• Load data
  • Macrophage cells figure panels
  • T/NK cells figure panels
  • Rare cells figure panels
  • Session info

Last updated: 2024-11-12

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Knit directory: paed-inflammation-CITEseq/

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    Untracked:  data/Proteins_macs_07.10.24.csv
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    Untracked:  data/cluster_annotations/marker_genes_macrophages_figure_2.xlsx
    Untracked:  data/cluster_annotations/marker_genes_other_figure_2.xlsx
    Untracked:  data/cluster_annotations/marker_proteins_TNK_supp.xlsx
    Untracked:  data/cluster_annotations/marker_proteins_macrophages_supp.xlsx
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Rmd 50ee332 Jovana Maksimovic 2024-09-20 Started work on panel figures

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, umap

Macrophage 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

Version Author Date
30e844f Jovana Maksimovic 2024-10-07 
# 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

Version Author Date
30e844f Jovana Maksimovic 2024-10-07 
draw_cell_type_proportions_barplot(seuLst[[3]],
                                   "ann_level_3",
                                   cluster_pal) -> f2c

f2c

Version Author Date
30e844f Jovana Maksimovic 2024-10-07

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

Version Author Date
30e844f Jovana Maksimovic 2024-10-07 
# 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

Version Author Date
30e844f Jovana Maksimovic 2024-10-07 
draw_cell_type_proportions_barplot(seuLst[[2]],
                                   "ann_level_3",
                                   cluster_pal,
                                   direction = -1) -> f2f

f2f

Version Author Date
30e844f Jovana Maksimovic 2024-10-07

Rare cells figure panels

cluster_pal <- "ggsci::category20c_d3"

draw_umap_with_labels(seuLst[[1]], 
                      "ann_level_3", 
                      cluster_pal) -> f2g

f2g

Version Author Date
30e844f Jovana Maksimovic 2024-10-07 
# 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

Version Author Date
30e844f Jovana Maksimovic 2024-10-07 
draw_cell_type_proportions_barplot(seuLst[[1]],
                                   "ann_level_3",
                                   cluster_pal) -> f2i

f2i

Version Author Date
30e844f Jovana Maksimovic 2024-10-07 
layout = "
AAABBBBB
AAACCCCC
DDDEEEEE
DDDFFFFF
GGGHHHHH
GGGIIIII
"
(wrap_elements(f2a) +
    wrap_elements(f2b) +
    wrap_elements(f2c) +
    wrap_elements(f2d) +
    wrap_elements(f2e) +
    wrap_elements(f2f) +
    wrap_elements(f2g) +
    wrap_elements(f2h) +
    wrap_elements(f2i)) +
  plot_layout(design = layout) +
  plot_annotation(tag_levels = "A") &
  theme(plot.tag = element_text(size = 16,
                                face = "bold",
                                family = "arial"))

Version Author Date
30e844f Jovana Maksimovic 2024-10-07

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_1.7.0                  org.Hs.eg.db_3.18.0        
 [9] AnnotationDbi_1.64.1        patchwork_1.2.0            
[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_1.6.3                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_3.6.2              
 [19] spatstat.explore_3.2-6  prismatic_1.1.1         labeling_0.4.3         
 [22] sass_0.4.8              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_1.2.0        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_0.5.7         pkgconfig_2.0.3         highr_0.10             
[103] fastmap_1.1.1           rlang_1.1.3             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