24.xlsx"),
"marker_genes_other_figure_2.xlsx"))
#skip = 1)
unnest(enframe(setNames(str_split(labels$`non-overlapping marker genes`, ", "),
labels$`cell label`),
value = "gene",
name = "cluster"),
cols = gene) %>%
arrange(cluster) %>%
distinct() -> markers
markers <- markers[markers$gene %in% rownames(seuLst[[1]]),]
draw_marker_gene_dotplot(seuLst[[1]],
markers,
"ann_level_3",
cluster_pal,
direction = 1,
num = 5) -> f2h
f2h
draw_cell_type_proportions_barplot(seuLst[[1]],
"ann_level_3",
cluster_pal) -> f2i
f2i
Figure 2
layout = "
AAABBBBB
AAACCCCC
DDDEEEEE
DDDFFFFF
GGGHHHHH
GGGIIIII
"
(wrap_elements(f2a + theme(plot.margin = unit(rep(0,4), "cm"))) +
wrap_elements(f2b + theme(plot.margin = unit(rep(0,4), "cm"))) +
wrap_elements(f2c + theme(plot.margin = unit(rep(0,4), "cm"))) +
wrap_elements(f2d + theme(plot.margin = unit(rep(0,4), "cm"))) +
wrap_elements(f2e + theme(plot.margin = unit(rep(0,4), "cm"))) +
wrap_elements(f2f + theme(plot.margin = unit(rep(0,4), "cm"))) +
wrap_elements(f2g + theme(plot.margin = unit(rep(0,4), "cm"))) +
wrap_elements(f2h + theme(plot.margin = unit(rep(0,4), "cm"))) +
wrap_elements(f2i) + theme(plot.margin = unit(rep(0,4), "cm"))) +
plot_layout(design = layout) +
plot_annotation(tag_levels = "A") &
theme(plot.tag = element_text(size = 16,
face = "bold",
family = "arial"))
Session info
sessionInfo()R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices datasets utils methods
[8] base
other attached packages:
[1] readxl_1.4.3 ggh4x_0.2.8
[3] dsb_1.0.3 paletteer_1.6.0
[5] tidyHeatmap_1.8.1 speckle_1.2.0
[7] glue_1.8.0 org.Hs.eg.db_3.18.0
[9] AnnotationDbi_1.64.1 patchwork_1.3.0
[11] clustree_0.5.1 ggraph_2.2.0
[13] here_1.0.1 dittoSeq_1.14.2
[15] glmGamPoi_1.14.3 SeuratObject_4.1.4
[17] Seurat_4.4.0 lubridate_1.9.3
[19] forcats_1.0.0 stringr_1.5.1
[21] dplyr_1.1.4 purrr_1.0.2
[23] readr_2.1.5 tidyr_1.3.1
[25] tibble_3.2.1 ggplot2_3.5.0
[27] tidyverse_2.0.0 edgeR_4.0.15
[29] limma_3.58.1 SingleCellExperiment_1.24.0
[31] SummarizedExperiment_1.32.0 Biobase_2.62.0
[33] GenomicRanges_1.54.1 GenomeInfoDb_1.38.6
[35] IRanges_2.36.0 S4Vectors_0.40.2
[37] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
[39] matrixStats_1.2.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.5 spatstat.sparse_3.0-3 bitops_1.0-7
[4] httr_1.4.7 RColorBrewer_1.1-3 doParallel_1.0.17
[7] tools_4.3.3 sctransform_0.4.1 utf8_1.2.4
[10] R6_2.5.1 lazyeval_0.2.2 uwot_0.1.16
[13] GetoptLong_1.0.5 withr_3.0.0 sp_2.1-3
[16] gridExtra_2.3 progressr_0.14.0 cli_3.6.3
[19] spatstat.explore_3.2-6 prismatic_1.1.1 labeling_0.4.3
[22] sass_0.4.9 spatstat.data_3.0-4 ggridges_0.5.6
[25] pbapply_1.7-2 parallelly_1.37.0 rstudioapi_0.15.0
[28] RSQLite_2.3.5 generics_0.1.3 shape_1.4.6
[31] ica_1.0-3 spatstat.random_3.2-2 dendextend_1.17.1
[34] Matrix_1.6-5 fansi_1.0.6 abind_1.4-5
[37] lifecycle_1.0.4 whisker_0.4.1 yaml_2.3.8
[40] SparseArray_1.2.4 Rtsne_0.17 grid_4.3.3
[43] blob_1.2.4 promises_1.2.1 crayon_1.5.2
[46] miniUI_0.1.1.1 lattice_0.22-5 cowplot_1.1.3
[49] KEGGREST_1.42.0 pillar_1.9.0 knitr_1.45
[52] ComplexHeatmap_2.18.0 rjson_0.2.21 future.apply_1.11.1
[55] codetools_0.2-19 leiden_0.4.3.1 getPass_0.2-4
[58] data.table_1.15.0 vctrs_0.6.5 png_0.1-8
[61] cellranger_1.1.0 gtable_0.3.4 rematch2_2.1.2
[64] cachem_1.0.8 xfun_0.42 S4Arrays_1.2.0
[67] mime_0.12 tidygraph_1.3.1 survival_3.7-0
[70] pheatmap_1.0.12 iterators_1.0.14 statmod_1.5.0
[73] ellipsis_0.3.2 fitdistrplus_1.1-11 ROCR_1.0-11
[76] nlme_3.1-164 bit64_4.0.5 RcppAnnoy_0.0.22
[79] rprojroot_2.0.4 bslib_0.6.1 irlba_2.3.5.1
[82] KernSmooth_2.23-24 colorspace_2.1-0 DBI_1.2.1
[85] tidyselect_1.2.1 processx_3.8.3 bit_4.0.5
[88] compiler_4.3.3 git2r_0.33.0 DelayedArray_0.28.0
[91] plotly_4.10.4 scales_1.3.0 lmtest_0.9-40
[94] callr_3.7.3 digest_0.6.34 goftest_1.2-3
[97] spatstat.utils_3.0-4 rmarkdown_2.25 XVector_0.42.0
[100] htmltools_0.5.8.1 pkgconfig_2.0.3 highr_0.10
[103] fastmap_1.1.1 rlang_1.1.4 GlobalOptions_0.1.2
[106] htmlwidgets_1.6.4 shiny_1.8.0 farver_2.1.1
[109] jquerylib_0.1.4 zoo_1.8-12 jsonlite_1.8.8
[112] mclust_6.1 RCurl_1.98-1.14 magrittr_2.0.3
[115] GenomeInfoDbData_1.2.11 munsell_0.5.0 Rcpp_1.0.12
[118] viridis_0.6.5 reticulate_1.35.0 stringi_1.8.3
[121] zlibbioc_1.48.0 MASS_7.3-60.0.1 plyr_1.8.9
[124] parallel_4.3.3 listenv_0.9.1 ggrepel_0.9.5
[127] deldir_2.0-2 Biostrings_2.70.2 graphlayouts_1.1.0
[130] splines_4.3.3 tensor_1.5 hms_1.1.3
[133] circlize_0.4.15 locfit_1.5-9.8 ps_1.7.6
[136] igraph_2.0.1.1 spatstat.geom_3.2-8 reshape2_1.4.4
[139] evaluate_0.23 renv_1.0.3 BiocManager_1.30.22
[142] tzdb_0.4.0 foreach_1.5.2 tweenr_2.0.3
[145] httpuv_1.6.14 RANN_2.6.1 polyclip_1.10-6
[148] future_1.33.1 clue_0.3-65 scattermore_1.2
[151] ggforce_0.4.2 xtable_1.8-4 later_1.3.2
[154] viridisLite_0.4.2 memoise_2.0.1 cluster_2.1.6
[157] timechange_0.3.0 globals_0.16.2